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Repository: schochastics/networkdata
Branch: main
Commit: 521594049f72
Files: 2004
Total size: 1.8 MB
Directory structure:
gitextract_wgt_nhtu/
├── .Rbuildignore
├── .github/
│ ├── .gitignore
│ └── workflows/
│ └── pkgdown.yaml
├── .gitignore
├── DESCRIPTION
├── LICENSE
├── LICENSE.md
├── NAMESPACE
├── NEWS.md
├── R/
│ ├── data-animals.R
│ ├── data-covert.R
│ ├── data-freeman.R
│ ├── data-konnect.R
│ ├── data-misc.R
│ ├── data-movie.R
│ ├── data-shakespeare.R
│ └── networkdata.R
├── README.Rmd
├── README.md
├── _pkgdown.yml
├── data/
│ ├── adjnoun.rda
│ ├── animal_1.rda
│ ├── animal_10.rda
│ ├── animal_11.rda
│ ├── animal_12.rda
│ ├── animal_13.rda
│ ├── animal_14.rda
│ ├── animal_15.rda
│ ├── animal_16.rda
│ ├── animal_17.rda
│ ├── animal_18.rda
│ ├── animal_19.rda
│ ├── animal_2.rda
│ ├── animal_20.rda
│ ├── animal_21.rda
│ ├── animal_22.rda
│ ├── animal_23.rda
│ ├── animal_24.rda
│ ├── animal_25.rda
│ ├── animal_26.rda
│ ├── animal_27.rda
│ ├── animal_28.rda
│ ├── animal_29.rda
│ ├── animal_3.rda
│ ├── animal_30.rda
│ ├── animal_31.rda
│ ├── animal_32.rda
│ ├── animal_33.rda
│ ├── animal_34.rda
│ ├── animal_35.rda
│ ├── animal_36.rda
│ ├── animal_4.rda
│ ├── animal_5.rda
│ ├── animal_6.rda
│ ├── animal_7.rda
│ ├── animal_8.rda
│ ├── animal_9.rda
│ ├── ants_1.rda
│ ├── ants_2.rda
│ ├── arenas_email.rda
│ ├── arenas_meta.rda
│ ├── atp.rda
│ ├── bible.rda
│ ├── bkfrab.rda
│ ├── bkfrac.rda
│ ├── bkoffb.rda
│ ├── bkoffc.rda
│ ├── bktecb.rda
│ ├── bktecc.rda
│ ├── bott.rda
│ ├── brunson_club_membership.rda
│ ├── brunson_corporate_leadership.rda
│ ├── brunson_revolution.rda
│ ├── brunson_south_africa.rda
│ ├── cent_lit.rda
│ ├── ceos_clubs.rda
│ ├── chicagoroad.rda
│ ├── clique_graph.rda
│ ├── coleman.rda
│ ├── core_graph.rda
│ ├── cosponsor.rda
│ ├── covert_1.rda
│ ├── covert_10.rda
│ ├── covert_11.rda
│ ├── covert_12.rda
│ ├── covert_13.rda
│ ├── covert_14.rda
│ ├── covert_15.rda
│ ├── covert_16.rda
│ ├── covert_17.rda
│ ├── covert_18.rda
│ ├── covert_19.rda
│ ├── covert_2.rda
│ ├── covert_20.rda
│ ├── covert_21.rda
│ ├── covert_22.rda
│ ├── covert_23.rda
│ ├── covert_24.rda
│ ├── covert_25.rda
│ ├── covert_26.rda
│ ├── covert_27.rda
│ ├── covert_28.rda
│ ├── covert_29.rda
│ ├── covert_3.rda
│ ├── covert_30.rda
│ ├── covert_31.rda
│ ├── covert_32.rda
│ ├── covert_33.rda
│ ├── covert_34.rda
│ ├── covert_35.rda
│ ├── covert_36.rda
│ ├── covert_37.rda
│ ├── covert_38.rda
│ ├── covert_39.rda
│ ├── covert_4.rda
│ ├── covert_40.rda
│ ├── covert_41.rda
│ ├── covert_42.rda
│ ├── covert_43.rda
│ ├── covert_44.rda
│ ├── covert_45.rda
│ ├── covert_46.rda
│ ├── covert_47.rda
│ ├── covert_6.rda
│ ├── covert_7.rda
│ ├── covert_8.rda
│ ├── covert_9.rda
│ ├── crime.rda
│ ├── dnc_corecipient.rda
│ ├── dnc_temporalGraph.rda
│ ├── dolphins_1.rda
│ ├── dolphins_2.rda
│ ├── eies_messages.rda
│ ├── eies_relations.rda
│ ├── euroroad.rda
│ ├── f2f_hypertext.rda
│ ├── f2f_infectious.rda
│ ├── ffe_elite.rda
│ ├── ffe_friends.rda
│ ├── ffe_influence.rda
│ ├── flo_business.rda
│ ├── flo_marriage.rda
│ ├── football_triad.rda
│ ├── fraternity.rda
│ ├── giraffe.rda
│ ├── glasgow129.rda
│ ├── got.rda
│ ├── greys.rda
│ ├── gss_egor.rda
│ ├── hall.rda
│ ├── hens.rda
│ ├── highschool_boys.rda
│ ├── ht_advice.rda
│ ├── ht_friends.rda
│ ├── ht_reports.rda
│ ├── insna.rda
│ ├── jazz.rda
│ ├── jpr.rda
│ ├── kangaroo.rda
│ ├── karate.rda
│ ├── karate_weight.rda
│ ├── knecht.rda
│ ├── law_advice.rda
│ ├── law_cowork.rda
│ ├── law_friends.rda
│ ├── literary.rda
│ ├── maayan_faa.rda
│ ├── maayan_pdzbase.rda
│ ├── macaque.rda
│ ├── mine.rda
│ ├── miserables.rda
│ ├── movie_1.rda
│ ├── movie_10.rda
│ ├── movie_100.rda
│ ├── movie_101.rda
│ ├── movie_102.rda
│ ├── movie_103.rda
│ ├── movie_104.rda
│ ├── movie_105.rda
│ ├── movie_106.rda
│ ├── movie_107.rda
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│ ├── movie_109.rda
│ ├── movie_11.rda
│ ├── movie_110.rda
│ ├── movie_111.rda
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│ ├── movie_114.rda
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│ ├── movie_116.rda
│ ├── movie_117.rda
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│ ├── movie_12.rda
│ ├── movie_120.rda
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│ ├── movie_128.rda
│ ├── movie_129.rda
│ ├── movie_13.rda
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│ ├── movie_14.rda
│ ├── movie_140.rda
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│ ├── movie_15.rda
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│ ├── movie_16.rda
│ ├── movie_160.rda
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│ ├── movie_17.rda
│ ├── movie_170.rda
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│ ├── movie_18.rda
│ ├── movie_180.rda
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│ ├── movie_182.rda
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│ ├── movie_187.rda
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│ ├── movie_189.rda
│ ├── movie_19.rda
│ ├── movie_190.rda
│ ├── movie_191.rda
│ ├── movie_192.rda
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│ ├── movie_194.rda
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│ ├── movie_198.rda
│ ├── movie_199.rda
│ ├── movie_2.rda
│ ├── movie_20.rda
│ ├── movie_200.rda
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│ ├── movie_21.rda
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│ ├── movie_22.rda
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│ ├── movie_23.rda
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│ ├── movie_26.rda
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│ ├── movie_27.rda
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│ ├── movie_502.rda
│ ├── movie_503.rda
│ ├── movie_504.rda
│ ├── movie_505.rda
│ ├── movie_506.rda
│ ├── movie_507.rda
│ ├── movie_508.rda
│ ├── movie_509.rda
│ ├── movie_51.rda
│ ├── movie_510.rda
│ ├── movie_511.rda
│ ├── movie_512.rda
│ ├── movie_513.rda
│ ├── movie_514.rda
│ ├── movie_515.rda
│ ├── movie_516.rda
│ ├── movie_517.rda
│ ├── movie_518.rda
│ ├── movie_519.rda
│ ├── movie_52.rda
│ ├── movie_520.rda
│ ├── movie_521.rda
│ ├── movie_522.rda
│ ├── movie_523.rda
│ ├── movie_524.rda
│ ├── movie_525.rda
│ ├── movie_526.rda
│ ├── movie_527.rda
│ ├── movie_528.rda
│ ├── movie_529.rda
│ ├── movie_53.rda
│ ├── movie_530.rda
│ ├── movie_531.rda
│ ├── movie_532.rda
│ ├── movie_533.rda
│ ├── movie_534.rda
│ ├── movie_535.rda
│ ├── movie_536.rda
│ ├── movie_537.rda
│ ├── movie_538.rda
│ ├── movie_539.rda
│ ├── movie_54.rda
│ ├── movie_540.rda
│ ├── movie_541.rda
│ ├── movie_542.rda
│ ├── movie_543.rda
│ ├── movie_544.rda
│ ├── movie_545.rda
│ ├── movie_546.rda
│ ├── movie_547.rda
│ ├── movie_548.rda
│ ├── movie_549.rda
│ ├── movie_55.rda
│ ├── movie_550.rda
│ ├── movie_551.rda
│ ├── movie_552.rda
│ ├── movie_553.rda
│ ├── movie_554.rda
│ ├── movie_555.rda
│ ├── movie_556.rda
│ ├── movie_557.rda
│ ├── movie_558.rda
│ ├── movie_559.rda
│ ├── movie_56.rda
│ ├── movie_560.rda
│ ├── movie_561.rda
│ ├── movie_562.rda
│ ├── movie_563.rda
│ ├── movie_564.rda
│ ├── movie_565.rda
│ ├── movie_566.rda
│ ├── movie_567.rda
│ ├── movie_568.rda
│ ├── movie_569.rda
│ ├── movie_57.rda
│ ├── movie_570.rda
│ ├── movie_571.rda
│ ├── movie_572.rda
│ ├── movie_573.rda
│ ├── movie_574.rda
│ ├── movie_575.rda
│ ├── movie_576.rda
│ ├── movie_577.rda
│ ├── movie_578.rda
│ ├── movie_579.rda
│ ├── movie_58.rda
│ ├── movie_580.rda
│ ├── movie_581.rda
│ ├── movie_582.rda
│ ├── movie_583.rda
│ ├── movie_584.rda
│ ├── movie_585.rda
│ ├── movie_586.rda
│ ├── movie_587.rda
│ ├── movie_588.rda
│ ├── movie_589.rda
│ ├── movie_59.rda
│ ├── movie_590.rda
│ ├── movie_591.rda
│ ├── movie_592.rda
│ ├── movie_593.rda
│ ├── movie_594.rda
│ ├── movie_595.rda
│ ├── movie_596.rda
│ ├── movie_597.rda
│ ├── movie_598.rda
│ ├── movie_599.rda
│ ├── movie_6.rda
│ ├── movie_60.rda
│ ├── movie_600.rda
│ ├── movie_601.rda
│ ├── movie_602.rda
│ ├── movie_603.rda
│ ├── movie_604.rda
│ ├── movie_605.rda
│ ├── movie_606.rda
│ ├── movie_607.rda
│ ├── movie_608.rda
│ ├── movie_609.rda
│ ├── movie_61.rda
│ ├── movie_610.rda
│ ├── movie_611.rda
│ ├── movie_612.rda
│ ├── movie_613.rda
│ ├── movie_614.rda
│ ├── movie_615.rda
│ ├── movie_616.rda
│ ├── movie_617.rda
│ ├── movie_618.rda
│ ├── movie_619.rda
│ ├── movie_62.rda
│ ├── movie_620.rda
│ ├── movie_621.rda
│ ├── movie_622.rda
│ ├── movie_623.rda
│ ├── movie_624.rda
│ ├── movie_625.rda
│ ├── movie_626.rda
│ ├── movie_627.rda
│ ├── movie_628.rda
│ ├── movie_629.rda
│ ├── movie_63.rda
│ ├── movie_630.rda
│ ├── movie_631.rda
│ ├── movie_632.rda
│ ├── movie_633.rda
│ ├── movie_634.rda
│ ├── movie_635.rda
│ ├── movie_636.rda
│ ├── movie_637.rda
│ ├── movie_638.rda
│ ├── movie_639.rda
│ ├── movie_64.rda
│ ├── movie_640.rda
│ ├── movie_641.rda
│ ├── movie_642.rda
│ ├── movie_643.rda
│ ├── movie_644.rda
│ ├── movie_645.rda
│ ├── movie_646.rda
│ ├── movie_647.rda
│ ├── movie_648.rda
│ ├── movie_649.rda
│ ├── movie_65.rda
│ ├── movie_650.rda
│ ├── movie_651.rda
│ ├── movie_652.rda
│ ├── movie_653.rda
│ ├── movie_654.rda
│ ├── movie_655.rda
│ ├── movie_656.rda
│ ├── movie_657.rda
│ ├── movie_658.rda
│ ├── movie_659.rda
│ ├── movie_66.rda
│ ├── movie_660.rda
│ ├── movie_661.rda
│ ├── movie_662.rda
│ ├── movie_663.rda
│ ├── movie_664.rda
│ ├── movie_665.rda
│ ├── movie_666.rda
│ ├── movie_667.rda
│ ├── movie_668.rda
│ ├── movie_669.rda
│ ├── movie_67.rda
│ ├── movie_670.rda
│ ├── movie_671.rda
│ ├── movie_672.rda
│ ├── movie_673.rda
│ ├── movie_674.rda
│ ├── movie_675.rda
│ ├── movie_676.rda
│ ├── movie_677.rda
│ ├── movie_678.rda
│ ├── movie_679.rda
│ ├── movie_68.rda
│ ├── movie_680.rda
│ ├── movie_681.rda
│ ├── movie_682.rda
│ ├── movie_683.rda
│ ├── movie_684.rda
│ ├── movie_685.rda
│ ├── movie_686.rda
│ ├── movie_687.rda
│ ├── movie_688.rda
│ ├── movie_689.rda
│ ├── movie_69.rda
│ ├── movie_690.rda
│ ├── movie_691.rda
│ ├── movie_692.rda
│ ├── movie_693.rda
│ ├── movie_694.rda
│ ├── movie_695.rda
│ ├── movie_696.rda
│ ├── movie_697.rda
│ ├── movie_698.rda
│ ├── movie_699.rda
│ ├── movie_7.rda
│ ├── movie_70.rda
│ ├── movie_700.rda
│ ├── movie_701.rda
│ ├── movie_702.rda
│ ├── movie_703.rda
│ ├── movie_704.rda
│ ├── movie_705.rda
│ ├── movie_706.rda
│ ├── movie_707.rda
│ ├── movie_708.rda
│ ├── movie_709.rda
│ ├── movie_71.rda
│ ├── movie_710.rda
│ ├── movie_711.rda
│ ├── movie_712.rda
│ ├── movie_713.rda
│ ├── movie_714.rda
│ ├── movie_715.rda
│ ├── movie_716.rda
│ ├── movie_717.rda
│ ├── movie_718.rda
│ ├── movie_719.rda
│ ├── movie_72.rda
│ ├── movie_720.rda
│ ├── movie_721.rda
│ ├── movie_722.rda
│ ├── movie_723.rda
│ ├── movie_724.rda
│ ├── movie_725.rda
│ ├── movie_726.rda
│ ├── movie_727.rda
│ ├── movie_728.rda
│ ├── movie_729.rda
│ ├── movie_73.rda
│ ├── movie_730.rda
│ ├── movie_731.rda
│ ├── movie_732.rda
│ ├── movie_733.rda
│ ├── movie_734.rda
│ ├── movie_735.rda
│ ├── movie_736.rda
│ ├── movie_737.rda
│ ├── movie_738.rda
│ ├── movie_739.rda
│ ├── movie_74.rda
│ ├── movie_740.rda
│ ├── movie_741.rda
│ ├── movie_742.rda
│ ├── movie_743.rda
│ ├── movie_744.rda
│ ├── movie_745.rda
│ ├── movie_746.rda
│ ├── movie_747.rda
│ ├── movie_748.rda
│ ├── movie_749.rda
│ ├── movie_75.rda
│ ├── movie_750.rda
│ ├── movie_751.rda
│ ├── movie_752.rda
│ ├── movie_753.rda
│ ├── movie_754.rda
│ ├── movie_755.rda
│ ├── movie_756.rda
│ ├── movie_757.rda
│ ├── movie_758.rda
│ ├── movie_759.rda
│ ├── movie_76.rda
│ ├── movie_760.rda
│ ├── movie_761.rda
│ ├── movie_762.rda
│ ├── movie_763.rda
│ ├── movie_764.rda
│ ├── movie_765.rda
│ ├── movie_766.rda
│ ├── movie_767.rda
│ ├── movie_768.rda
│ ├── movie_769.rda
│ ├── movie_77.rda
│ ├── movie_770.rda
│ ├── movie_771.rda
│ ├── movie_772.rda
│ ├── movie_773.rda
│ ├── movie_78.rda
│ ├── movie_79.rda
│ ├── movie_8.rda
│ ├── movie_80.rda
│ ├── movie_81.rda
│ ├── movie_82.rda
│ ├── movie_83.rda
│ ├── movie_84.rda
│ ├── movie_85.rda
│ ├── movie_86.rda
│ ├── movie_87.rda
│ ├── movie_88.rda
│ ├── movie_89.rda
│ ├── movie_9.rda
│ ├── movie_90.rda
│ ├── movie_91.rda
│ ├── movie_92.rda
│ ├── movie_93.rda
│ ├── movie_94.rda
│ ├── movie_95.rda
│ ├── movie_96.rda
│ ├── movie_97.rda
│ ├── movie_98.rda
│ ├── movie_99.rda
│ ├── netsci.rda
│ ├── petster.rda
│ ├── physicians.rda
│ ├── polblogs.rda
│ ├── polbooks.rda
│ ├── pony.rda
│ ├── powergrid.rda
│ ├── protein.rda
│ ├── radoslaw_email.rda
│ ├── rhesus.rda
│ ├── s50.rda
│ ├── sampson.rda
│ ├── shakespeare_1.rda
│ ├── shakespeare_10.rda
│ ├── shakespeare_11.rda
│ ├── shakespeare_12.rda
│ ├── shakespeare_13.rda
│ ├── shakespeare_14.rda
│ ├── shakespeare_15.rda
│ ├── shakespeare_16.rda
│ ├── shakespeare_17.rda
│ ├── shakespeare_18.rda
│ ├── shakespeare_19.rda
│ ├── shakespeare_2.rda
│ ├── shakespeare_20.rda
│ ├── shakespeare_21.rda
│ ├── shakespeare_22.rda
│ ├── shakespeare_23.rda
│ ├── shakespeare_24.rda
│ ├── shakespeare_25.rda
│ ├── shakespeare_26.rda
│ ├── shakespeare_27.rda
│ ├── shakespeare_28.rda
│ ├── shakespeare_29.rda
│ ├── shakespeare_3.rda
│ ├── shakespeare_30.rda
│ ├── shakespeare_31.rda
│ ├── shakespeare_32.rda
│ ├── shakespeare_33.rda
│ ├── shakespeare_34.rda
│ ├── shakespeare_35.rda
│ ├── shakespeare_36.rda
│ ├── shakespeare_4.rda
│ ├── shakespeare_5.rda
│ ├── shakespeare_6.rda
│ ├── shakespeare_7.rda
│ ├── shakespeare_8.rda
│ ├── shakespeare_9.rda
│ ├── sheep.rda
│ ├── sn_auth.rda
│ ├── southern_women.rda
│ ├── starwars.rda
│ ├── surfersb.rda
│ ├── surfersc.rda
│ ├── tailor_social.rda
│ ├── tailor_work.rda
│ ├── taro.rda
│ ├── train.rda
│ ├── ucforum.rda
│ ├── ucsocial.rda
│ ├── unicodelang.rda
│ ├── us_flights.rda
│ ├── usa_borders.rda
│ ├── usflights.rda
│ ├── wiring.rda
│ └── wta.rda
├── data-raw/
│ ├── glasgow129.R
│ └── gss.R
├── inst/
│ └── CITATION
└── man/
├── adjnoun.Rd
├── animal_1.Rd
├── animal_10.Rd
├── animal_11.Rd
├── animal_12.Rd
├── animal_13.Rd
├── animal_14.Rd
├── animal_15.Rd
├── animal_16.Rd
├── animal_17.Rd
├── animal_18.Rd
├── animal_19.Rd
├── animal_2.Rd
├── animal_20.Rd
├── animal_21.Rd
├── animal_22.Rd
├── animal_23.Rd
├── animal_24.Rd
├── animal_25.Rd
├── animal_26.Rd
├── animal_27.Rd
├── animal_28.Rd
├── animal_29.Rd
├── animal_3.Rd
├── animal_30.Rd
├── animal_31.Rd
├── animal_32.Rd
├── animal_33.Rd
├── animal_34.Rd
├── animal_35.Rd
├── animal_36.Rd
├── animal_4.Rd
├── animal_5.Rd
├── animal_6.Rd
├── animal_7.Rd
├── animal_8.Rd
├── animal_9.Rd
├── ants_1.Rd
├── ants_2.Rd
├── arenas_email.Rd
├── arenas_meta.Rd
├── atp.Rd
├── bible.Rd
├── bkfrab.Rd
├── bkfrac.Rd
├── bkoffb.Rd
├── bkoffc.Rd
├── bktecb.Rd
├── bktecc.Rd
├── bott.Rd
├── brunson_club_membership.Rd
├── brunson_corporate_leadership.Rd
├── brunson_revolution.Rd
├── brunson_south_africa.Rd
├── cent_lit.Rd
├── ceos_clubs.Rd
├── chicagoroad.Rd
├── clique_graph.Rd
├── coleman.Rd
├── core_graph.Rd
├── cosponsor.Rd
├── covert_1.Rd
├── covert_10.Rd
├── covert_11.Rd
├── covert_12.Rd
├── covert_13.Rd
├── covert_14.Rd
├── covert_15.Rd
├── covert_16.Rd
├── covert_17.Rd
├── covert_18.Rd
├── covert_19.Rd
├── covert_2.Rd
├── covert_20.Rd
├── covert_21.Rd
├── covert_22.Rd
├── covert_23.Rd
├── covert_24.Rd
├── covert_25.Rd
├── covert_26.Rd
├── covert_27.Rd
├── covert_28.Rd
├── covert_29.Rd
├── covert_3.Rd
├── covert_30.Rd
├── covert_31.Rd
├── covert_32.Rd
├── covert_33.Rd
├── covert_34.Rd
├── covert_35.Rd
├── covert_36.Rd
├── covert_37.Rd
├── covert_38.Rd
├── covert_39.Rd
├── covert_4.Rd
├── covert_40.Rd
├── covert_41.Rd
├── covert_42.Rd
├── covert_43.Rd
├── covert_44.Rd
├── covert_45.Rd
├── covert_46.Rd
├── covert_47.Rd
├── covert_6.Rd
├── covert_7.Rd
├── covert_8.Rd
├── covert_9.Rd
├── crime.Rd
├── dnc_corecipient.Rd
├── dnc_temporalGraph.Rd
├── dolphins_1.Rd
├── dolphins_2.Rd
├── eies_messages.Rd
├── eies_relations.Rd
├── euroroad.Rd
├── f2f_hypertext.Rd
├── f2f_infectious.Rd
├── ffe_elite.Rd
├── ffe_friends.Rd
├── ffe_influence.Rd
├── flo_business.Rd
├── flo_marriage.Rd
├── football_triad.Rd
├── fraternity.Rd
├── giraffe.Rd
├── glasgow129.Rd
├── got.Rd
├── greys.Rd
├── gss_egor.Rd
├── hall.Rd
├── hens.Rd
├── highschool_boys.Rd
├── ht_advice.Rd
├── ht_friends.Rd
├── ht_reports.Rd
├── insna.Rd
├── jazz.Rd
├── jpr.Rd
├── kangaroo.Rd
├── karate.Rd
├── karate_weight.Rd
├── knecht.Rd
├── law_advice.Rd
├── law_cowork.Rd
├── law_friends.Rd
├── literary.Rd
├── maayan_faa.Rd
├── maayan_pdzbase.Rd
├── macaque.Rd
├── mine.Rd
├── miserables.Rd
├── movie_1.Rd
├── movie_10.Rd
├── movie_100.Rd
├── movie_101.Rd
├── movie_102.Rd
├── movie_103.Rd
├── movie_104.Rd
├── movie_105.Rd
├── movie_106.Rd
├── movie_107.Rd
├── movie_108.Rd
├── movie_109.Rd
├── movie_11.Rd
├── movie_110.Rd
├── movie_111.Rd
├── movie_112.Rd
├── movie_113.Rd
├── movie_114.Rd
├── movie_115.Rd
├── movie_116.Rd
├── movie_117.Rd
├── movie_118.Rd
├── movie_119.Rd
├── movie_12.Rd
├── movie_120.Rd
├── movie_121.Rd
├── movie_122.Rd
├── movie_123.Rd
├── movie_124.Rd
├── movie_125.Rd
├── movie_126.Rd
├── movie_127.Rd
├── movie_128.Rd
├── movie_129.Rd
├── movie_13.Rd
├── movie_130.Rd
├── movie_131.Rd
├── movie_132.Rd
├── movie_133.Rd
├── movie_134.Rd
├── movie_135.Rd
├── movie_136.Rd
├── movie_137.Rd
├── movie_138.Rd
├── movie_139.Rd
├── movie_14.Rd
├── movie_140.Rd
├── movie_141.Rd
├── movie_142.Rd
├── movie_143.Rd
├── movie_144.Rd
├── movie_145.Rd
├── movie_146.Rd
├── movie_147.Rd
├── movie_148.Rd
├── movie_149.Rd
├── movie_15.Rd
├── movie_150.Rd
├── movie_151.Rd
├── movie_152.Rd
├── movie_153.Rd
├── movie_154.Rd
├── movie_155.Rd
├── movie_156.Rd
├── movie_157.Rd
├── movie_158.Rd
├── movie_159.Rd
├── movie_16.Rd
├── movie_160.Rd
├── movie_161.Rd
├── movie_162.Rd
├── movie_163.Rd
├── movie_164.Rd
├── movie_165.Rd
├── movie_166.Rd
├── movie_167.Rd
├── movie_168.Rd
├── movie_169.Rd
├── movie_17.Rd
├── movie_170.Rd
├── movie_171.Rd
├── movie_172.Rd
├── movie_173.Rd
├── movie_174.Rd
├── movie_175.Rd
├── movie_176.Rd
├── movie_177.Rd
├── movie_178.Rd
├── movie_179.Rd
├── movie_18.Rd
├── movie_180.Rd
├── movie_181.Rd
├── movie_182.Rd
├── movie_183.Rd
├── movie_184.Rd
├── movie_185.Rd
├── movie_186.Rd
├── movie_187.Rd
├── movie_188.Rd
├── movie_189.Rd
├── movie_19.Rd
├── movie_190.Rd
├── movie_191.Rd
├── movie_192.Rd
├── movie_193.Rd
├── movie_194.Rd
├── movie_195.Rd
├── movie_196.Rd
├── movie_197.Rd
├── movie_198.Rd
├── movie_199.Rd
├── movie_2.Rd
├── movie_20.Rd
├── movie_200.Rd
├── movie_201.Rd
├── movie_202.Rd
├── movie_203.Rd
├── movie_204.Rd
├── movie_205.Rd
├── movie_206.Rd
├── movie_207.Rd
├── movie_208.Rd
├── movie_209.Rd
├── movie_21.Rd
├── movie_210.Rd
├── movie_211.Rd
├── movie_212.Rd
├── movie_213.Rd
├── movie_214.Rd
├── movie_215.Rd
├── movie_216.Rd
├── movie_217.Rd
├── movie_218.Rd
├── movie_219.Rd
├── movie_22.Rd
├── movie_220.Rd
├── movie_221.Rd
├── movie_222.Rd
├── movie_223.Rd
├── movie_224.Rd
├── movie_225.Rd
├── movie_226.Rd
├── movie_227.Rd
├── movie_228.Rd
├── movie_229.Rd
├── movie_23.Rd
├── movie_230.Rd
├── movie_231.Rd
├── movie_232.Rd
├── movie_233.Rd
├── movie_234.Rd
├── movie_235.Rd
├── movie_236.Rd
├── movie_237.Rd
├── movie_238.Rd
├── movie_239.Rd
├── movie_24.Rd
├── movie_240.Rd
├── movie_241.Rd
├── movie_242.Rd
├── movie_243.Rd
├── movie_244.Rd
├── movie_245.Rd
├── movie_246.Rd
├── movie_247.Rd
├── movie_248.Rd
├── movie_249.Rd
├── movie_25.Rd
├── movie_250.Rd
├── movie_251.Rd
├── movie_252.Rd
├── movie_253.Rd
├── movie_254.Rd
├── movie_255.Rd
├── movie_256.Rd
├── movie_257.Rd
├── movie_258.Rd
├── movie_259.Rd
├── movie_26.Rd
├── movie_260.Rd
├── movie_261.Rd
├── movie_262.Rd
├── movie_263.Rd
├── movie_264.Rd
├── movie_265.Rd
├── movie_266.Rd
├── movie_267.Rd
├── movie_268.Rd
├── movie_269.Rd
├── movie_27.Rd
├── movie_270.Rd
├── movie_271.Rd
├── movie_272.Rd
├── movie_273.Rd
├── movie_274.Rd
├── movie_275.Rd
├── movie_276.Rd
├── movie_277.Rd
├── movie_278.Rd
├── movie_279.Rd
├── movie_28.Rd
├── movie_280.Rd
├── movie_281.Rd
├── movie_282.Rd
├── movie_283.Rd
├── movie_284.Rd
├── movie_285.Rd
├── movie_286.Rd
├── movie_287.Rd
├── movie_288.Rd
├── movie_289.Rd
├── movie_29.Rd
├── movie_290.Rd
├── movie_291.Rd
├── movie_292.Rd
├── movie_293.Rd
├── movie_294.Rd
├── movie_295.Rd
├── movie_296.Rd
├── movie_297.Rd
├── movie_298.Rd
├── movie_299.Rd
├── movie_3.Rd
├── movie_30.Rd
├── movie_300.Rd
├── movie_301.Rd
├── movie_302.Rd
├── movie_303.Rd
├── movie_304.Rd
├── movie_305.Rd
├── movie_306.Rd
├── movie_307.Rd
├── movie_308.Rd
├── movie_309.Rd
├── movie_31.Rd
├── movie_310.Rd
├── movie_311.Rd
├── movie_312.Rd
├── movie_313.Rd
├── movie_314.Rd
├── movie_315.Rd
├── movie_316.Rd
├── movie_317.Rd
├── movie_318.Rd
├── movie_319.Rd
├── movie_32.Rd
├── movie_320.Rd
├── movie_321.Rd
├── movie_322.Rd
├── movie_323.Rd
├── movie_324.Rd
├── movie_325.Rd
├── movie_326.Rd
├── movie_327.Rd
├── movie_328.Rd
├── movie_329.Rd
├── movie_33.Rd
├── movie_330.Rd
├── movie_331.Rd
├── movie_332.Rd
├── movie_333.Rd
├── movie_334.Rd
├── movie_335.Rd
├── movie_336.Rd
├── movie_337.Rd
├── movie_338.Rd
├── movie_339.Rd
├── movie_34.Rd
├── movie_340.Rd
├── movie_341.Rd
├── movie_342.Rd
├── movie_343.Rd
├── movie_344.Rd
├── movie_345.Rd
├── movie_346.Rd
├── movie_347.Rd
├── movie_348.Rd
├── movie_349.Rd
├── movie_35.Rd
├── movie_350.Rd
├── movie_351.Rd
├── movie_352.Rd
├── movie_353.Rd
├── movie_354.Rd
├── movie_355.Rd
├── movie_356.Rd
├── movie_357.Rd
├── movie_358.Rd
├── movie_359.Rd
├── movie_36.Rd
├── movie_360.Rd
├── movie_361.Rd
├── movie_362.Rd
├── movie_363.Rd
├── movie_364.Rd
├── movie_365.Rd
├── movie_366.Rd
├── movie_367.Rd
├── movie_368.Rd
├── movie_369.Rd
├── movie_37.Rd
├── movie_370.Rd
├── movie_371.Rd
├── movie_372.Rd
├── movie_373.Rd
├── movie_374.Rd
├── movie_375.Rd
├── movie_376.Rd
├── movie_377.Rd
├── movie_378.Rd
├── movie_379.Rd
├── movie_38.Rd
├── movie_380.Rd
├── movie_381.Rd
├── movie_382.Rd
├── movie_383.Rd
├── movie_384.Rd
├── movie_385.Rd
├── movie_386.Rd
├── movie_387.Rd
├── movie_388.Rd
├── movie_389.Rd
├── movie_39.Rd
├── movie_390.Rd
├── movie_391.Rd
├── movie_392.Rd
├── movie_393.Rd
├── movie_394.Rd
├── movie_395.Rd
├── movie_396.Rd
├── movie_397.Rd
├── movie_398.Rd
├── movie_399.Rd
├── movie_4.Rd
├── movie_40.Rd
├── movie_400.Rd
├── movie_401.Rd
├── movie_402.Rd
├── movie_403.Rd
├── movie_404.Rd
├── movie_405.Rd
├── movie_406.Rd
├── movie_407.Rd
├── movie_408.Rd
├── movie_409.Rd
├── movie_41.Rd
├── movie_410.Rd
├── movie_411.Rd
├── movie_412.Rd
├── movie_413.Rd
├── movie_414.Rd
├── movie_415.Rd
├── movie_416.Rd
├── movie_417.Rd
├── movie_418.Rd
├── movie_419.Rd
├── movie_42.Rd
├── movie_420.Rd
├── movie_421.Rd
├── movie_422.Rd
├── movie_423.Rd
├── movie_424.Rd
├── movie_425.Rd
├── movie_426.Rd
├── movie_427.Rd
├── movie_428.Rd
├── movie_429.Rd
├── movie_43.Rd
├── movie_430.Rd
├── movie_431.Rd
├── movie_432.Rd
├── movie_433.Rd
├── movie_434.Rd
├── movie_435.Rd
├── movie_436.Rd
├── movie_437.Rd
├── movie_438.Rd
├── movie_439.Rd
├── movie_44.Rd
├── movie_440.Rd
├── movie_441.Rd
├── movie_442.Rd
├── movie_443.Rd
├── movie_444.Rd
├── movie_445.Rd
├── movie_446.Rd
├── movie_447.Rd
├── movie_448.Rd
├── movie_449.Rd
├── movie_45.Rd
├── movie_450.Rd
├── movie_451.Rd
├── movie_452.Rd
├── movie_453.Rd
├── movie_454.Rd
├── movie_455.Rd
├── movie_456.Rd
├── movie_457.Rd
├── movie_458.Rd
├── movie_459.Rd
├── movie_46.Rd
├── movie_460.Rd
├── movie_461.Rd
├── movie_462.Rd
├── movie_463.Rd
├── movie_464.Rd
├── movie_465.Rd
├── movie_466.Rd
├── movie_467.Rd
├── movie_468.Rd
├── movie_469.Rd
├── movie_47.Rd
├── movie_470.Rd
├── movie_471.Rd
├── movie_472.Rd
├── movie_473.Rd
├── movie_474.Rd
├── movie_475.Rd
├── movie_476.Rd
├── movie_477.Rd
├── movie_478.Rd
├── movie_479.Rd
├── movie_48.Rd
├── movie_480.Rd
├── movie_481.Rd
├── movie_482.Rd
├── movie_483.Rd
├── movie_484.Rd
├── movie_485.Rd
├── movie_486.Rd
├── movie_487.Rd
├── movie_488.Rd
├── movie_489.Rd
├── movie_49.Rd
├── movie_490.Rd
├── movie_491.Rd
├── movie_492.Rd
├── movie_493.Rd
├── movie_494.Rd
├── movie_495.Rd
├── movie_496.Rd
├── movie_497.Rd
├── movie_498.Rd
├── movie_499.Rd
├── movie_5.Rd
├── movie_50.Rd
├── movie_500.Rd
├── movie_501.Rd
├── movie_502.Rd
├── movie_503.Rd
├── movie_504.Rd
├── movie_505.Rd
├── movie_506.Rd
├── movie_507.Rd
├── movie_508.Rd
├── movie_509.Rd
├── movie_51.Rd
├── movie_510.Rd
├── movie_511.Rd
├── movie_512.Rd
├── movie_513.Rd
├── movie_514.Rd
├── movie_515.Rd
├── movie_516.Rd
├── movie_517.Rd
├── movie_518.Rd
├── movie_519.Rd
├── movie_52.Rd
├── movie_520.Rd
├── movie_521.Rd
├── movie_522.Rd
├── movie_523.Rd
├── movie_524.Rd
├── movie_525.Rd
├── movie_526.Rd
├── movie_527.Rd
├── movie_528.Rd
├── movie_529.Rd
├── movie_53.Rd
├── movie_530.Rd
├── movie_531.Rd
├── movie_532.Rd
├── movie_533.Rd
├── movie_534.Rd
├── movie_535.Rd
├── movie_536.Rd
├── movie_537.Rd
├── movie_538.Rd
├── movie_539.Rd
├── movie_54.Rd
├── movie_540.Rd
├── movie_541.Rd
├── movie_542.Rd
├── movie_543.Rd
├── movie_544.Rd
├── movie_545.Rd
├── movie_546.Rd
├── movie_547.Rd
├── movie_548.Rd
├── movie_549.Rd
├── movie_55.Rd
├── movie_550.Rd
├── movie_551.Rd
├── movie_552.Rd
├── movie_553.Rd
├── movie_554.Rd
├── movie_555.Rd
├── movie_556.Rd
├── movie_557.Rd
├── movie_558.Rd
├── movie_559.Rd
├── movie_56.Rd
├── movie_560.Rd
├── movie_561.Rd
├── movie_562.Rd
├── movie_563.Rd
├── movie_564.Rd
├── movie_565.Rd
├── movie_566.Rd
├── movie_567.Rd
├── movie_568.Rd
├── movie_569.Rd
├── movie_57.Rd
├── movie_570.Rd
├── movie_571.Rd
├── movie_572.Rd
├── movie_573.Rd
├── movie_574.Rd
├── movie_575.Rd
├── movie_576.Rd
├── movie_577.Rd
├── movie_578.Rd
├── movie_579.Rd
├── movie_58.Rd
├── movie_580.Rd
├── movie_581.Rd
├── movie_582.Rd
├── movie_583.Rd
├── movie_584.Rd
├── movie_585.Rd
├── movie_586.Rd
├── movie_587.Rd
├── movie_588.Rd
├── movie_589.Rd
├── movie_59.Rd
├── movie_590.Rd
├── movie_591.Rd
├── movie_592.Rd
├── movie_593.Rd
├── movie_594.Rd
├── movie_595.Rd
├── movie_596.Rd
├── movie_597.Rd
├── movie_598.Rd
├── movie_599.Rd
├── movie_6.Rd
├── movie_60.Rd
├── movie_600.Rd
├── movie_601.Rd
├── movie_602.Rd
├── movie_603.Rd
├── movie_604.Rd
├── movie_605.Rd
├── movie_606.Rd
├── movie_607.Rd
├── movie_608.Rd
├── movie_609.Rd
├── movie_61.Rd
├── movie_610.Rd
├── movie_611.Rd
├── movie_612.Rd
├── movie_613.Rd
├── movie_614.Rd
├── movie_615.Rd
├── movie_616.Rd
├── movie_617.Rd
├── movie_618.Rd
├── movie_619.Rd
├── movie_62.Rd
├── movie_620.Rd
├── movie_621.Rd
├── movie_622.Rd
├── movie_623.Rd
├── movie_624.Rd
├── movie_625.Rd
├── movie_626.Rd
├── movie_627.Rd
├── movie_628.Rd
├── movie_629.Rd
├── movie_63.Rd
├── movie_630.Rd
├── movie_631.Rd
├── movie_632.Rd
├── movie_633.Rd
├── movie_634.Rd
├── movie_635.Rd
├── movie_636.Rd
├── movie_637.Rd
├── movie_638.Rd
├── movie_639.Rd
├── movie_64.Rd
├── movie_640.Rd
├── movie_641.Rd
├── movie_642.Rd
├── movie_643.Rd
├── movie_644.Rd
├── movie_645.Rd
├── movie_646.Rd
├── movie_647.Rd
├── movie_648.Rd
├── movie_649.Rd
├── movie_65.Rd
├── movie_650.Rd
├── movie_651.Rd
├── movie_652.Rd
├── movie_653.Rd
├── movie_654.Rd
├── movie_655.Rd
├── movie_656.Rd
├── movie_657.Rd
├── movie_658.Rd
├── movie_659.Rd
├── movie_66.Rd
├── movie_660.Rd
├── movie_661.Rd
├── movie_662.Rd
├── movie_663.Rd
├── movie_664.Rd
├── movie_665.Rd
├── movie_666.Rd
├── movie_667.Rd
├── movie_668.Rd
├── movie_669.Rd
├── movie_67.Rd
├── movie_670.Rd
├── movie_671.Rd
├── movie_672.Rd
├── movie_673.Rd
├── movie_674.Rd
├── movie_675.Rd
├── movie_676.Rd
├── movie_677.Rd
├── movie_678.Rd
├── movie_679.Rd
├── movie_68.Rd
├── movie_680.Rd
├── movie_681.Rd
├── movie_682.Rd
├── movie_683.Rd
├── movie_684.Rd
├── movie_685.Rd
├── movie_686.Rd
├── movie_687.Rd
├── movie_688.Rd
├── movie_689.Rd
├── movie_69.Rd
├── movie_690.Rd
├── movie_691.Rd
├── movie_692.Rd
├── movie_693.Rd
├── movie_694.Rd
├── movie_695.Rd
├── movie_696.Rd
├── movie_697.Rd
├── movie_698.Rd
├── movie_699.Rd
├── movie_7.Rd
├── movie_70.Rd
├── movie_700.Rd
├── movie_701.Rd
├── movie_702.Rd
├── movie_703.Rd
├── movie_704.Rd
├── movie_705.Rd
├── movie_706.Rd
├── movie_707.Rd
├── movie_708.Rd
├── movie_709.Rd
├── movie_71.Rd
├── movie_710.Rd
├── movie_711.Rd
├── movie_712.Rd
├── movie_713.Rd
├── movie_714.Rd
├── movie_715.Rd
├── movie_716.Rd
├── movie_717.Rd
├── movie_718.Rd
├── movie_719.Rd
├── movie_72.Rd
├── movie_720.Rd
├── movie_721.Rd
├── movie_722.Rd
├── movie_723.Rd
├── movie_724.Rd
├── movie_725.Rd
├── movie_726.Rd
├── movie_727.Rd
├── movie_728.Rd
├── movie_729.Rd
├── movie_73.Rd
├── movie_730.Rd
├── movie_731.Rd
├── movie_732.Rd
├── movie_733.Rd
├── movie_734.Rd
├── movie_735.Rd
├── movie_736.Rd
├── movie_737.Rd
├── movie_738.Rd
├── movie_739.Rd
├── movie_74.Rd
├── movie_740.Rd
├── movie_741.Rd
├── movie_742.Rd
├── movie_743.Rd
├── movie_744.Rd
├── movie_745.Rd
├── movie_746.Rd
├── movie_747.Rd
├── movie_748.Rd
├── movie_749.Rd
├── movie_75.Rd
├── movie_750.Rd
├── movie_751.Rd
├── movie_752.Rd
├── movie_753.Rd
├── movie_754.Rd
├── movie_755.Rd
├── movie_756.Rd
├── movie_757.Rd
├── movie_758.Rd
├── movie_759.Rd
├── movie_76.Rd
├── movie_760.Rd
├── movie_761.Rd
├── movie_762.Rd
├── movie_763.Rd
├── movie_764.Rd
├── movie_765.Rd
├── movie_766.Rd
├── movie_767.Rd
├── movie_768.Rd
├── movie_769.Rd
├── movie_77.Rd
├── movie_770.Rd
├── movie_771.Rd
├── movie_772.Rd
├── movie_773.Rd
├── movie_78.Rd
├── movie_79.Rd
├── movie_8.Rd
├── movie_80.Rd
├── movie_81.Rd
├── movie_82.Rd
├── movie_83.Rd
├── movie_84.Rd
├── movie_85.Rd
├── movie_86.Rd
├── movie_87.Rd
├── movie_88.Rd
├── movie_89.Rd
├── movie_9.Rd
├── movie_90.Rd
├── movie_91.Rd
├── movie_92.Rd
├── movie_93.Rd
├── movie_94.Rd
├── movie_95.Rd
├── movie_96.Rd
├── movie_97.Rd
├── movie_98.Rd
├── movie_99.Rd
├── netsci.Rd
├── networkdata-package.Rd
├── petster.Rd
├── physicians.Rd
├── polblogs.Rd
├── polbooks.Rd
├── pony.Rd
├── powergrid.Rd
├── protein.Rd
├── radoslaw_email.Rd
├── rhesus.Rd
├── s50.Rd
├── sampson.Rd
├── shakespeare_1.Rd
├── shakespeare_10.Rd
├── shakespeare_11.Rd
├── shakespeare_12.Rd
├── shakespeare_13.Rd
├── shakespeare_14.Rd
├── shakespeare_15.Rd
├── shakespeare_16.Rd
├── shakespeare_17.Rd
├── shakespeare_18.Rd
├── shakespeare_19.Rd
├── shakespeare_2.Rd
├── shakespeare_20.Rd
├── shakespeare_21.Rd
├── shakespeare_22.Rd
├── shakespeare_23.Rd
├── shakespeare_24.Rd
├── shakespeare_25.Rd
├── shakespeare_26.Rd
├── shakespeare_27.Rd
├── shakespeare_28.Rd
├── shakespeare_29.Rd
├── shakespeare_3.Rd
├── shakespeare_30.Rd
├── shakespeare_31.Rd
├── shakespeare_32.Rd
├── shakespeare_33.Rd
├── shakespeare_34.Rd
├── shakespeare_35.Rd
├── shakespeare_36.Rd
├── shakespeare_4.Rd
├── shakespeare_5.Rd
├── shakespeare_6.Rd
├── shakespeare_7.Rd
├── shakespeare_8.Rd
├── shakespeare_9.Rd
├── sheep.Rd
├── sn_auth.Rd
├── southern_women.Rd
├── starwars.Rd
├── surfersb.Rd
├── surfersc.Rd
├── tailor_social.Rd
├── tailor_work.Rd
├── taro.Rd
├── train.Rd
├── ucforum.Rd
├── ucsocial.Rd
├── unicodelang.Rd
├── us_flights.Rd
├── usa_borders.Rd
├── usflights.Rd
├── wiring.Rd
└── wta.Rd
================================================
FILE CONTENTS
================================================
================================================
FILE: .Rbuildignore
================================================
^.*\.Rproj$
^\.Rproj\.user$
^LICENSE\.md$
^README\.Rmd$
^_pkgdown\.yml$
^docs$
^pkgdown$
^\.github$
^data-raw$
================================================
FILE: .github/.gitignore
================================================
*.html
================================================
FILE: .github/workflows/pkgdown.yaml
================================================
# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples
# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
on:
push:
branches: [main, master]
pull_request:
branches: [main, master]
release:
types: [published]
workflow_dispatch:
name: pkgdown
jobs:
pkgdown:
runs-on: ubuntu-latest
# Only restrict concurrency for non-PR jobs
concurrency:
group: pkgdown-${{ github.event_name != 'pull_request' || github.run_id }}
env:
GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
permissions:
contents: write
steps:
- uses: actions/checkout@v3
- uses: r-lib/actions/setup-pandoc@v2
- uses: r-lib/actions/setup-r@v2
with:
use-public-rspm: true
- uses: r-lib/actions/setup-r-dependencies@v2
with:
extra-packages: any::pkgdown, local::.
needs: website
- name: Build site
run: pkgdown::build_site_github_pages(new_process = FALSE, install = FALSE)
shell: Rscript {0}
- name: Deploy to GitHub pages 🚀
if: github.event_name != 'pull_request'
uses: JamesIves/github-pages-deploy-action@v4.4.1
with:
clean: false
branch: gh-pages
folder: docs
================================================
FILE: .gitignore
================================================
.Rproj.user
.Rhistory
.RData
.Ruserdata
docs
================================================
FILE: DESCRIPTION
================================================
Package: networkdata
Type: Package
Title: Repository of Network Datasets
Version: 0.2.4
Authors@R:
person(given = "David",
family = "Schoch",
role = c("aut", "cre"),
email = "david@schochastics.net",
comment = c(ORCID = "0000-0003-2952-4812"))
Description: The package contains a large collection of network dataset with different context. This includes social networks, animal networks and movie networks. All datasets are in 'igraph' format.
Depends:
R (>= 3.5)
URL: https://github.com/schochastics/networkdata, https://schochastics.github.io/networkdata/
BugReports: https://github.com/schochastics/networkdata/issues
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.3.2
Imports:
igraph
================================================
FILE: LICENSE
================================================
YEAR: 2019
COPYRIGHT HOLDER: David Schoch
================================================
FILE: LICENSE.md
================================================
# MIT License
Copyright (c) 2019 David Schoch
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
================================================
FILE: NAMESPACE
================================================
# Generated by roxygen2: do not edit by hand
================================================
FILE: NEWS.md
================================================
# networkdata 0.2.3
* added gss dataset
# networkdata 0.2.2
* added full Teenage Friends and Lifestyle Study
# networkdata 0.2.1
* added cosponsorship network
# networkdata 0.2.0
* updated graphs according to new igraph release
# networkdata 0.1.15
* added network names for sampson dataset (#10)
# networkdata 0.1.14
* added `knecht` dataset
# networkdata 0.1.13
* updated `wta` and `atp` until season 2021
* added `coleman_friends`
# networkdata 0.1.12
* added `core_graph`
# networkdata 0.1.11
* added `football_triad`
* added `clique_graph`
# networkdata 0.1.10
* fixed names of Florentine Families
# networkdata 0.1.9
* fixed error in `ffe_*` data (Freeman's page is down, so the new version comes from [here](https://github.com/zalmquist/networkdata))
# networkdata 0.1.8
* added `s50`
# networkdata 0.1.7
* fixed typo in `movie_439`
# networkdata 0.1.6
fixed error in `southern_women`
# networkdata 0.1.5
* added starwars networks
* removed `show_networks()`
# networkdata 0.1.4
* fixed list issue in `animal_34`
* fixed genders in `crime` (#5)
# networkdata 0.1.3
* fixed name mapping in florentine families dataset.
# networkdata 0.1.2
* some movie data was wrongly mapped. should be fixed now.
# networkdata 0.1.1
* `show_networks()` was returning a tibble which requires additional dependencies. The function now returns a data.frame
# networkdata 0.1.0
* Added a `NEWS.md` file to track changes to the package.
================================================
FILE: R/data-animals.R
================================================
#' Fishstickleback Proximity (weighted)
#'
#' @description
#'
#' Species: *Gasterosteus aculeatus*
#'
#' Taxonomic class: Actinopterygii
#'
#' Population type: captive
#'
#' Geographical location: St. Andrews, UK
#'
#' Data collection technique: video
#'
#' Interaction type: group membership
#'
#' Definition of interaction: A pair of individuals were classed as shoaling if they were within four body lengths of one another from head to head. Gambit of the group approach was then used to assume that a string of fish connected by less than two body lengths were all assumed to be associating with one another.
#'
#' Edge weight type: frequency
#'
#' Total duration of data collection: 120 min
#'
#' Time resolution of data collection (within a day): 6 min
#'
#' Time span of data collection (within a day): 120 min
#'
#' Note: Networks represent social data collected from seven replicate groups of fish
#'
#' @references Atton, N., et al. "Familiarity affects social network structure and discovery of prey patch locations in foraging stickleback shoals." Proceedings of the Royal Society of London B: Biological Sciences 281.1789 (2014): 20140579.
#'
#' @format list of igraph objects
#'
#' @source https: //bansallab.github.io/asnr/
#'
"animal_1"
#' Guppy Proximity Frequency (weighted)
#'
#' @description
#'
#' Species: *Poecilia reticulata*
#'
#' Taxonomic class: Actinopterygii
#'
#' Population type: captive
#'
#' Geographical location: Louisville, Kentucky
#'
#' Data collection technique: video
#'
#' Interaction type: spatial proximity
#'
#' Definition of interaction: A contact phase between two individuals was one or more consecutive observations in which they were recorded as being within four body length to one another.
#'
#' Edge weight type: frequency
#'
#' Total duration of data collection: 90min
#'
#' Time resolution of data collection (within a day): 10 seconds
#'
#' Time span of data collection (within a day): 90min
#'
#' Note: Networks represent 10 independed replicates of grourp of 5 guppies that were familiar with each other
#'
#' @references Hasenjager, Matthew J., and Lee Alan Dugatkin. "Familiarity affects network structure and information flow in guppy (Poecilia reticulata) shoals." Behavioral Ecology (2016): arw152.
#'
#' @format list of igraph objects
#'
#' @source https: //bansallab.github.io/asnr/
#'
"animal_2"
#' Hirundo rustica
#'
#' @description
#'
#' Species: *Hirundo rustica*
#'
#' Taxonomic class: Aves
#'
#' Population type: free-ranging
#'
#' Geographical location: Boulder County, Colorado, USA
#'
#' Data collection technique: logger
#'
#' Interaction type: physical contact
#'
#' Definition of interaction: Interaction 0.1 m and closer
#'
#' Edge weight type: frequency
#'
#' Total duration of data collection: 11 days
#'
#' Time resolution of data collection (within a day): 1 sec
#'
#' Time span of data collection (within a day): 6 hours
#'
#' Note: Two networks were constructed with edges weighted by the number of interactions at two spatial proximities: body contact interactions and all other spatially proximate interactions
#'
#' @references Levin, Iris I., et al. "Stress response, gut microbial diversity and sexual signals correlate with social interactions." Biology Letters 12.6 (2016): 20160352.
#'
#' @format list of igraph objects
#'
#' @source https: //bansallab.github.io/asnr/
#'
"animal_3"
#' Branta leucopsis
#'
#' @description
#'
#' Species: *Branta leucopsis*
#'
#' Taxonomic class: Aves
#'
#' Population type: captive
#'
#' Geographical location: Heteren, Netherlands
#'
#' Data collection technique: survey scan
#'
#' Interaction type: foraging
#'
#' Definition of interaction: Animals grazing on the same patch during a sampling period were assumed to be associating (gambit of the group)
#'
#' Edge weight type: frequency
#'
#' Total duration of data collection: 15 days
#'
#' Time resolution of data collection (within a day): 4min
#'
#' Time span of data collection (within a day): 4 hours
#'
#' Note: Networks represent male and female only network
#'
#' @references Kurvers, Ralf HJM, et al. "Contrasting context dependence of familiarity and kinship in animal social networks." Animal Behaviour 86.5 (2013): 993-1001.
#'
#' @format list of igraph objects
#'
#' @source https: //bansallab.github.io/asnr/
#'
"animal_4"
#' White Leghorn
#'
#' @description
#'
#' Species: *White leghorn*
#'
#' Taxonomic class: Aves
#'
#' Population type: captive
#'
#' Geographical location: Manhattan, Kansas
#'
#' Data collection technique: mn/unspecified
#'
#' Interaction type: dominance
#'
#' Definition of interaction: Dominance interaction recoded based on agonistic behavior until each bird has established its dominance and/or subordination.
#'
#' Edge weight type: frequency
#'
#' Total duration of data collection:
#'
#' Time resolution of data collection (within a day):
#'
#' Time span of data collection (within a day):
#'
#' Note:
#'
#' @references Guhl, A. M. "Social behavior of the domestic fowl." Transactions of the Kansas Academy of Science (1903-) 71.3 (1968): 379-384.
#'
#' @format list of igraph objects
#'
#' @source https: //bansallab.github.io/asnr/
#'
"animal_5"
#' Haemorhous mexicanus
#'
#' @description
#'
#' Species: *Haemorhous mexicanus*
#'
#' Taxonomic class: Aves
#'
#' Population type: free-ranging
#'
#' Geographical location: Virginia Tech, Virginia, USA
#'
#' Data collection technique: RFID
#'
#' Interaction type: social projection bipartite
#'
#' Definition of interaction: A machine learning algorithm was applied to identify clusters of detections on feeders. Next, the network was generated based on patterns of co-occurrence by individuals in the same feeding events. Asociations between birds were defined using the simple ratio index.
#'
#' Edge weight type: simple_ratio_index
#'
#' Total duration of data collection: 137 days
#'
#' Time resolution of data collection (within a day): 1 sec
#'
#' Time span of data collection (within a day): 24 hours
#'
#' Note:
#'
#' @references Adelman, James S., et al. "Feeder use predicts both acquisition and transmission of a contagious pathogen in a North American songbird." Proc. R. Soc. B. Vol. 282. No. 1815. The Royal Society, 2015.
#'
#' @format list of igraph objects
#'
#' @source https: //bansallab.github.io/asnr/
#'
"animal_6"
#' Zonotrichia Atricapilla
#'
#' @description
#'
#' Species: *Zonotrichia atricapilla*
#'
#' Taxonomic class: Aves
#'
#' Population type: free-ranging
#'
#' Geographical location: California, USA
#'
#' Data collection technique: survey scan
#'
#' Interaction type: group membership
#'
#' Definition of interaction: A flock was defined as a group of birds within an approximately 5-metre radius. Social networks of flock comembership was constructed where nodes represent individual birds and edges represent the simple ratio association index
#'
#' Edge weight type: simple_ratio_index
#'
#' Total duration of data collection: 3 months
#'
#' Time resolution of data collection (within a day):
#'
#' Time span of data collection (within a day): focal follow/ad libitum
#'
#' Note: Two networks collected over two consecutive years
#'
#' @references Arnberg, Nina N., et al. "Social network structure in wintering golden‐crowned sparrows is not correlated with kinship." Molecular ecology 24.19 (2015): 5034-5044.
#'
#' @format list of igraph objects
#'
#' @source https: //bansallab.github.io/asnr/
#'
"animal_7"
#' Zonotrichia atricapilla
#'
#' @description
#'
#' Species: *Zonotrichia atricapilla*
#'
#' Taxonomic class: Aves
#'
#' Population type: free-ranging
#'
#' Geographical location: California, USA
#'
#' Data collection technique: survey scan
#'
#' Interaction type: group membership
#'
#' Definition of interaction: Flocks were defined as a group of individuals found within a single 5 m radius. For each season, Simple Ratio association index was calculated for each pair of individuals, which ranged from 0 for pairs never seen in the same flock and 1 for pairs always seen in the same flock.
#'
#' Edge weight type: frequency
#'
#' Total duration of data collection: 4 months
#'
#' Time resolution of data collection (within a day):
#'
#' Time span of data collection (within a day): focal follow/ad libitum
#'
#' Note: Networks represent social data collected during two non-breeding seasons: October 2010�February 2011 (Season 2) and October2011�April 2012 (Season 3).
#'
#' @references Shizuka, Daizaburo, et al. "Across‐year social stability shapes network structure in wintering migrant sparrows." Ecology letters 17.8 (2014): 998-1007.
#'
#' @format list of igraph objects
#'
#' @source https: //bansallab.github.io/asnr/
#'
"animal_8"
#' Acanthiza
#'
#' @description
#'
#' Species: *Acanthiza*
#'
#' Taxonomic class: Aves
#'
#' Population type: free-ranging
#'
#' Geographical location: Canberra, Australia
#'
#' Data collection technique: survey scan
#'
#' Interaction type: group membership
#'
#' Definition of interaction: Flock membership was identified based on frequent interactions between (such as �beating� for insects) flocks and large gaps between flocks. Association strength of each dyad was calculated using the simple ratio index.
#'
#' Edge weight type: frequency
#'
#' Total duration of data collection: 2 months
#'
#' Time resolution of data collection (within a day):
#'
#' Time span of data collection (within a day): focal follow/ad libitum
#'
#' Note:
#'
#' @references Farine, Damien R., and Peter J. Milburn. "Social organisation of thornbill-dominated mixed-species flocks using social network analysis." Behavioral Ecology and Sociobiology 67.2 (2013): 321-330.
#'
#' @format list of igraph objects
#'
#' @source https: //bansallab.github.io/asnr/
#'
"animal_9"
#' Philetairus socius
#'
#' @description
#'
#' Species: *Philetairus socius*
#'
#' Taxonomic class: Aves
#'
#' Population type: free-ranging
#'
#' Geographical location: Kimberley, South Africa
#'
#' Data collection technique: mark recapture
#'
#' Interaction type: social projection bipartite
#'
#' Definition of interaction: A network edge was drawn between individuals that used the same nest chambers either for roosting or nest-building at any given time within a series of observations at the same colony in the same year, either together in the nest chamber at the same time or at different times.
#'
#' Edge weight type: unweighted
#'
#' Total duration of data collection: 10 months
#'
#' Time resolution of data collection (within a day):
#'
#' Time span of data collection (within a day): focal follow/ad libitum
#'
#' Note: Networks represent social data collected from 23 colonies of sociable weavers
#'
#' @references Dijk, René E., et al. "Cooperative investment in public goods is kin directed in communal nests of social birds." Ecology letters 17.9 (2014): 1141-1148.
#'
#' @format list of igraph objects
#'
#' @source https: //bansallab.github.io/asnr/
#'
"animal_10"
#' Wild birds
#'
#' @description
#'
#' Species: *Wild birds*
#'
#' Taxonomic class: Aves
#'
#' Population type: free-ranging
#'
#' Geographical location: Oxford, UK
#'
#' Data collection technique: RFID
#'
#' Interaction type: social projection bipartite
#'
#' Definition of interaction: Groups were defined as individuals detected on the same nest-box during the same day, and co-memberships represented individuals that overlapped in nest-box exploration patterns during the same day. Networks were calculated from these group-by-individual matrices using the halfweight index.
#'
#' Edge weight type: half_weight_index
#'
#' Total duration of data collection: 6 days
#'
#' Time resolution of data collection (within a day): 1 sec
#'
#' Time span of data collection (within a day): 12 hours
#'
#' Note: Each network represents social data collected for consecutive 6-day time window.
#'
#' @references Firth, Josh A., and Ben C. Sheldon. "Experimental manipulation of avian social structure reveals segregation is carried over across contexts." Proceedings of the Royal Society of London B: Biological Sciences 282.1802 (2015): 20142350.
#'
#' @format list of igraph objects
#'
#' @source https: //bansallab.github.io/asnr/
#'
"animal_11"
#' Ants Proximity (weighted)
#'
#' @description
#'
#' Species: *Camponotus fellah*
#'
#' Taxonomic class: Insecta
#'
#' Population type: captive
#'
#' Geographical location: University of Lausanne, Laussane, Switzerland
#'
#' Data collection technique: video
#'
#' Interaction type: physical contact
#'
#' Definition of interaction: A pair of ants was considered to interact when the front end of one ant was located within the trapezoidal shape representing the other ant.
#'
#' Edge weight type: frequency
#'
#' Total duration of data collection: 1day
#'
#' Time resolution of data collection (within a day): 0.5 sec
#'
#' Time span of data collection (within a day): 24 hours
#'
#' Note: Networks represent six separate colonies of the ant. The authors recorded the position and orientation of all individuals twice per second to reconstruct spatial movement and infer all social interactions occurring over the 41 days of the experiment.
#'
#' @references Mersch, Danielle P., Alessandro Crespi, and Laurent Keller. "Tracking individuals shows spatial fidelity is a key regulator of ant social organization."Science 340.6136 (2013): 1090-1093.
#'
#' @format list of igraph objects
#'
#' @source https: //bansallab.github.io/asnr/
#'
"animal_12"
#' Ants Trophallaxis (weighted)
#'
#' @description
#'
#' Species: *Camponotus pennsylvanicus*
#'
#' Taxonomic class: Insecta
#'
#' Population type: captive
#'
#' Geographical location: Old Main, State College, Pennsylvania
#'
#' Data collection technique: video
#'
#' Interaction type: trophallaxis
#'
#' Definition of interaction: "
#'
#' A trophallaxis event was recorded when ants engaged in mandible-to-mandible contact for greater than 1 s"
#'
#' Edge weight type: duration
#'
#' Total duration of data collection: 1 day
#'
#' Time resolution of data collection (within a day): 1sec
#'
#' Time span of data collection (within a day): 20min
#'
#' Note: Networks represent two C. pennsylvanicus colonies. Each colony was filmed for approximately 30 minutes for 8 consecutive nights.
#'
#' @references Quevillon, Lauren E., et al. "Social, spatial, and temporal organization in a complex insect society." Scientific reports 5 (2015).
#'
#' @format list of igraph objects
#'
#' @source https: //bansallab.github.io/asnr/
#'
"animal_13"
#' Beetle Proximity (weighted)
#'
#' @description
#'
#' Species: *Bolitotherus cornutus*
#'
#' Taxonomic class: Insecta
#'
#' Population type: captive
#'
#' Geographical location: Virginia, USA
#'
#' Data collection technique: survey scan
#'
#' Interaction type: spatial proximity
#'
#' Definition of interaction: Social partners were defined as any beetle within 3 cm (i.e., approximately 2 body lengths) of the focal beetle.
#'
#' Edge weight type: simple_ratio_index
#'
#' Total duration of data collection: 12 days
#'
#' Time resolution of data collection (within a day): 3.5 hours
#'
#' Time span of data collection (within a day): few minutes
#'
#' Note: Networks represent four control (C) and four treatment (T) groups recorded during �undisturbed� phase where individuals were allowed to interact with each other freely.
#'
#' @references Formica, Vincent, et al. "Consistency of animal social networks after disturbance." Behavioral Ecology (2016): arw128.
#'
#' @format list of igraph objects
#'
#' @source https: //bansallab.github.io/asnr/
#'
"animal_14"
#' Asianelephants Dominance (unweighted)
#'
#' @description
#'
#' Species: *Elephas maximus*
#'
#' Taxonomic class: Mammalia
#'
#' Population type: free-ranging
#'
#' Geographical location: Uda Walawe National Park, Sri Lanka
#'
#' Data collection technique: focal sampling
#'
#' Interaction type: dominance
#'
#' Definition of interaction: Indicators of dominance as well as subordination was included. If a series of interactions occurred during a particular event, the winners/losers were determined only on conclusion of the event, when individuals or groups moved apart.
#'
#' Edge weight type: unweighted
#'
#' Total duration of data collection: 206days
#'
#' Time resolution of data collection (within a day): 1sec
#'
#' Time span of data collection (within a day): 5.5 hours
#'
#' Note:
#'
#' @references de Silva, Shermin, Volker Schmid, and George Wittemyer. "Fission–fusion processes weaken dominance networks of female Asian elephants in a productive habitat." Behavioral Ecology (2016): arw153.
#'
#' @format list of igraph objects
#'
#' @source https: //bansallab.github.io/asnr/
#'
"animal_15"
#' Baboon Association (weighted)
#'
#' @description
#'
#' Species: *Papio cynocephalus*
#'
#' Taxonomic class: Mammalia
#'
#' Population type: free-ranging
#'
#' Geographical location: Amboseli National Park, Kenya
#'
#' Data collection technique: focal sampling
#'
#' Interaction type: spatial proximity
#'
#' Definition of interaction: These networks were constructed based on nearest neighbour data collected during focal sampling.
#'
#' Edge weight type: frequency
#'
#' Total duration of data collection: 30days
#'
#' Time resolution of data collection (within a day):
#'
#' Time span of data collection (within a day): focal follow/ad libitum
#'
#' Note: Networks represent grooming interaction or association between five social groups of baboons. Each network summarizes data collected within 30 days before and 90 days after each knockout. A natural knockout was considered to have occurred when a given alpha or beta male was present in the group for at least three months prior to his disappearance, and then he disappeared permanently from the group.
#'
#' @references Franz, Mathias, Jeanne Altmann, and Susan C. Alberts. "Knockouts of high-ranking males have limited impact on baboon social networks." Current zoology 61.1 (2015): 107-113.
#'
#' @format list of igraph objects
#'
#' @source https: //bansallab.github.io/asnr/
#'
"animal_16"
#' Bats Foodsharing (weighted)
#'
#' @description
#'
#' Species: *Desmodus rotundus*
#'
#' Taxonomic class: Mammalia
#'
#' Population type: captive
#'
#' Geographical location: Bloomfield Hills, Michigan, USA
#'
#' Data collection technique: video
#'
#' Interaction type: trophallaxis
#'
#' Definition of interaction: Food sharing event were defined as periods where food could be passed that lasted at least 5 s and were separated by more than 5s.
#'
#' Edge weight type: duration
#'
#' Total duration of data collection: 2 hours
#'
#' Time resolution of data collection (within a day): 1 sec
#'
#' Time span of data collection (within a day): 2 hours
#'
#' Note:
#'
#' @references Carter, Gerald G., and Gerald S. Wilkinson. "Food sharing in vampire bats: reciprocal help predicts donations more than relatedness or harassment."Proceedings of the Royal Society of London B: Biological Sciences 280.1753 (2013): 20122573.
#'
#' @format list of igraph objects
#'
#' @source https: //bansallab.github.io/asnr/
#'
"animal_17"
#' Bats Roostuse (weighted)
#'
#' @description
#'
#' Species: *Myotis sodalis*
#'
#' Taxonomic class: Mammalia
#'
#' Population type: free-ranging
#'
#' Geographical location: Pickaway County, Ohio, USA
#'
#' Data collection technique: radio tags
#'
#' Interaction type: social projection bipartite
#'
#' Definition of interaction: Roost network was first contructed as a two-mode network that consisted of bats and roosts. Single-mode projection of the bat nodes was used to assess colony social structure.
#'
#' Edge weight type: unweighted
#'
#' Total duration of data collection: 1 year
#'
#' Time resolution of data collection (within a day):
#'
#' Time span of data collection (within a day): 8 hours
#'
#' Note:
#'
#' @references Silvis, Alexander, et al. "Roosting and foraging social structure of the endangered Indiana bat (Myotis sodalis)." PloS one 9.5 (2014): e96937.
#'
#' @format list of igraph objects
#'
#' @source https: //bansallab.github.io/asnr/
#'
"animal_18"
#' Bison Dominance (weighted)
#'
#' @description
#'
#' Species: *Bison bison*
#'
#' Taxonomic class: Mammalia
#'
#' Population type: semi-ranging
#'
#' Geographical location: Moiese, Montana, USA
#'
#' Data collection technique: survey scan
#'
#' Interaction type: dominance
#'
#' Definition of interaction: Only those aggressive interactions were analyzed that led to an outcome in which the lost and the winner could be categorized unambiguosly
#'
#' Edge weight type: frequency
#'
#' Total duration of data collection: 2 months
#'
#' Time resolution of data collection (within a day): 1sec
#'
#' Time span of data collection (within a day): focal follow/ad libitum
#'
#' Note:
#'
#' @references Dale F Lott. Dominance relations and breeding rate in mature male American bison. Zeitschrift für Tierpsychologie, 49(4): 418-432, 1979.
#'
#' @format list of igraph objects
#'
#' @source https: //bansallab.github.io/asnr/
#'
"animal_19"
#' Cattle Dominance (weighted)
#'
#' @description
#'
#' Species: *Cattle N/A*
#'
#' Taxonomic class: Mammalia
#'
#' Population type: semi-ranging
#'
#' Geographical location: Jeanerette, Louisiana, USA
#'
#' Data collection technique: survey scan
#'
#' Interaction type: dominance
#'
#' Definition of interaction: The dominance relationship was ascertained from direct physical contests.
#'
#' Edge weight type: frequency
#'
#' Total duration of data collection: Not specified
#'
#' Time resolution of data collection (within a day): 1 sec
#'
#' Time span of data collection (within a day): 1 hour
#'
#' Note:
#'
#' @references Martin W. Schein and Milton H. Fohrman. Social dominance relationships in a herd of dairy cattle. The British J. of Animal Behaviour, 3(2): 45-55, 1955.
#'
#' @format list of igraph objects
#'
#' @source https: //bansallab.github.io/asnr/
#'
"animal_20"
#' Dolphin Association (weighted)
#'
#' @description
#'
#' Species: *Tursiops truncatus*
#'
#' Taxonomic class: Mammalia
#'
#' Population type: free-ranging
#'
#' Geographical location: Cedar Key, Florida, USA
#'
#' Data collection technique: survey scan
#'
#' Interaction type: spatial proximity
#'
#' Definition of interaction: Interactions characterized by prey capture or persistent incidents of prey searching as indicated by long dives or specialized feeding behaviours with direction shifts between surfacings.
#'
#' Edge weight type: frequency
#'
#' Total duration of data collection: 124 days
#'
#' Time resolution of data collection (within a day):
#'
#' Time span of data collection (within a day): 5min
#'
#' Note: Four networks: an overall network that does not take behaviour into account, and the socialize network, the travel network and the forage network that correspond to their respective behaviours.
#'
#' @references Gazda, Stefanie, et al. "The importance of delineating networks by activity type in bottlenose dolphins (Tursiops truncatus) in Cedar Key, Florida." Royal Society open science 2.3 (2015): 140263.
#'
#' @format list of igraph objects
#'
#' @source https: //bansallab.github.io/asnr/
#'
"animal_21"
#' Dolphin Proximity (unweighted)
#'
#' @description
#'
#' Species: *Tursiops truncatus*
#'
#' Taxonomic class: Mammalia
#'
#' Population type: free-ranging
#'
#' Geographical location: Fiordland, New Zealand
#'
#' Data collection technique: survey scan
#'
#' Interaction type: spatial proximity
#'
#' Definition of interaction: All members of a school were assumed associated. Half-weight index (HWI) was used to quantify the frequency of association among individuals.
#'
#' Edge weight type: frequency
#'
#' Total duration of data collection: 594 days
#'
#' Time resolution of data collection (within a day):
#'
#' Time span of data collection (within a day): Not specified
#'
#' Note:
#'
#' @references Lusseau, David, et al. "The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations." Behavioral Ecology and Sociobiology 54.4 (2003): 396-405.
#'
#' @format list of igraph objects
#'
#' @source https: //bansallab.github.io/asnr/
#'
"animal_22"
#' Elephantseal Dominance (weighted)
#'
#' @description
#'
#' Species: *Mirounga angustirostris*
#'
#' Taxonomic class: Mammalia
#'
#' Population type: free-ranging
#'
#' Geographical location: California, USA
#'
#' Data collection technique: survey scan
#'
#' Interaction type: dominance
#'
#' Definition of interaction: Dominance status determined by Elo rating based on winner of an competitive interaction and intensity of interaction
#'
#' Edge weight type: frequency
#'
#' Total duration of data collection: 69 days
#'
#' Time resolution of data collection (within a day):
#'
#' Time span of data collection (within a day): focal follow/ad libitum
#'
#' Note: Network represent interactions collected over different years and/or in different colonies
#'
#' @references Casey, Caroline, et al. "Rival assessment among northern elephant seals: evidence of associative learning during male–male contests." Royal Society open science 2.8 (2015): 150228.
#'
#' @format list of igraph objects
#'
#' @source https: //bansallab.github.io/asnr/
#'
"animal_23"
#' Hyenas Groupmembership (weighted)
#'
#' @description
#'
#' Species: *Crocuta crocuta*
#'
#' Taxonomic class: Mammalia
#'
#' Population type: free-ranging
#'
#' Geographical location: Masai Mara National Reserve, Kenya
#'
#' Data collection technique: survey scan
#'
#' Interaction type: group membership
#'
#' Definition of interaction: Association patterns were recorded based on the co-occurrence of each pair of individuals, during the period for which they were concurrently present in the clan.
#'
#' Edge weight type: twice_weight_index
#'
#' Total duration of data collection: 4 months (approx 16 days each month)
#'
#' Time resolution of data collection (within a day): 15min
#'
#' Time span of data collection (within a day): few hours
#'
#' Note: The three social networks were collected during periods of low (networks A and C) and high (network B) prey abundance.
#'
#' @references Holekamp, Kay E., et al. "Society, demography and genetic structure in the spotted hyena." Molecular Ecology 21.3 (2012): 613-632.
#'
#' @format list of igraph objects
#'
#' @source https: //bansallab.github.io/asnr/
#'
"animal_24"
#' Kangeroos Proximity (weighted)
#'
#' @description
#'
#' Species: *Macropus giganteus*
#'
#' Taxonomic class: Mammalia
#'
#' Population type: free-ranging
#'
#' Geographical location: New South Wales, Australia
#'
#' Data collection technique: survey scan
#'
#' Interaction type: spatial proximity
#'
#' Definition of interaction: Two individuals were assumed to be associating if they occured within 120 cm of another at set 15-min intervals in the enclosure.
#'
#' Edge weight type: frequency
#'
#' Total duration of data collection: Not specified
#'
#' Time resolution of data collection (within a day):
#'
#' Time span of data collection (within a day): focal follow/ad libitum
#'
#' Note:
#'
#' @references TR Grant. Dominance and association among members of a captive and a free-ranging group of grey kangaroos (Macropus giganteus). Animal Behaviour, 21(3): 449-456, 1973.
#'
#' @format list of igraph objects
#'
#' @source https: //bansallab.github.io/asnr/
#'
"animal_25"
#' Possum Burrowsharing (weighted)
#'
#' @description
#'
#' Species: *Trichosurus cunninghami*
#'
#' Taxonomic class: Mammalia
#'
#' Population type: free-ranging
#'
#' Geographical location: Cambarville, Victoria, Australia
#'
#' Data collection technique: logger
#'
#' Interaction type: social projection bipartite
#'
#' Definition of interaction: The proximity loggers recorded the identity of interacting individuals (based on a threshold proximity set to detect den-sharing events) and the time and length of those interactions. From these data, den-sharing was recorded as a binary variable with �1� representing an instance of day-time den-sharing and �0� representing the use of separate dens for every pairwise combination of individuals on each of the 223 days of data collection.
#'
#' Edge weight type: frequency
#'
#' Total duration of data collection: 3 months
#'
#' Time resolution of data collection (within a day): 1 sec
#'
#' Time span of data collection (within a day): 24 hours
#'
#' Note:
#'
#' @references Banks, Sam C., et al. "Adaptive responses and disruptive effects: how major wildfire influences kinship‐based social interactions in a forest marsupial." Molecular ecology 21.3 (2012): 673-684.
#'
#' @format list of igraph objects
#'
#' @source https: //bansallab.github.io/asnr/
#'
"animal_26"
#' Primates Association (weighted)
#'
#' @description
#'
#' Species: *Macaca fuscata*
#'
#' Taxonomic class: Mammalia
#'
#' Population type: free-ranging
#'
#' Geographical location: Yakushima, Japan
#'
#' Data collection technique: focal sampling
#'
#' Interaction type: grooming
#'
#' Definition of interaction: Based on observation of grooming interaction
#'
#' Edge weight type: frequency
#'
#' Total duration of data collection: 3 months
#'
#' Time resolution of data collection (within a day):
#'
#' Time span of data collection (within a day): focal follow/ad libitum
#'
#' Note: Networks represent four control (C) and four treatment (T) groups recorded during �undisturbed� phase where individuals were allowed to interact with each other freely.
#'
#' @references Griffin, Randi H., and Charles L. Nunn. "Community structure and the spread of infectious disease in primate social networks." Evolutionary Ecology 26.4 (2012): 779-800.
#'
#' @format list of igraph objects
#'
#' @source https: //bansallab.github.io/asnr/
#'
"animal_27"
#' Raccoon Proximity (weighted)
#'
#' @description
#'
#' Species: *Procyon lotor*
#'
#' Taxonomic class: Mammalia
#'
#' Population type: free-ranging
#'
#' Geographical location: Illinois, USA
#'
#' Data collection technique: logger
#'
#' Interaction type: spatial proximity
#'
#' Definition of interaction: Close proximity (within 1�1.5m). Any contacts <1 s in duration were excluded.
#'
#' Edge weight type: duration
#'
#' Total duration of data collection: 7days
#'
#' Time resolution of data collection (within a day): 1 sec
#'
#' Time span of data collection (within a day): 24 hours
#'
#' Note: Networks represents adjacency matrices constructed for each week of the year
#'
#' @references Reynolds, Jennifer JH, et al. "Raccoon contact networks predict seasonal susceptibility to rabies outbreaks and limitations of vaccination." Journal of Animal Ecology 84.6 (2015): 1720-1731.
#'
#' @format list of igraph objects
#'
#' @source https: //bansallab.github.io/asnr/
#'
"animal_28"
#' Rhesusmacaque Association (weighted)
#'
#' @description
#'
#' Species: *Macaca mulatta*
#'
#' Taxonomic class: Mammalia
#'
#' Population type: captive
#'
#' Geographical location: Rijswijk, the Netherlands
#'
#' Data collection technique: survey scan
#'
#' Interaction type: physical contact
#'
#' Definition of interaction: Edge defined based on individuals sitting in contact with eah other.
#'
#' Edge weight type: frequency
#'
#' Total duration of data collection: 3years
#'
#' Time resolution of data collection (within a day): 1 hour
#'
#' Time span of data collection (within a day): few minutes
#'
#' Note: Two networks represent proximity (contact sits) and grooming patterns - who was sitting in contact with whom and who was grooming whom.
#'
#' @references Massen, Jorg JM, and Elisabeth HM Sterck. "Stability and durability of intra-and intersex social bonds of captive rhesus macaques (Macaca mulatta)." International Journal of Primatology 34.4 (2013): 770-791.
#'
#' @format list of igraph objects
#'
#' @source https: //bansallab.github.io/asnr/
#'
"animal_29"
#' Rhesusmacaque Dominance (weighted)
#'
#' @description
#'
#' Species: *Macaca fuscata*
#'
#' Taxonomic class: Mammalia
#'
#' Population type: semi-ranging
#'
#' Geographical location: Arashiyama, Japan
#'
#' Data collection technique: survey scan
#'
#' Interaction type: dominance
#'
#' Definition of interaction: The dominance relations between females were determined based on approach-retreat episodes around the food. The dominance range order was arranged based on these dyadic relations.
#'
#' Edge weight type: frequency
#'
#' Total duration of data collection: 6 months
#'
#' Time resolution of data collection (within a day): 2 hours
#'
#' Time span of data collection (within a day): focal follow/ad libitum
#'
#' Note:
#'
#' @references Takahata, Yukio. "Diachronic changes in the dominance relations of adult female Japanese monkeys of the Arashiyama B group." The monkeys of Arashiyama. State University of New York Press, Albany (1991): 123-139.
#'
#' @format list of igraph objects
#'
#' @source https: //bansallab.github.io/asnr/
#'
"animal_30"
#' Sheep Dominance (weighted)
#'
#' @description
#'
#' Species: *Ovis canadensis*
#'
#' Taxonomic class: Mammalia
#'
#' Population type: semi-ranging
#'
#' Geographical location: Montana, USA
#'
#' Data collection technique: focal sampling
#'
#' Interaction type: dominance
#'
#' Definition of interaction: Social status was determined by assembling a win-loss matrix based on the outcome of agonistic interactions. The winner of a dominance fight, involving a series of butts and clashes, was recorded as winning one interaction.
#'
#' Edge weight type: frequency
#'
#' Total duration of data collection: 15 months
#'
#' Time resolution of data collection (within a day):
#'
#' Time span of data collection (within a day): focal follow/ad libitum
#'
#' Note:
#'
#' @references Christine C Hass. Social status in female bighorn sheep (Ovis canadensis): Expression, development and reproductive correlates. J. of Zoology, 225(3): 509-523, 1991.
#'
#' @format list of igraph objects
#'
#' @source https: //bansallab.github.io/asnr/
#'
"animal_31"
#' Spidermonkeys Contact (weighted)
#'
#' @description
#'
#' Species: *Ateles hybridus*
#'
#' Taxonomic class: Mammalia
#'
#' Population type: free-ranging
#'
#' Geographical location: Colombia
#'
#' Data collection technique: focal sampling
#'
#' Interaction type: physical contact
#'
#' Definition of interaction: Physical contact event between the two individuals (e.g. grooming, mating and embracing)
#'
#' Edge weight type: duration
#'
#' Total duration of data collection: 2 years
#'
#' Time resolution of data collection (within a day): 15min
#'
#' Time span of data collection (within a day): focal follow/ad libitum
#'
#' Note:
#'
#' @references Rimbach, Rebecca, et al. "Brown spider monkeys (Ateles hybridus): a model for differentiating the role of social networks and physical contact on parasite transmission dynamics." Phil. Trans. R. Soc. B 370.1669 (2015): 20140110.
#'
#' @format list of igraph objects
#'
#' @source https: //bansallab.github.io/asnr/
#'
"animal_32"
#' Voles Social Projection Bipartite (weighted)
#'
#' @description
#'
#' Species: *Microtus agrestis*
#'
#' Taxonomic class: Mammalia
#'
#' Population type: free-ranging
#'
#' Geographical location: Northumberland, England
#'
#' Data collection technique: mark recapture
#'
#' Interaction type: social projection bipartite
#'
#' Definition of interaction: An edge was inserted into the network whenever two voles were caught in at least one common trap over the primary trapping sessions being considered
#'
#' Edge weight type: frequency
#'
#' Total duration of data collection: 6 days
#'
#' Time resolution of data collection (within a day): 12 hours
#'
#' Time span of data collection (within a day): 24 hours
#'
#' Note: Networks represent social data combined over two consecutive trapping sessions at four sites (BHP, KCS, PLJ and ROB). Populations were trapped in �primary� sessions every 28 days from March to November, and every 56 days from November to March.
#'
#' @references Davis, Stephen, et al. "Spatial analyses of wildlife contact networks." Journal of the Royal Society Interface 12.102 (2015): 20141004.
#'
#' @format list of igraph objects
#'
#' @source https: //bansallab.github.io/asnr/
#'
"animal_33"
#' Zebra Groupmembership (weighted)
#'
#' @description
#'
#' Species: *Equus grevyi*
#'
#' Taxonomic class: Mammalia
#'
#' Population type: free-ranging
#'
#' Geographical location: Kenya
#'
#' Data collection technique: survey scan
#'
#' Interaction type: group membership
#'
#' Definition of interaction: A group was defined as a set of one or more individuals that is spatially cohesive and distinct from other groups at the time of observation. Edges were constructed based on half-weight index (HWI).
#'
#' Edge weight type: unweighted
#'
#' Total duration of data collection: 3 months
#'
#' Time resolution of data collection (within a day):
#'
#' Time span of data collection (within a day): focal follow/ad libitum
#'
#' Note:
#'
#' @references Siva R Sundaresan, Ilya R Fischhoff, Jonathan Dushoff, and Daniel I Rubenstein. Network metrics reveal differences in social organization between two fission-fusion species, Grevy's zebra and onager. Oecologia, 151(1): 140-149, 2007.
#'
#' @format list of igraph objects
#'
#' @source https: //bansallab.github.io/asnr/
#'
"animal_34"
#' Desert Tortoise Asynchronous Burrow Sharing (unweighted)
#'
#' @description
#'
#' Species: *Gopherus agassizii*
#'
#' Taxonomic class: Reptilia
#'
#' Population type: free-ranging
#'
#' Geographical location: Nevada, USA
#'
#' Data collection technique: radio tags
#'
#' Interaction type: social projection bipartite
#'
#' Definition of interaction: A bipartite network was first constructed based on burrow use - an edge connecting a tortoise node to a burrow node indicated burrow use by the individual. Social networks of desert tortoises were then constructed by the bipartite network into a single-mode projection of tortoise nodes.
#'
#' Edge weight type: unweighted
#'
#' Total duration of data collection: 8 months
#'
#' Time resolution of data collection (within a day):
#'
#' Time span of data collection (within a day): focal follow/ad libitum
#'
#' Note: Networks represent social data collected over different years and inactive (November�February)/active (March�October) season.
#'
#' @references Sah, Pratha, et al. "Inferring social structure and its drivers from refuge use in the desert tortoise, a relatively solitary species." Behavioral Ecology and Sociobiology 70.8 (2016): 1277-1289.
#'
#' @format list of igraph objects
#'
#' @source https: //bansallab.github.io/asnr/
#'
"animal_35"
#' Lizard Proximity (weighted)
#'
#' @description
#'
#' Species: *Tiliqua rugosa*
#'
#' Taxonomic class: Reptilia
#'
#' Population type: free-ranging
#'
#' Geographical location: Kungara, South Australia
#'
#' Data collection technique: logger
#'
#' Interaction type: spatial proximity
#'
#' Definition of interaction: Two lizards were assumed to had made a social contact if they were within 2 m of each other at any of the synchronized 10 min GPS locations.
#'
#' Edge weight type: simple_ratio_index
#'
#' Total duration of data collection: 120 days
#'
#' Time resolution of data collection (within a day): 10 minutes
#'
#' Time span of data collection (within a day): 24 hours
#'
#' Note:
#'
#' @references Bull, C. M., S. S. Godfrey, and D. M. Gordon. "Social networks and the spread of Salmonella in a sleepy lizard population." Molecular Ecology 21.17 (2012): 4386-4392.
#'
#' @format list of igraph objects
#'
#' @source https: //bansallab.github.io/asnr/
#'
"animal_36"
================================================
FILE: R/data-covert.R
================================================
#'17 November Greece Bombing
#'@description The dataset refers to the 17 November Revolutionary Organisation, a Marxist urban guerrilla organization operating in Greece. The data refers to the specific temporal window which runs from 1975 to 2002. During these years the group has been responsible for several violent acts such as assassinations, kidnappings and symbolic attacks on government offices.
#' The following has been reconstructed:
#' \preformatted{
#'1) 2-mode matrix, binary, 15x12 persons by events. Ties are participation in terrorist events
#'2) 1-mode stacked matrices 18x18 persons by persons, binary
#' Kinship
#' 1975-1984
#' 1985-1994
#' 1995-2002
#'The original file presents a distinction among several types of relationships:
#' 1. Acquaintances/Distant family ties (interactions limited to radical organisation activities),
#' 2. Friends/Moderately close family ties (interactions extend beyond radical organisations to include such categories as co-workers and roommates). Operational/Organisational leadership (i.e. JI leadership, formally or informally ranking members of burgeoning cells).Operational Ties (i.e. worked closely on a bombing together).
#' 3. Close Friends/Family, Tight-knit operational cliques (would die for each other)
#'If one of these three types of relationships was present, it has been coded with 1.
#'}
#'@source Reconstructed at Manchester (https://sites.google.com/site/ucinetsoftware/datasets/covert-networks). Freely available from http://doitapps.jjay.cuny.edu/jjatt/data.php
#' @format list of igraph objects
"covert_1"
#'9/11 Hijackers
#'@description Famous dataset of the terrorists involved in the 9/11 bombing of the World Trade Centres in 2011. Data was extracted from news reports and ties range from ‘at school with’ to ‘on same plane’.
#' 1-mode matrix 19 x 19 person by person of trusted prior contacts and 1-mode matrix 61 x 61 of other associates.
#'Ties are undirected and binary. Relations are a mix of prior-contacts like trained together, lived together, financial transactions, at school with, on same flight.
#'@source Reconstructed at Manchester (https://sites.google.com/site/ucinetsoftware/datasets/covert-networks).
#'
#'http://orgnet.com/tnet.html
#'
#'http://firstmonday.org/ojs/index.php/fm/article/view/941/863#fig4
#'@references Krebs, Valdis E. "Mapping networks of terrorist cells." *Connections* 24.3 (2002): 43-52.
#' @format igraph object
"covert_2"
#'Al Qaeda 1993-2003
#'@description The Al Qaeda Operations Attack Series is data that pools the relations of individuals associated with over 10 attacks and individually depicts them for each event. It is not a time series but instead an aggregate attack series best perceived as the variously composed attack teams deployed by Al Qaeda over a decade.
#' 1-mode stacked matrices 272 x 272 person by person, each matrix represents an attack. Undirected ties. Relations are co-participation in an attack.
#'1-mode stacked matrices 272 x 272 person by person. Undirected ties. Relations are kinship relations. Matrices coded as follows:
#'0 = No Kinship // 1 = In-laws // 2 = Cousins // 3 = Sibling // 4 = Parent/Child // 5 = Married
#'@source Available from Manchester (https://sites.google.com/site/ucinetsoftware/datasets/covert-networks) or http://doitapps.jjay.cuny.edu/jjatt/data.php
#' @format list of igraph objects
"covert_3"
#'Australian Embassy Bombing, Indonesia 2004
#'@description This is a time series that treat specific attacks as endpoints and depict the evolution of relations between individuals indirectly and directly associated with the Australian Embassy bombing. http://en.wikipedia.org/wiki/2004_Australian_Embassy_bombing_in_Jakarta
#' 1-mode stacked matrices 27 x 27 person by person. Data for 11 time periods plus kinship data. Undirected, valued ties.
#' \preformatted{
#'Tie value codes for kinship matrix:
#'0 = No Kinship // 1 = In-laws // 2 = Cousins // 3 = Sibling // 4 = Parent/Child // 5 = Married // 6 = Grandparent/Child // 7 = Significant Other
#'Tie value codes for time series matrices:
#'0 = No relation // 1 = Acquaintances/distant family ties (interaction limited to radical organisation activities) // 2 = Friends/Moderately close family (inc co-workers/ roommates) Operational/Org leadership/Operational lies (e.g. worked closely on a bombing together) // 3 = Close friends/family, tight-knit operational cliques
#'}
#'@source Available from Manchester (https://sites.google.com/site/ucinetsoftware/datasets/covert-networks) or http://doitapps.jjay.cuny.edu/jjatt/data.php
#' @format igraph object
"covert_4"
#'Bali Bombing 2002/Jemaah Islamiyah
#'@description This is a time series that treat specific attacks as endpoints and depict the evolution of relations between individuals indirectly and directly associated with the 2002 Bali bombing by Jemaah Islamayah. http://en.wikipedia.org/wiki/2002_Bali_bombings
#' 1-mode stacked matrices 27 x 27 person by person, data for 11 time periods. Ties are undirected and valued. Tie codes: 0 = No relation // 1 = Acquaintances/distant family ties (interaction limited to radical organisation activities) // 2 = Friends/Moderately close family (inc co-workers/ roommates) Operational/Org leadership/Operational lies (e.g. worked closely on a bombing together) // 3 = Close friends/family, tight-knit operational cliques
#'@source Available from Manchester (https://sites.google.com/site/ucinetsoftware/datasets/covert-networks) or here http://doitapps.jjay.cuny.edu/jjatt/data.php
#' @format list of igraph objects
"covert_6"
#'Bali Bombing 2005
#'@description This is a time series that treat specific attacks as endpoints and depict the evolution of relations between individuals indirectly and directly associated with the 2005 Bali bombing by Jemaah Islamayah. http://en.wikipedia.org/wiki/2005_Bali_bombings
#' 1-mode matrix 27 x 27 person by person, data for 11 time periods. Ties are undirected and valued. Tie codes: 0 = No relation // 1 = Acquaintances/distant
#'family ties (interaction limited to radical organisation activities) // 2
#' = Friends/Moderately close family (inc co-workers/ roommates)
#'Operational/Org leadership/Operational lies (e.g. worked closely on a
#'bombing together) // 3 = Close friends/family, tight-knit operational
#'cliques.
#'@source Available from Manchester (https://sites.google.com/site/ucinetsoftware/datasets/covert-networks) or here http://doitapps.jjay.cuny.edu/jjatt/data.php
#' @format list of igraph objects
"covert_7"
#'Baseball Steroid Use
#'@description “When the Mitchell Report on steroid use in Major League Baseball (MLB), was published people were surprised at both the number and names of those who were mentioned. The diagram below shows a network map created from data found in the Mitchell Report. Baseball players are shown as green nodes. Those who were found to be providers of steroids and other illegal performance enhancing substances appear as red nodes. The links reveal the flow of chemicals -- from providers to players.” http://orgnet.com/steroids.html
#' 2-mode matrix 72 x 12 of users by providers, relations are the supply of illegal performance enhancing substances.
#'1-mode matrix 72x72 of player by player, relations are having suppliers in common.
#'@source Freely available http://orgnet.com/steroids.html and reconstructed by Manchester (https://sites.google.com/site/ucinetsoftware/datasets/covert-networks)
#' @format list of igraph objects
"covert_8"
#'Big Allied and Dangerous (BAAD)
#'@description The Big Allied And Dangerous (BAAD) project focuses on creation and maintenance of a comprehensive database of terrorist organizational characteristics. This project has created two datasets. The first, BAAD Version 1.0 (BAAD1) contains a single snapshot of 395 terrorist organizations active (meaning they perpetrated at least one attack) between 1998-2005. This dataset grew from the information originally hosted by the Memorial Institute for the Prevention of Terrorism’s (MIPT) in their Terrorism Knowledge Base (TKB). BAAD1 improved and extended the data available from MIPT through independent verification and coding efforts. The dataset includes both case-by-variables information on each organization and network data. The case-by-variables dataset is available for download currently. The network data will be available for download during the second quarter of 2010. Big Allied and Dangerous, Version 2.0 (BAAD2) seeks to improve upon BAAD1 in multiple ways by (1) providing time series data in yearly slices, (2) expanding the time period forward through 2007, and (3) increasing the number and depth of variables collected and coded. BAAD2 is made up of two major components. The first is the data on organizational variables. These variables include: group name, aliases, homebase, ideology, size, age, structure, financial support, electoral involvement, leadership loss, territorial control, CBRN pursuit or use, and number of incidents, injuries, and fatalities. The second component is the social network data, which characterizes relations between terrorist organizations as well as between countries and terrorist organizations. Relationships are coded for categories such as: suspected ally, ally, faction, splinter group, rival, enemy, target, and state sponsor. This data can then be used to create dynamic network visualizations to show the networks evolving over the 10 years included in the dataset. Data construction for BAAD2 is currently ongoing.
#'NETWORK 2-mode matrix 394 x 65 organization by territory, undirected ties. Ties are location of attacks.
#'1-mode matrix 394 x 394 organization by organization (co-location of attacks), undirected ties.
#'Attribute data for each organization. Attribute codebook available at: http://www.albany.edu/pvc/lethality_paper__CodeBook.pdf
#'@source Freely available from http://www.albany.edu/pvc/data.shtml and from Manchester (https://sites.google.com/site/ucinetsoftware/datasets/covert-networks)
#'@references Asal, Victor H. and R. Karl Rethemeyer. (2008). “The Nature of the Beast: Terrorist Organizational Characteristics and Organizational Lethality.” *Journal of Politics*, 70(2): 437-449.
#' @format list of igraph objects
"covert_9"
#'Caviar
#'@description Project Caviar was a unique investigation that targeted a network of hashish and cocaine importers operating out of Montreal. The network was targeted between 1994 and 1996 by a tandem investigation uniting the Montreal Police, the Royal Canadian Mounted Police, and other national and regional law-enforcement agencies from various countries (i.e., England, Spain, Italy, Brazil, Paraguay, and Colombia). The case is unique because it involved a specific investigative approach that will be referred to as a “seize and wait” strategy. Unlike most law-enforcement strategies, the mandate set forward in the Project Caviar case was to seize identified drug consignments, but not to arrest any of the identified participants. This took place over a 2-year period. Thus, although 11 importation consignments were seized at different moments throughout this period, arrests only took place at the end of the investigation. What this case offers is a rare opportunity to study the evolution of a criminal network phenomenon as it was being disrupted by law-enforcement agents. The inherent investigative strategy permits an assessment of change in the network structure and an inside look into how network participants react and adapt to the growing constraints set upon them.
#'The principal data source was comprised of information submitted as evidence during the trials of 22 participants in the Caviar network. It included 4,279 paragraphs of information (over 1,000 pages) revealing electronically intercepted telephone conversations between network participants. These transcripts were used to create the overall matrix of the drug-trafficking operation’s communication system throughout the course of the investigation. Individuals falling in the surveillance net were not all participants in the trafficking operation. An initial extraction of all names appearing in the surveillance data led to the identification of 318 individuals. From this pool, 208 individuals were not implicated in the trafficking operations. Most were simply named during the many transcripts of conversations, but never detected. Others who were detected had no clear participatory role within the network (e.g., family members or legitimate entrepreneurs). The final network was thus composed of 110 participants.
#'NETWORK 11 1-mode matrices person by person, representing the 11 phases of the investigation. Ties are directed and valued. Number of nodes = 1) 15x15, 2) 24x24, 3) 33x33, 4) 33x33, 5) 32x32, 6) 27x27, 7) 34x34, 8) 42x42, 9) 34x34, 10) 42x42, 11) 41x41
#'1-mode matrix 110 x 110 person by person of the complete network.
#'Ties are communication exchanges between criminals. Values represent level of communication activity. Data comes from police wiretapping.
#'@source Available from Manchester (https://sites.google.com/site/ucinetsoftware/datasets/covert-networks), reconstructed from Morselli’s book, Inside Criminal Networks http://www.springer.com/social+sciences/criminology/book/978-0-387-09525-7 Book pages from 173 to 186, Appendix
#'@references Morselli, C., 2009. Inside criminal networks. New York: Springer.
#' @format list of igraph objects
"covert_10"
#'Christmas Eve Bombings Indonesia 2000
#'@description This is a time series that treat specific attacks as endpoints and depict the evolution of relations between individuals indirectly and directly associated with the 2000 Christmas Eve bombing. http://en.wikipedia.org/wiki/Christmas_Eve_2000_Indonesia_bombings
#' 1-mode stacked matrices 27 x 27 person by person. Data for 11 time periods plus kinship data. Undirected, valued ties.
#'Tie value codes for kinship matrix:
#'0 = No Kinship // 1 = In-laws // 2 = Cousins // 3 = Sibling // 4 =
#'Parent/Child // 5 = Married // 6 = Grandparent/Child // 7 = Significant
#'Other
#'Tie value codes for time series matrices:
#'0 = No
#'relation // 1 = Acquaintances/distant family ties (interaction limited
#'to radical organisation activities) // 2 = Friends/Moderately close
#'family (inc co-workers/ roommates) Operational/Org
#'leadership/Operational lies (e.g. worked closely on a bombing together)
#'// 3 = Close friends/family, tight-knit operational cliques
#'@source Available from Manchester (https://sites.google.com/site/ucinetsoftware/datasets/covert-networks) or here http://doitapps.jjay.cuny.edu/jjatt/data.php
#' @format list of igraph objects
"covert_11"
#'CielNet
#'@description Project Ciel is based on a small drug-importation network that was importing liquid hashish from Jamaica to Montreal. This network was targeted by the Royal Canadian Mounted Police and the Montreal Police from May 1996 to June 1997. Typical of many Canadian investigations of drug smuggling and trafficking, the operations in Project Ciel were described as taking place within a tightly governed organizational framework—a hierarchy, in short. Reports from the investigation maintained that the main target of the investigation was the “organizational leader.” Other key targets included the leader’s “lieutenant” and a series of other subordinates. The investigation produced three separate seizures, with two taking place at Mirabel airport near Montreal and another occurring at Sangster airport in Jamaica. Overall, 75 people fell into the surveillance net. A selection process that was aimed at identifying only those individuals who were active in the drug-importation operations resulted in a final network of 25 participants.
#'NETWORK 1-mode matrix 25 x 25 person by person. Ties are communication exchanges between criminals. Ties are directed and valued. Higher values represent more active communication channels. Data comes from police wiretapping.
#'@source Available from Manchester (https://sites.google.com/site/ucinetsoftware/datasets/covert-networks). Reconstructed from Morselli’s book, Inside Criminal Networks http://www.springer.com/social+sciences/criminology/book/978-0-387-09525-7 book page 172, Appendix.
#'@references Morselli, C., 2009. Inside criminal networks. New York: Springer.
#' @format igraph object
"covert_12"
#'Cocaine Dealing Natarajan
#'@description This dataset comes from an investigation into to a large cocaine trafficking organization in New York City.
#' 1-mode matrix 28 x 28 persons by persons.
#'Directed valued relations are communications exchanges / flows of information. Data come from police wiretappings (transcripts of 151 telephone conversations).
#'@source Available from Manchester (https://sites.google.com/site/ucinetsoftware/datasets/covert-networks). Reconstructed from Natarajan, M. "Understanding the structure of a drug trafficking organization: a conversational analysis." Crime Prevention Studies 11 (2000): 273-298.
#'@references Natarajan, M. "Understanding the structure of a drug trafficking organization: a conversational analysis." Crime Prevention Studies 11 (2000): 273-298.
#' @format igraph object
"covert_13"
#'Cocaine Smuggling
#'@description Data refers to four groups involved in cocaine trafficking in Spain. Information comes from police wiretapping and meetings registered by police investigations of these criminal organisations between 2007 and 2009.
#'Operation MAMBO (N=22). The investigation started in 2006 and involved Colombian citizens that were introducing 50 kg of cocaine to be adulterated and distributed in Madrid (Spain). Ultimately, the group was involved in smuggling cocaine from Colombia through Brazil and Uruguay to be distributed in Spain. This is a typical Spanish middle cocaine group acting as wholesale supplier between a South American importer group and retailers in Madrid.
#'Operation JUANES (N=51). In 2009, the police investigation detected a group involved in the smuggling of cocaine from Mexico to be distributed in Madrid (Spain). In this case, the group operated in close cooperation with another organization that was laundering the illegal income from drug distribution from this and other groups. The cocaine traffickers earned an estimated EUR 60 million.
#'Operation JAKE (N=62). In 2008, the group investigated was operating as a wholesale supplier and retail distributor of cocaine and heroin in a large distribution zone located in Madrid (Spain), where gypsy clans traditionally carry out similar activities. The group was in charge of acquiring, manipulating and selling the drugs in the gypsy quarter.
#'Operation ACERO (N=11). This investigation started in 2007 and involved a smaller family-based group. The group was composed mainly of members of a same family and was led by a female. They distributed cocaine in Madrid (Spain) that was provided to them by other groups based in a northwest region of the country, one of the most active areas in the provision of cocaine from the countries of origin. The group also had their own procedures to launder money.
#' 4 1-mode matrices person by person from each of the operations described above. Undirected, valued ties.
#'Mambo: 31x31
#'Juanes:51x51
#'Jake: 38x38
#'Acero: 25x25
#'Relations are communications between individuals. Meaning of tie values is unclear - may represent level of communications activity.
#'@source Available at Manchester (https://sites.google.com/site/ucinetsoftware/datasets/covert-networks). Reconstructed from Jimenez-Salinas Framis, A. "Illegal networks or criminal
#'organizations: Power, roles and facilitators in four cocaine trafficking
#' structures." In Third Annual Illicit Networks Workshop, Montreal. 2011
#'@references Jimenez-Salinas Framis, A. "Illegal networks or criminal organizations: Power, roles and facilitators in four cocaine trafficking structures." In *Third Annual Illicit Networks Workshop*, Montreal. 2011.
#' @format list of igraph objects
"covert_14"
#'Czech Corruption
#'@description The data comes from a Czech media database called Newton Media Search and involves all major Czech newspapers for the period from 4th June 2013 to 4th June 2014.
#'Actors are:
#'Jana Nagyová, Petr Nečas – former prime minister and his office chief and love affair.
#'Ivan Fuksa, Petr Tluchoř, Marek Šnajdr – deputies of ODS (conservative governing party at that time)
#'Ondrej Páleník, Roman Boček, Jan Pohůnek, Milan Kovanda, Lubomír Poul, Libor Grygárek – high government officials and espionage agents
#'Ivo Rittig, Roman Janoušek, Václav Ryba, Tomáš Hrdlička, Jiří Toman – eminences gris, "godfathers"
#'DATA FORMATS: UCINET, .csv
#' 1-mode matrix 16 x 16 person by person.
#'The ties are co-appearances – every time an actor was mentioned in one article together with any other actor, it is considered to be a tie.
#'Ties are valued on am 11 point scale, where 10 is the strongest tie (Nagyova – Necas).
#'All other ties were transformed by dividing the total number of co-appearances between the two actors by the value of the strongest tie, which gave the percent of the maximal tie. This percentage was then assigned an integer value from range 0 - 9 according to which tenth of percents this particular value falls into.
#'Example: The Fuksa - Nagyova tie reaching 50% of the strongest tie value was assigned a value of 5. The Nagyova - Ryba tie reaching 3% of the max value was assigned zero etc.
#'@source Data from Tomas Diviák, Available from Manchester (https://sites.google.com/site/ucinetsoftware/datasets/covert-networks).
#' @format igraph object
"covert_15"
#'Domestic Terrorist Web Links
#'@description Network of hyperlinks between domestic
#'terrorist group websites in the United States.
#'DATA FORMATS: UCINET, .csv
#' 1-mode matrix 32 x 32 website by website
#'Directed binary ties are based on analysis of hyperlinks between sites.
#'@source Available from Manchester (https://sites.google.com/site/ucinetsoftware/datasets/covert-networks)
#'@references Zhou et al. (2005), ‘US domestic extremist groups
#'on the web: link and content analysis’, available at http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=1511999&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D1511999
#' @format igraph object
"covert_16"
#'Drugnet
#'@description This is a dichotomous adjacency matrix of drug users in Hartford. Ties are directed and represent acquaintanceship. The network is a result of two years of ethnographic observations of people's drug habits.
#'NETWORK 1-mode matrix 293x293 person by person, directed ties. Relations are acquaintanceship.
#'Attribute dataset includes ethnicity, gender.
#'Ethnicity codes: 2 = African American; 3 = Puerto Rican/Latino; 1, 5, 6, 7 = white or
#' other
#'Gender codes: 1 = male; 2 = female; 0 = unknown
#'@references WEEKS, M. R., CLAIR, S., BORGATTI, S. P., RADDA, K. & SCHENSUL, J. J. 2002. Social networks of drug users in high-risk sites: Finding the connections. AIDS and Behaviour, 6, 193-206.
#'@source https://sites.google.com/site/sfeverton18/research/cohesion-and-clustering
#' @format igraph object
"covert_17"
#'FIFA
#'@description Two Networks of Standing Committee membership. These are overt networks with covert elements.
#' 2-Mode persons to Standing Committees (converted to 1-Mode)
#'340 x 340 persons by persons undirected binary
#'450 x 450 persons by persons undirected binary
#'@source Available from Manchester (https://sites.google.com/site/ucinetsoftware/datasets/covert-networks). Reconstructed by Gemma Edwards from Select Committee 2006 activity report.
#' @format list of igraph objects
"covert_18"
#'Global Suicide Attacks
#'@description Data is on militant organizations between 1985 and 2006. Each node signifies a militant organization or other type of entity that conducts suicide attacks.
#' 1-mode matrix for each year, organization by organization. Undirected, binary ties represent a known physical relationship between agents from different but “connected” organizations.
#' \preformatted{
#'1985 10 x 10
#'1986 4 x 4
#'1990 4 x 4
#'1993 4 x 4
#'1994 4 x 4
#'1995 9 x 9
#'1996 6 x6
#'1997 4 x 4
#'1998 10 x 10
#'1999 7 x 7
#'2000 10 x 10
#'2001 15 x 15
#'2002 11 x 11
#'2003 21 x 21
#'2004 25 x 25
#'2005 27 x 27
#'2006 31 x 31
#'}
#'@source Available from Manchester (https://sites.google.com/site/ucinetsoftware/datasets/covert-networks). Reconstructed from Benjamin Acosta & Steven J. Childs (2013) ‘Illuminating the Global Suicide-Attack Network’, Studies in Conflict & Terrorism, 36:1, 49-76
#'@references Benjamin Acosta & Steven J. Childs (2013) ‘Illuminating the Global Suicide-Attack Network’, *Studies in Conflict & Terrorism*, 36:1, 49-76
#' @format list of igraph objects
"covert_19"
#'Hamburg Cell 9/11 2001
#'@description This is a time series that treat specific attacks as endpoints and depict the evolution of relations between individuals indirectly and directly associated with the sleeper Al Qaeda cell in Hamburg around the time of the 9/11 bombings. http://en.wikipedia.org/wiki/Hamburg_cell
#' 1-mode stacked matrices 35 x 35 person by person, data for 11 time points. Ties are undirected and valued. Tie codes: 0 = No relation // 1 = Acquaintances/distant
#'family ties (interaction limited to radical organisation activities) // 2
#' = Friends/Moderately close family (inc co-workers/ roommates)
#'Operational/Org leadership/Operational lies (e.g. worked closely on a
#'bombing together) // 3 = Close friends/family, tight-knit operational
#'cliques.
#'@source Available from Manchester (https://sites.google.com/site/ucinetsoftware/datasets/covert-networks) or here http://doitapps.jjay.cuny.edu/jjatt/data.php
#' @format list of igraph objects
"covert_20"
#'Heroin Dealing Natarajan
#'@description This dataset comes from an investigation into a large heroin trafficking organization in New York City in the 1990s.
#' 1-mode matrix 38x38 person by person.
#'Directed binary relations are communications exchanges / flows of information. Data come from police wiretappings (transcripts of 151 telephone conversations).
#'@source Available from Manchester (https://sites.google.com/site/ucinetsoftware/datasets/covert-networks). Reconstructed from Natarajan, M. (2006). Understanding the Structure of a Large Heroin Distribution Network: A Quantitative Analysis of Qualitative Data. Quantitative Journal of Criminology, 22(2), 171-192.
#'@references Available from Manchester (https://sites.google.com/site/ucinetsoftware/datasets/covert-networks). Reconstructed from Natarajan, M. (2006). Understanding the Structure of a Large Heroin Distribution Network: A Quantitative Analysis of Qualitative Data. Quantitative Journal of Criminology, 22(2), 171-192.
#' @format igraph object
"covert_21"
#'Islamic State Allegiances
#'@description 2-mode dataset describing groups allied to
#'Islamic State and the countries in which they are operating
#' 2-mode matrix 47 x 20 organizations by state, undirected binary ties.
#'@source Available from Manchester (https://sites.google.com/site/ucinetsoftware/datasets/covert-networks)
#' @format igraph object
"covert_22"
#'Islamic State Group
#'@description The data describes relationships between members of the Islamic State supplied to the BBC by IS investigation team.
#' 1-mode matrices 56 x 56 persons by persons undirected binary ties.
#'The data includes three matrices with three types of relationships:
#'Links (relationship definition unknown)
#'Components & friends (former companion, close, friends, close coordination, most important companion, close relationship)
#'Kinship (brothers, brothers working together, young brother, brother, married to sister, son)
#'@source Available from Manchester (https://sites.google.com/site/ucinetsoftware/datasets/covert-networks). Reconstructed from http://www.bbc.co.uk/news/world-middle-east-29052475
#' @format list of igraph objects
"covert_23"
#'Italian Gangs
#'@description Describes Italian gang members and their nationalities. No further contextual data available.
#' 1-Mode matrix 67x67 person by person, relations are co-membership of gangs.
#'Attribute data is gang member’s country of origin, coded numerically. No codebook available.
#'@source Available from Manchester (https://sites.google.com/site/ucinetsoftware/datasets/covert-networks)
#' @format igraph object
"covert_24"
#'Jakarta Bombing 2009 / Noordin Top
#'@description This is a time series that treat specific attacks as endpoints and depict the evolution of relations between individuals indirectly and directly associated with the 2009 Jakarta bombing. http://en.wikipedia.org/wiki/2009_Jakarta_bombings
#'This network draws on the same terrorist activities as the Noordin Top network.
#' 1-mode stacked matrices, 28 x 28 person by person, data for kinship, pre-attack and post-attack. Ties undirected and valued.
#'Codebook available here http://doitapps.jjay.cuny.edu/jjatt/files/Relations_Codebook_Public_Version2.pdf
#'@source Available from Manchester (https://sites.google.com/site/ucinetsoftware/datasets/covert-networks) or here http://doitapps.jjay.cuny.edu/jjatt/data.php
#' @format list of igraph objects
"covert_25"
#'Jemaah Islamiyah Koschade
#'@description Jemaah Islamiyah cell that was responsible for the Bali bombings in 2002 – i.e. should tally with the other Bali bombing dataset from JJATT. The recording of the interaction of the cell began following the meeting in the Hotel Harem in Denpasar on October 6, when the group was considered to go ‘operationally covert’, and concluded when the majority of the group had left Bali before the implementation of the operation on October 11, 2002.
#' 1-mode matrix 17 x 17 person by person, undirected and valued.
#'Relationships between terrorists concern who exchanged information with whom (communications exchanges).
#'The valued relations represent the strength of the relations between the individuals, with a score of one signifying the weakest relationship such as a single text message or a financial transaction, and five signifying the strongest relationship such as individuals who resided together, or individuals who had numerous weak contacts over the period in question.
#'@source Dataset comes from the publication Koschade, Stuart (2006) A Social Network Analysis of Jemaah Islamiyah: The Applications to Counter-Terrorism and Intelligence. Studies in Conflict and Terrorism Vol. 29(6):pp. 559-575
#'This data has been reconstructed by Koschade by using the report provided by the international crisis group which collected depositions of JI suspects, court documents, and others Indonesian press reports. (INTERNATIONAL CRISIS GROUP. 2003 ‘Jemaah Islamiyah in South East Asia: Damaged but Still Dangerous’).
#' @format igraph object
"covert_26"
#'Linux Terrorists
#'@description These are two datasets about a set of terrorists and the attacks they carry out collected by the MIND Lab at University of Maryland (UMD) (http://www.mindswap.org/).
#'NETWORK 1-mode matrix 645 x 645 organization by organization, directed ties. Ties are co-location of terrorist attacks.
#'1-mode matrix 260 x 260 organization by organization, directed ties. Ties are co-located terrorist attacks by same organisation.
#'@source Available from Manchester (https://sites.google.com/site/ucinetsoftware/datasets/covert-networks).
#' @format list of igraph objects
"covert_27"
#'London Gang
#'@description Data is on co-offending in a London-based inner-city street gang, 2005-2009, operating from a social housing estate. Data comes from anonymised police arrest and conviction data for ‘all confirmed’ members of the gang.
#' 1-Mode matrix 54 x 54 persons by persons, undirected, valued.
#' \preformatted{
#'Network tie values:
#' = 1 (hang out together)
#' = 2 (co-offend together)
#' = 3 (co-offend together, serious crime)
#' = 4 (co-offend together, serious crime, kin)
#'}
#'Attributes: Age, Birthplace (1 = West Africa, 2= Caribbean, 3= UK, 4= East Africa), Residence, Arrests, Convictions, Prison, Music.
#'@references Grund, T. and Densley, J. (2015) Ethnic Homophily and Triad Closure: Mapping Internal Gang Structure Using Exponential Random Graph Models. Journal of Contemporary Criminal Justice, Vol. 31, Issue 3, pp. 354-370
#'Grund, T. and Densley, J. (2012) Ethnic Heterogeneity in the Activity and Structure of a Black Street Gang. European Journal of Criminology, Vol. 9, Issue 3, pp. 388-406.
#'@source Available from Manchester (https://sites.google.com/site/ucinetsoftware/datasets/covert-networks).
#' @format igraph object
"covert_28"
#'Madoff Fraud
#'@description Bernie Madoff was involved in a massive financial fraud in the USA and was sentenced to 150 years in prison (http://en.wikipedia.org/wiki/Bernard_Madoff). The network is finance flows between financial institutions and Madoff’s firm. All data for this network was gathered from news stories and court documents found on major media web sites. Read more about the social network underpinnings of this scheme in The Network Thinkers blog post (http://www.thenetworkthinkers.com/2009/02/madoff-feeder-funds.html)
#' 1-mode directed network 61 x 61 firm by firm, showing money flows between banks and other organizations, leading ultimately to Madoff’s firm.
#'Relations are money flows. Arrows show direction of money flow.
#'@source Freely available, to be reconstructed by Manchester http://orgnet.com/Madoff9.png
#' @format igraph object
"covert_29"
#'Madrid Train Bombing 2004
#'@description This is a time series that treat specific attacks as endpoints and depict the evolution of relations between individuals indirectly and directly associated with the Madrid train bombing. http://en.wikipedia.org/wiki/2004_Madrid_train_bombings
#' 1-mode stacked matrices 55 x 55 person by person, data on 20 time periods plus kinship data and tie extinguished data.
#'Codebook available here http://doitapps.jjay.cuny.edu/jjatt/files/Relations_Codebook_Public_Version2.pdf
#'@source Available from Manchester (https://sites.google.com/site/ucinetsoftware/datasets/covert-networks) or here http://doitapps.jjay.cuny.edu/jjatt/data.php
#' @format list of igraph objects
"covert_30"
#'Mali Terrorist Network
#'@description Data refers to a terrorist network operating in the Sahel Sahel-Sahara region and describes relationships between Islamists and Tuareg rebels during the Malian conflict.
#'Data comes from a selection of newspaper articles published between 2010 and 2012.
#'“Using social network analysis, our first aim is to illuminate the relationships between the Islamists and the rebels involved in the current Malian conflict. We use a selection of newspaper articles to demonstrate that the connection between Islamists and rebels depends on brokers who passed from the Tuareg rebellion to radical groups. Our second objective is to detail the internal relationships within each of the subgroups. Our findings show how Islamists were affected by the accidental disappearance of one the AQMI regional emirs and how the death of one of the architects of the Tuareg rebellion affected rebel cohesion.” Walther et al., INSNA Sunbelt Conference 2013
#' 1-mode matrix 36x36 person by person
#'Relations derived from interactions, including participation in political or military event, political meetings; trainings in Afghanistan, Iraq, or Libya; combats; negotiations for hostage releases; or involvements with a killing, an abduction, or a bombing.
#'@source The dataset has been reconstructed from the following publication: Walther, Olivier J., and Dimitris Christopoulos (2015), "Islamic terrorism and the Malian rebellion." Terrorism and Political Violence, 27 (3), 497-519.
#'@references Walther, Olivier J., and Dimitris Christopoulos (2015), "Islamic terrorism and the Malian rebellion."
#' *Terrorism and Political Violence*, 27 (3), 497-519.
#' @format igraph object
"covert_31"
#'Montreal Street Gangs
#'@description Data obtained from the Montreal Police’s central intelligence base, the Automated Criminal Intelligence Information System (ACIIS), was used to reconstruct the organization of drug-distribution operations in Montreal North. These operations were targeted during three separate investigations between 2004 and 2007 by the Montreal Police, who believed that the criminal activities were under the control of one of the more reputed gangs in Montreal—the Bo-Gars (or Handsome Boys, in English). Because the trials extending from two of the investigations were still ongoing at the time of analysis, their names remain confidential and I simply refer to Investigations A, B, and C. Investigation A began in February 2004 and ended in April 2005, with the arrests of 27 individuals who were accused primarily of importing and distributing crack and cocaine in a Montreal North neighborhood. Investigation A was the largest of the three investigations under study and it was the only case to offer electronic surveillance information amongst the available data sources. Investigations B and C, which were direct extensions of observations made during Investigation A, both began during the fall of 2006 and ended in June 2007, with the arrests of 24 individuals who were targeted in Investigation B and 11 individuals targeted in Investigation C. Investigation B concentrated on street dealers of marijuana and crack, while Investigation C focused specifically on the activities of a group of wholesalers who were supplying some of the dealers targeted in Investigation B. Overall, 101 individuals were monitored during the investigations—45 in Investigation A, 30 in Investigation B, and 26 in Investigation C. This list of 101 individuals was used as a starting point to reconstruct the final network. This final network was comprised of 70 participants and was based on information obtained from three data sources.
#'NETWORK 1-mode matrix 35 x 35 organization by organization (Gangs in Montreal). Undirected ties, binary (original network is directed).
#'\preformatted{
#'Ties are relationships between gangs (as reported in focus groups/interviews with gang members).
#'Attribute data:
#'-- Gang affiliation: 1) Bloods, 2) Crips, or 3) Other
#'-- Gang Ethnicity: 1) Hispanic, 2) Afro-Canadian, 3) Caucasian, 4) Asian, 5) No main association/mixed.
#'-- Territory data: 1) Downtown 2) East 3) West
#'-- Missing data coded as 99
#'}
#'@source The data has been reconstructed from the following article: Karine Descormiers and Carlo Morselli (2011) 'Alliances, Conflicts, and Contradictions in Montreal's Street Gang Landscape' International Criminal Justice Review, Vol. 1 No. 3, pp. 297-314
#'@references Karine Descormiers and Carlo Morselli (2011) 'Alliances, Conflicts, and Contradictions in Montreal's Street Gang Landscape' International Criminal Justice Review, Vol. 1 No. 3, pp. 297-314
#' @format igraph object
"covert_32"
#'Ndrangheta Mafia 2
#'@description Data is on attendance of suspected members of the Ndrangheta criminal organization at summits (meetings whose purpose is to make important decisions and/or affiliations, but also to solve internal problems and to establish roles and powers) taking place between 2007 and 2009.
#' 2-mode matrix 156 x 47 persons by events (summits), undirected binary ties.
#'Attendance at events have been registered by police authorities through wiretapping and observations during the large investigation called "Operazione Infinito".
#'@source The data has been reconstructed by the document "ORDINANZA DI APPLICAZIONE DI MISURA COERCITIVA con mandato di cattura - art. 292 c.p.p. -" which is available online at the following address http://www.stampoantimafioso.it/documentazione-antimafia/ordinanze/.
#'Stampo Antimafioso is a project which aims to share information about the Mafia operating in Northem Italy.
#'The dataset has been reconstructed by mostly referring to pp.87-110 of the document named "Operazione Infinito". This report is a judicial document concerning the pre-trial detention order triggered by the the preliminary investigation judge (Giudice per le indagini preliminari) of Milan. With this judicial act, measures of custody and pretrial detention have been ordered for the reported suspected of 'Ndrangheta affiliation.
#' @format igraph object
"covert_33"
#'Noordin Top
#'@description These
#'data were drawn primarily from "Terrorism in Indonesia: Noordin's
#'Networks," a publication of the International Crisis Group (2006) and
#'include relational data on the 79 individuals listed in Appendix C of that
#'publication. The data were initially coded by Naval Postgraduate School
#'students as part of the course “Tracking and Disrupting Dark Networks” under
#'the direction of Professor Sean Everton, Co-Director of the CORE Lab, and Professor Nancy Roberts. CORE Lab Research
#'Assistant Daniel Cunningham reviewed and cleaned all coding made by students.
#'NETWORK 1-mode stacked matrix 79 x 79 person by person. Ties are undirected.
#'Ties include classmates; friendship; soulmates; co-location of logistical activity; co-attendance at meetings; co-participation in operations; co-attendance at training events; communications; business & financial ties.
#'Codebook available here http://www.thearda.com/archive/files/codebooks/origCB/Noordin%20Subset%20Codebook.pdf
#'@source Data is available in its original format from Manchester (https://sites.google.com/site/ucinetsoftware/datasets/covert-networks).
#'@references Roberts, Nancy and Sean F. Everton. 2011. Roberts and Everton Terrorist Data: Noordin Top Terrorist Network (Subset).
#'
#'For a detailed analysis of Noordin top network see: Everton, S. F. (2012) Disrupting dark networks: Cambridge University Press. For a detailed explanation of matrices and the kind of relationship considered see the appendix of the book.
#' @format list of igraph objects
"covert_34"
#'Paul Revere
#'@description The Paul Revere conspiracy dataset concerns relationships between 254 people and their affiliations with seven different organizations in Boston. The dataset refers to Paul Revere, who was responsible for organizing a local militia of Boston's revolutionary movement (see http://en.wikipedia.org/wiki/Sons_of_Liberty). The dataset was analysed by Kieran Healy of Duke University.
#'This dataset has been reconstructed by looking at the information presented in the appendix of the book ‘Paul Revere's Ride’ published by David Fischer (1994).
#' 2-mode affiliation matrix 254x7 people by organizations, relations refer to membership of organizations; 1-mode matrix 254 x 254 people by people, relations are shared membership of organizations, relations are valued with values indicating number of memberships in common.
#'@source Freely available: http://kieranhealy.org/blog/archives/2013/06/09/using-metadata-to-find-paul-revere/
#'@references Fischer, D. 1994. Paul Revere's ride. Oxford University Press.
#' @format list of igraph objects
"covert_35"
#'Philippine Kidnappings
#'@description Data refers to the Abu Sayyaf Group (ASG), a violent non-state actor operating in the Southern Philippines. In particular, this data is related to the Salast movement that has been founded by Aburajak Janjalani, a native terrorist of the Southern Philippines in 1991. ASG is active in kidnapping and other kinds of terrorist attacks (Gerdes et al. 2014).
#'The reconstructed 2-mode matrix combines terrorist kidnappers and the terrorist events they have attended.
#' 2-model matrix 246x105 persons by terrorist events, undirected binary relations are participation in events
#'@source Available from Manchester (https://sites.google.com/site/ucinetsoftware/datasets/covert-networks). http://www.tandfonline.com/eprint/cCV3RJihmG3miPFECpV7/full
#'@references Gerdes, Luke M., Kristine Ringler, and Barbara Autin. "Assessing the Abu Sayyaf Group's Strategic and Learning Capacities." *Studies in Conflict & Terrorism* 37, no. 3 (2014): 267-293.
#' @format igraph object
"covert_36"
#'Philippines Bombing
#'@description This is a time series that treat specific attacks as endpoints and depict the evolution of relations between individuals indirectly and directly associated with the Philippines bombing http://en.wikipedia.org/wiki/Rizal_Day_bombings
#' 1-mode stacked matrices 16 x 16 person by person nodes, data on 11 time periods plus kinship data and tie extinguished data.
#'Codebook available here http://doitapps.jjay.cuny.edu/jjatt/files/Relations_Codebook_Public_Version2.pdf
#'@source Available from Manchester (https://sites.google.com/site/ucinetsoftware/datasets/covert-networks) or here http://doitapps.jjay.cuny.edu/jjatt/data.php
#' @format list of igraph objects
"covert_37"
#'Provisional Irish Republican Army
#'@description Data is on active Provisional IRA (hereafter PIRA) members between 1970 and 1998. Data collected at the International Center for the Study of Terrorism, Pennsylvania State University. From this data are derived network structures and the nature of dependence within them. The PIRA network comprises the following four types of relationships: (1) involvement in a PIRA activity together, (2) friends before joining PIRA movement, (3) blood relatives, and (4) related through marriage. We treated each relation as a tie and coded whether a tie exists between two members or not. Thus, the networks have, conceptually and technically, binary and symmetric relations between members.
#'Data also includes sociological information of members, such as gender, age, marital status, recruiting age, education (that is, attending university), brigade memberships, non-/violent characteristics, role-related characteristics—senior leader, IED constructor, IED planter, and gunman—and task-related characteristics (that is, foreign operation tasks, and involvement n bank robbery, kidnapping, hijacking, and drugs).
#'@source Available from Manchester (https://sites.google.com/site/ucinetsoftware/datasets/covert-networks).
#'@references Paul Gill , Jeongyoon Lee , Karl R. Rethemeyer , John Horgan & Victor Asal (2014) Lethal Connections: The Determinants of Network Connections in the Provisional Irish Republican Army, 1970–1998, International Interactions: Empirical and Theoretical Research in International Relations, 40:1, 52-78
#' @format list of igraph objects
"covert_38"
#'Rhodes Bombing
#'@description Data is a social network of the (believed defunct) Greek terrorist group November17 (N17) that was derived from open source reporting (Irwin et al, 2002; Abram and Smith, 2004).
#' 1-mode matrix 22x22 persons by persons.
#'Relations indicate that open source reporting has demonstrated some connection between the two individuals at some point in the past.
#'Attribute data includes…
#'Role 1= Leader (gives orders), 2 = operational (receives orders)
#'Faction 1 = 1st Generation Leadership Faction, 2 = Koufontinas Faction, 3 = Sardanopoulos Faction
#'Resources 1= controls one resource, 2= controls two resources, 3= controls three resources (resources are money, weapons, safe houses)
#'Some attribute data is missing
#'@source Available from Manchester (https://sites.google.com/site/ucinetsoftware/datasets/covert-networks). Reconstructed from Rhodes, C.J. and P. Jones, “Inferring Missing Links in Partially Observed Social Networks”, Journal of the Operational Research Society (2009) 60, 1373-1383
#'For more details of attribute data see Rhodes CJ, Keefe EMJ (2007). Social network topology: A Bayesian approach. J Opl Res Soc 58(12): 1605–1611.
#'@references Rhodes, C.J. and P. Jones, “Inferring Missing Links in Partially Observed Social Networks”, *Journal of the Operational Research Society* (2009) 60, 1373-1383
#' @format igraph object
"covert_39"
#'Saxena Terror India
#'@description Data is organisation-to-organisation links of
#'terrorist organisations operating in the Indian State of Jammu & Kashmir.
#' Four 1-mode matrices persons by persons for years 2000
#'(5 x 5), 2001 (25 x25), 2002 (23 x 23), 2003 (18 x 18).
#'Undirected, binary ties are "co-occurrence"
#'mentions of terrorist organisations together in various sources e.g. on-line
#'@source Available from Manchester (https://sites.google.com/site/ucinetsoftware/datasets/covert-networks)
#'@references Sudhir Saxena, K. Santhanam, Aparna Basu (2004),
#''Application of social network analysis (SNA) to terrorist networks in Jammu & Kashmir’, *Strategic Analysis* 28(1)
#' @format list of igraph objects
"covert_40"
#'Siren
#'@description Project Siren began in February 1998 when a port worker informed members of the CERVO group that a container of stolen vehicles had been recently shipped to Ghana. This shipment was subsequently seized at its transit point in Anvers, Belgium. This initial tip and action led to a close monitoring of the suspects involved in the shipment. The investigation continued for 4 months (to June 1998), during which time CERVO members monitored stolen-vehicle shipments intended for Russia, Egypt, Iraq, Italy, and Switzerland. Some vehicles were also resold in Toronto. Overall, 35 cars were retrieved according to the files that were consulted.
#'NETWORK 1-mode matrix 44 x 44 person by person. Ties are undirected.
#'Relations represent communication exchanges between criminals. Data comes from police wiretapping.
#'@source Available from Manchester (https://sites.google.com/site/ucinetsoftware/datasets/covert-networks), reconstructed from Morselli’s book, Inside Criminal Networks http://www.springer.com/social+sciences/criminology/book/978-0-387-09525-7 book page 187, Appendix
#'@references Morselli, C., 2009. Inside criminal networks. New York: Springer.
#' @format igraph object
"covert_41"
#'Slumlords
#'@description “A client of orgnet -- a small, not-for-profit, economic justice organization (EJO) -- used social network analysis to assist their city attorney in convicting a group of "slumlords" of various housing violations that the real estate investors had been side-stepping for years. The housing violations, in multiple buildings, included raw sewage leaks, multiple tenant children with high lead levels, eviction of complaining tenants, utility liens of six figures.
#' Set of matrices of ties between real estate agents, businesses, persons, and properties, corresponding to the step-by-step analysis described here http://www.orgnet.com/slumlords.html
#'\preformatted{
#'1-mode network 5x5 real estate transactions
#'2-mode network 11x5 owners by properties
#'1-mode network 11x11 person by person, relations are common ownership of properties
#'1-mode network 11x11 person by person, relations are family ties
#'1-mode network 13x13 person by person, relations are family ties
#'2-mode network 13x9 person by business and properties, relations are business affiliations/ownership
#'1 mode network 9x9 business/property by business/property, relations are having affiliated persons/owners in common
#'}
#'@source Available at http://www.orgnet.com/slumlords.html and reconstructed at Manchester (https://sites.google.com/site/ucinetsoftware/datasets/covert-networks).
#' @format list of igraph objects
"covert_42"
#'Southeast Asian Aggregated Attacks 2005
#'@description The Southeast Asian
#'Aggregate Attack Series collapses all of the individual, Indonesian cases into
#'a single series of relations useful for inspecting a series of behavioural and
#'compositional changes in one terrorist network.
#' 1-mode stacked matrices 109x109 person by person. Kinship, friendship, acquaintanceship, time series, tie formed, tie ended.
#'Codebook available here http://doitapps.jjay.cuny.edu/jjatt/files/Relations_Codebook_Public_Version2.pdf
#'@source Available from Manchester (https://sites.google.com/site/ucinetsoftware/datasets/covert-networks) or here http://doitapps.jjay.cuny.edu/jjatt/data.php
#' @format list of igraph objects
"covert_43"
#'Suffragettes
#'@description Dataset collected by Gemma Edwards on the Suffragette movement in the UK. “…Relational data were constructed from historical archives, including suffragette letters and speeches, and secondary sources like published auto-biographies and newspaper accounts. This historical material provided not only relational data for quantitative network analysis about the structure of these networks, but rich, narrative accounts about the meaning of ties over time and the perception of the network from those within it. Using historical letters as a source of data on suffragette networks was seen as particularly useful for example, as letters contained relational data in terms of ‘who was writing to whom’, and writers would further ‘talk their ties’ within the course of letter writing. Also, letters tend to be dated, allowing for an analysis of the evolution of ties over time (Edwards and Crossley 2009).
#'\preformatted{
#'1-mode network 85x85 persons by persons, relations are co-location (1908 visits)
#'1-mode network 85x85 persons by persons, relations are co-location (1909 visits)
#'1-mode network 85x85 persons by persons, relations are co-location (1910 visits)
#'1-mode network 85x85 persons by persons, relations are co-location (1911 visits)
#'1-mode network 85x85 persons by persons, relations are co-location (1912 visits)
#'1-mode network 85x85 persons by persons, relations are co-location (1913 visits)
#'1-mode network 85x85 persons by persons, relations are co-location (Blathwayt visits)
#'1-mode network 112x112 persons by persons, relations are pre-existing ties (Pankhurst Inner Circle)
#'2-mode network 13x18 persons by militant acts (Bristol Bath militant acts)
#'1-mode network 49x49 persons by persons, relations are co-attendence at events (Bristol Bath events)
#'2-mode network 1216x398 person by arrest date and location (50 all 2M)
#'}
#'@source Available from Manchester (https://sites.google.com/site/ucinetsoftware/datasets/covert-networks). http://eprints.ncrm.ac.uk/842/1/Social_Network_analysis_Edwards.pdf
#'@references
#'Edwards, Gemma, and Nick Crossley. "Measures and meanings: exploring the ego-net of Helen Kirkpatrick Watts, militant suffragette." *Methodological Innovations Online* 4.1 (2009): 37-61.
#'
#'Edwards, Gemma. "Infectious innovations? The diffusion of tactical innovation in social movement networks, the case of suffragette militancy." *Social Movement Studies* 13.1 (2014): 48-69.
#'
#'Edwards, Gemma. Social Movements and Protest. Cambridge University Press, 2014. Crossley, Nick, et al. "Covert social movement networks and the secrecy-efficiency trade off: The case of the UK suffragettes (1906–1914)." *Social Networks* 34.4 (2012): 634-644.
#' @format list of igraph objects
"covert_44"
#'Swingers
#'@description Data on couples attending swinging parties.
#' 2-mode matrix 57 x 39 couples by events (parties)
#'"Swing units" are a couple attending events with other "swing units".
#'@source Data from Anne-Marie Niekamp. Available from Manchester (https://sites.google.com/site/ucinetsoftware/datasets/covert-networks).
#' @format igraph object
"covert_45"
#'Kenya Tanzania Gerdes
#'@description Data collected by the Center for Computational Analysis of Social and Organizational Systems, a research group at Carnegie Mellon University, on the participation of 18 Al Qaeda members in 25 functional tasks underlying the 1998 bombings of the U.S. Embassies in Nairobi, Kenya, and Dar es Salaam, Tanzania 2-Mode persons to Standing Committees.
#' 2-mode matrix 18 x 25 persons to tasks, binary undirected. Relations are participation in tasks.
#'@source Available from Center for Computational Analysis of Social and Organizational Systems (CASOS). (2008). Tanzania-Kenya-imoon.xml. Data available online: http://www.casos.cs.cmu.edu/ computational_tools/datasets/internal/tanzania_ kenya/index11.php.
#'Also Available from Manchester (https://sites.google.com/site/ucinetsoftware/datasets/covert-networks).
#'@references Gerdes, Luke M. (2014), ‘Dependency Centrality from Bipartite Social Networks’, *Connections*, 34, 1&2
#' @format igraph object
"covert_46"
#'Togo
#'@description Project Togo began in February 1998 when a Toronto-based ringing operation was dismantled and one of its participants informed the police that he was previously employed by a Montreal businessman who was also active in the resale of stolen vehicles. This initial tip was corroborated soon after by a thief who had been arrested while driving a stolen vehicle. By December 1998, the Togo investigation was under way. It spanned into February 1999 and 20 cars that were destined for France, Ghana, and local buyers in southern Quebec were retrieved.
#'NETWORK 1-mode matrix 33 x 33 person by person. Undirected ties.
#'Ties are communication exchanges between criminals. Data comes from police wiretapping.
#'@source Available from Manchester (https://sites.google.com/site/ucinetsoftware/datasets/covert-networks), reconstructed from Morselli’s book, Inside Criminal Networks http://www.springer.com/social+sciences/criminology/book/978-0-387-09525-7 book page 187, Appendix
#'@references Morselli, C., 2009. Inside criminal networks. New York: Springer.
#' @format igraph object
"covert_47"
================================================
FILE: R/data-freeman.R
================================================
#' Political Blogs
#' @description The data were compiled by Lada Adamic and Natalie Glance. Links between blogs were automatically extracted from a crawl of the front page of the blog. In addition the authors drew on various sources (blog directories, and incoming and outgoing links and posts around the time of the 2004 presidential election) and classified the first 758 blogs as left-leaning and the remaining 732 as right-leaning.
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data.html#blogs
#' @references Lada A. Adamic and Natalie Glance, "The political blogosphere and the 2004 US election", *Proceedings of the WWW-2005 Workshop on the Weblogging Ecosystem* (2005)
"polblogs"
#' Giraffe Affiliation
#' @description The authors studied a herd of six female captive giraffe (Giraffa camelopardalis) for two years. They were concerned with the question of whether giraffe associated randomly or patterned their behavior and proximity in a manner indicative of social relationships. Affiliative interaction, proximity, and nearest neighbors for female giraffe living in a large outdoor enclosure were analyzed, and all three measures were nonrandomly distributed, indicating female giraffe had social preferences. Furthermore, preferences were consistent across measures and time, suggesting that adult female giraffe maintain relationships.
#' @details The three different relations (affil, proximity and neighbor) are given in the relation edge attribute.
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data.html#giraffe
#' @references Brashaw, M. J., M. A. Bloomsmith, T. L. Maple and F. B. Bercovitch. 2007. "The Structure of Social Relationships Among Captive Female Giraffe (Giraffa camelopardalis)." *Journal of Comparative Psychology* 121:46-53.
"giraffe"
#' Bernard/Killworth - Fraternity (interaction)
#' @description These data concern interactions among students living in a fraternity at a West Virginia college. All subjects had been residents in the fraternity from three months to three years. BKFRAB records the number of times a pair of subjects were seen in conversation by an "unobtrusive" observer (who walked through the public areas of the building every fifteen minutes, 21 hours a day, for five days). BKFRAC contains rankings made by the subjects of how frequently they interacted with other subjects in the observation week.
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data.html#bkfrat
#' @seealso [bkfrac]
#' @references
#' Bernard H. R., Killworth P. and Sailer L. (1980). Informant accuracy in social network data IV. *Social Networks*, 2, 191-218.
#'
#' Bernard H. R., Killworth P. and Sailer L. (1982). Informant accuracy in social network data V. *Social Science Research*, 11, 30-66.
#'
#' Romney A. K. and Weller S. (1984). Predicting informant accuracy from patterns of recall among individuals. *Social Networks*, 6, 59-78.
"bkfrab"
#' Bernard/Killworth - Fraternity (rankings)
#' @description These data concern interactions among students living in a fraternity at a West Virginia college. All subjects had been residents in the fraternity from three months to three years. BKFRAB records the number of times a pair of subjects were seen in conversation by an "unobtrusive" observer (who walked through the public areas of the building every fifteen minutes, 21 hours a day, for five days). BKFRAC contains rankings made by the subjects of how frequently they interacted with other subjects in the observation week.
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data.html#bkfrat
#' @seealso [bkfrab]
#' @references
#' #' Bernard H. R., Killworth P. and Sailer L. (1980). Informant accuracy in social network data IV. *Social Networks*, 2, 191-218.
#'
#' Bernard H. R., Killworth P. and Sailer L. (1982). Informant accuracy in social network data V. *Social Science Research*, 11, 30-66.
#'
#' Romney A. K. and Weller S. (1984). Predicting informant accuracy from patterns of recall among individuals. *Social Networks*, 6, 59-78.
"bkfrac"
#' Bernard/Killworth - Office (interaction)
#' @description These data concern interactions in a small business office, recorded by an "unobtrusive" observer. Observations were made as the observer patrolled a fixed route through the office every fifteen minutes during two four-day periods. BKOFFB contains the observed frequency of interactions; BKOFFC contains rankings of interaction frequency as recalled by the employees over the two-week period.
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data.html#bkoff
#' @seealso [bkoffc]
#' @references
#' Bernard H. R., Killworth P. and Sailer L. (1980). Informant accuracy in social network data IV. *Social Networks*, 2, 191-218.
#'
#' Bernard H. R., Killworth P. and Sailer L. (1982). Informant accuracy in social network data V. *Social Science Research*, 11, 30-66.
#'
#' Romney A. K. and Weller S. (1984). Predicting informant accuracy from patterns of recall among individuals. *Social Networks*, 6, 59-78.
"bkoffb"
#' Bernard/Killworth - Office (rankings)
#' @description These data concern interactions in a small business office, recorded by an "unobtrusive" observer. Observations were made as the observer patrolled a fixed route through the office every fifteen minutes during two four-day periods. BKOFFB contains the observed frequency of interactions; BKOFFC contains rankings of interaction frequency as recalled by the employees over the two-week period.
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data.html#bkoff
#' @seealso [bkoffb]
#' @references
#' #' Bernard H. R., Killworth P. and Sailer L. (1980). Informant accuracy in social network data IV. *Social Networks*, 2, 191-218.
#'
#' Bernard H. R., Killworth P. and Sailer L. (1982). Informant accuracy in social network data V. *Social Science Research*, 11, 30-66.
#'
#' Romney A. K. and Weller S. (1984). Predicting informant accuracy from patterns of recall among individuals. *Social Networks*, 6, 59-78.
"bkoffc"
#' Bernard/Killworth - Tech company (interaction)
#' @description These data concern interactions in a technical research group at a West Virginia university. BKTECB contains a frequency record of interactions, made by an observer every half-hour during one five-day work week. BKTECC contains the personal rankings of the remembered frequency of interactions in the same period.
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data.html#bktec
#' @seealso [bktecc]
#' @references
#' Bernard H. R., Killworth P. and Sailer L. (1980). Informant accuracy in social network data IV. *Social Networks*, 2, 191-218.
#'
#' Bernard H. R., Killworth P. and Sailer L. (1982). Informant accuracy in social network data V. *Social Science Research*, 11, 30-66.
#'
#' Romney A. K. and Weller S. (1984). Predicting informant accuracy from patterns of recall among individuals. *Social Networks*, 6, 59-78.
"bktecb"
#' Bernard/Killworth - Tech company (rankings)
#' @description These data concern interactions in a technical research group at a West Virginia university. BKTECB contains a frequency record of interactions, made by an observer every half-hour during one five-day work week. BKTECC contains the personal rankings of the remembered frequency of interactions in the same period.
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data.html#bktec
#' @seealso [bktecb]
#' @references
#' #' Bernard H. R., Killworth P. and Sailer L. (1980). Informant accuracy in social network data IV. *Social Networks*, 2, 191-218.
#'
#' Bernard H. R., Killworth P. and Sailer L. (1982). Informant accuracy in social network data V. *Social Science Research*, 11, 30-66.
#'
#' Romney A. K. and Weller S. (1984). Predicting informant accuracy from patterns of recall among individuals. *Social Networks*, 6, 59-78.
"bktecc"
#' Preschool
#' @description The data were collected in 1926 in a preschool in Toronto. Observations were made on each child in turn who was defined as a "focal" individual. Instances in which the focal child (1) talked to another, (2) interfered with another, (3) watched another, (4) imitated another or (5) cooperated with another were tabulated along with the name of the other to whom the social behavior was directed. The result was tabulated in five matrices.
#' @details The five different relations are given in the relation edge attribute.
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data.html#bott
#' @references
#' Bott, H. "Observations of play activities in a nursery school," *Genetic Psychology Monographs*, 1928, 4: 44-88.
"bott"
#' Pony
#' @description weights are the number of occasions in which the row pony threatened the column pony.
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data.html#pony
#' @references
#' T.H.Cluton-Brock, J.P.Greenwood and R.P.Powell, 1976, "Ranks and Relationships in Highland Ponies and Highland Cows," *Zeitschrift Tierpsychologie*, 41, 202-216.
#'
#' M.W.Schein and M.W.Frohman, 1955, "Social Dominance Relationships in a Herd of Dairy-Cattle," *British Journal of Animal Behaviour*, 3, 45-55 (1955).
"pony"
#' Ant Colony (I)
#' @description These are observations of ritual dominance activities in an ant community (a collection of 16 female Leptothorax allardycei ants over 18.2 hours in a queenright colony)
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data.html#ants
#' @seealso [ants_2]
#' @references
#' B. J. Cole, 1981, "Dominance hierarchies in Leptothorax ants" *Science*, 212: 83-84.
"ants_1"
#' Ant Colony (II)
#' @description These are observations of ritual dominance activities in an ant community (a collection of 13 female Leptothorax allardycei ants in a queenless colony)
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data.html#ants
#' @seealso [ants_1]
#' @references
#' B. J. Cole, 1981, "Dominance hierarchies in Leptothorax ants" *Science*, 212: 83-84.
"ants_2"
#' Friendships among High School Boys
#' @description In the fall of 1957. and the spring of 1958. boys in a small high school in Illinois were asked. "What fellows here in school do you go around with most often?" The data are from research reported by Coleman. The data report the direct choices of each of 73 boys at two times. HS1 was recorded in 1957 and HS2 in 1958.
#' @details the edge attribute time contains the time period.
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data.html#high
#' @references
#' Coleman, J. S. Introduction to Mathermatical Sociology. New York: Free Press, pp.450-451.
"highschool_boys"
#' Innovations among Physicians
#' @description This data set was prepared by Ron Burt. He dug out the 1966 data collected by Coleman, Katz and Menzel on medical innovation. They had collected data from physicians in four towns in Illinois, Peoria, Bloomington, Quincy and Galesburg.
#'
#'They were concerned with the impact of network ties on the physicians' adoprion of a new drug, tetracycline. Three sociometric matrices were generated. One was based on the replies to a question, "When you need information or advice about questions of therapy where do you usually turn?" A second stemmed from the question "And who are the three or four physicians with whom you most often find yourself discussing cases or therapy in the course of an ordinary week -- last week for instance?" And the third was simply "Would you tell me the first names of your three friends whom you see most often socially?"
#'
#' In addition, records of prescriptions were reviewed and a great many other questions were asked. In the ATTRIBUTES data I have included 13 items: city of practice, recorded date of tetracycline adoption date, years in practice, meetings attended, journal subscriptions, free time activities, discussions, club memberships, friends, time in the community, patient load, physical proximity to other physicians and medical specialty.
#' @details
#' The codes are:
#'City: 1 Peoria, 2 Bloomington, 3 Quincy, 4 Galesburg
#'\preformatted{
#'Adoption Date:
#'1 November, 1953
#'2 December, 1953
#'3 January, 1954
#'4 February, 1954
#'5 March, 1954
#'6 April, 1954
#'7 May, 1954
#'8 June, 1954
#'9 July, 1954
#'10 August, 1954
#'11 September, 1954
#'12 October, 1954
#'13 November, 1954
#'14 December, 1954
#'15 December/January, 1954/1955
#'16 January/February, 1955
#'17 February, 1955
#'18 no prescriptions found
#'98 no prescription data obtained
#'
#'Year started in the profession
#'1 1919 or before
#'2 1920-1929
#'3 1930-1934
#'4 1935-1939
#'5 1940-1944
#'6 1945 or later
#'9 no answer
#'
#'Have you attended any national, regional or state conventions of professional societies during the last 12 months? (if yes) Which ones?
#'0 none
#'1 only general meetings
#'2 specialty meetings
#'9 no answer
#'
#'Which medical journals do you receive regularly?
#'1 two
#'2 three
#'3 four
#'4 five
#'5 six
#'6 seven
#'7 eight
#'8 nine or more
#'9 no answer
#'
#'With whom do you actually spend more of your free time -- doctors or non-doctors?
#'1 non-doctors
#'2 about evenly split between them
#'3 doctors
#'9 mssing; no answer, don't know
#'
#'When you are with other doctors socially, do you like to talk about medical matter?
#'1 no
#'2 yes
#'3 don't care
#'9 missing; no answer, don't know
#'
#'Do you belong to any club or hobby composed mostly of doctors?
#'0 no
#'1 yes
#'9 no answer
#'
#'Would you tell me who are your three friends whom you see most often socially? What is (their) occupation?
#'1 none are doctors
#'2 one is a doctor
#'3 two are doctors
#'4 three are doctors
#'9 no answer
#'
#'How long have you been practicing in this community?
#'1 a year or less
#'2 more than a year, up to two years
#'3 more than two years, up to five years
#'4 more than five years, up to ten years
#'5 more than ten years, up to twenty years
#'6 more than twenty years
#'9 no answer
#'
#'About how many office visits would you say you have during the average week at this time of year?
#'1 25 or less
#'2 26-50
#'3 51-75
#'4 76-100
#'5 101-150
#'6 151 or more
#'9 missing; no answer, don't know
#'
#'Are there other physicians in this building? (if yes) Other physicians in same office or with same waiting room?
#'1 none in building
#'2 some in building, but none share his office or waiting room
#'3 some in building sharing his office or waiting room
#'4 some in building perhaps sharing his office or waiting room
#'9 no answer
#'
#'Do you specialize in any particular field of medicine? (if yes) What is it?
#'1 GP, general practitioner
#'2 internist
#'3 pediatrician
#'4 other specialty
#'9 no answer
#'}
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data.html#ckm
#' @references
#' Coleman, J. S. Introduction to Mathermatical Sociology. New York: Free Press, pp.450-451.
"physicians"
#' Dolphins (I)
#' @description Thirteen male dolphins were observed as they swam in a shallow lagoon. Tabulations were made of who was swimming with whom. The table shows the observed frequencies.
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data.html#dolphin
#' @references
#' R. C. Connor, R. A. Smolker and A. F. Richards, 1992, "Dolphin alliances and coalitions," in *Coalitions and Alliances in Humans and Other Animals* (Eds: A. H. Harcourt and F. B. M. deWaal). Oxford: Oxford University Press, 415-444.
"dolphins_1"
#' Dolphins (II)
#' @description undirected social network recording frequent associations between pairs in a community of 62 dolphins living off Doubtful Sound, New Zealand.
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data.html#dolphins
#' @references
#' D. Lusseau, K. Schneider, O. J. Boisseau, P. Haase, E. Slooten, and S. M. Dawson, The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations, *Behavioral Ecology and Sociobiology* 54, 396-405 (2003).
#'
#' D. Lusseau, The emergent properties of a dolphin social network, *Proc. R. Soc.* London B (suppl.) 270, S186-S188 (2003).
#'
#' D. Lusseau, Evidence for social role in a dolphin social network, Preprint q-bio/0607048 (http://arxiv.org/abs/q-bio.PE/0607048)
"dolphins_2"
#' Davis - Southern Women
#' @description These data were collected by Davis et al. in the 1930s. They represent observed attendance at 14 small social events by 18 Southern women.
#' @format (bipartite) igraph object
#' @source http://moreno.ss.uci.edu/data.html#davis
#' @references
#' Breiger R. (1974). The duality of persons and groups. *Social Forces*, 53, 181-190.
#'
#' Davis, A. et al. (1941). Deep South. Chicago: University of Chicago Press.
"southern_women"
#' Windsurfers (Interactions)
#' @description This was a study of windsurfers on a beach in southern California during the fall of 1986. The windsurfing community was fairly clearly divided into at least two sub-communities. Members of each community seemed, to some degree, to limit their interaction to fellow group members. Contacts between members of the two groups occurred, but these were less frequent. Observations of 43 individuals were made for 31 days. All interpersonal contacts among collections of these individuals were recorded.
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data.html#beach
#' @seealso [surfersc]
#' @references
#' L. C. Freeman, S. C. Freeman and A. G. Michaelson "On Human Social Intelligence." *Journal of Social and Biological Structures*, 11, 1988, 415-425.
#'
#' L. C. Freeman, S. C. Freeman and A. G. Michaelson "How Humans See Social Groups: A Test of the Sailer-Gaulin Models." *Journal of Quantitative Anthropology*, 1, 1989, 229-238.
"surfersb"
#' Windsurfers (Closeness)
#' @description This was a study of windsurfers on a beach in southern California during the fall of 1986. The windsurfing community was fairly clearly divided into at least two sub-communities. Members of each community seemed, to some degree, to limit their interaction to fellow group members. Contacts between members of the two groups occurred, but these were less frequent. Observations of 43 individuals were made for 31 days. All interpersonal contacts among collections of these individuals were recorded (see [surfersb]). Then all 43 individuals were interviewed following the end of observation. Data on each individual's perception of social affiliations were collected.
#'The perceptual data were generated by asking each subject to perform a sequence of card sorting tasks that assigned an index of the perceived closeness of every individual on the beach to each of the other individuals.
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data.html#beach
#' @seealso [surfersb]
#' @references
#' L. C. Freeman, S. C. Freeman and A. G. Michaelson "On Human Social Intelligence." *Journal of Social and Biological Structures*, 11, 1988, 415-425.
#'
#' L. C. Freeman, S. C. Freeman and A. G. Michaelson "How Humans See Social Groups: A Test of the Sailer-Gaulin Models." *Journal of Quantitative Anthropology*, 1, 1989, 229-238.
"surfersc"
#' EIES (relations)
#' @description These data arose from an early experiment on computer mediated communication. Fifty academics interested in social network research were allowed to contact each other via an Electronic Information Exchange System (EIES). The data collected consisted of all messages sent plus acquaintance relationships at two time periods (collected via a questionnaire).The data include the 32 actors who completed the study. In addition attribute data on primary discipline and number of citations was recorded.
#'
#' TIME_1 and TIME_2 give the reported acquaintance information at the beginning of the study and eight months later. These are coded as follows: 4 = close personal fiend, 3= friend, 2= person I've met, 1 = person I've heard of but not met, and 0 = person unknown to me (or no reply).
#'
#' The attribute data give the number of citations of the actors work in the social science citation index at the beginning of the study together with a discipline code: 1 = Sociology, 2 = Anthropology, 3 = Mathematics/Statistics, 4 = other. These data are used by Wasserman and Faust in their network analysis book.
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data.html#eies
#' @seealso [eies_messages]
#' @references
#' Freeman, S. C. and L. C. Freeman (1979). The networkers network: A study of the impact of a new communications medium on sociometric structure. *Social Science Research Reports* No 46. Irvine CA, University of California.
#'
#' Wasserman S. and K. Faust (1994). Social Network Analysis: Methods and Applications.Cambridge University Press, Cambridge.
"eies_relations"
#' EIES (Messages)
#' @description These data arose from an early experiment on computer mediated communication. Fifty academics interested in social network research were allowed to contact each other via an Electronic Information Exchange System (EIES). The data collected consisted of all messages sent plus acquaintance relationships at two time periods (collected via a questionnaire).The data include the 32 actors who completed the study. In addition attribute data on primary discipline and number of citations was recorded.
#'
#' NUMBER_OF MESSAGES is the total number of messages person i sent to j over the entire period of the study.
#'
#' The attribute data give the number of citations of the actors work in the social science citation index at the beginning of the study together with a discipline code: 1 = Sociology, 2 = Anthropology, 3 = Mathematics/Statistics, 4 = other. These data are used by Wasserman and Faust in their network analysis book.
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data.html#eies
#' @seealso [eies_relations]
#' @references
#' Freeman, S. C. and L. C. Freeman (1979). The networkers network: A study of the impact of a new communications medium on sociometric structure. *Social Science Research Reports* No 46. Irvine CA, University of California.
#'
#' Wasserman S. and K. Faust (1994). Social Network Analysis: Methods and Applications.Cambridge University Press, Cambridge.
"eies_messages"
#' Ceo's and Clubs
#' @description These data give the affiliation network of 26 CEO's of major corporations and banks and their spouses to 15 clubs, corporate and cultural boards.. Data were collected in the Minneapolis area. Membership was during the period 1978-1981.
#' @format (bipartite) igraph object
#' @source http://moreno.ss.uci.edu/data.html#galas
#' @references
#' Galaskiewicz J (1985). Social Organization of an Urban Grants Economy. New York. Academic Press.
"ceos_clubs"
#' Collaboration in Jazz
#' @description The data here record a network of jazz bands. The data were obtained from The Red Hot Jazz Archive digital database. The data include 198 bands that performed between 1912 and 1940, with most of the bands performing in the 1920's. In this case each vertex corresponds to a band, and a link between two bands is established if they had at least one musician in common.
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data.html#jazz
#' @references
#' PABLO M. GLEISER and LEON DANON "Community structure in jazz." Advances in Complex Systems (ACS) 2003 Vol: 6 Issue: 4 (December 2003) Page: 565 - 573.
"jazz"
#' Kangaroo
#' @description Frequencies of observed physical proximities among a collection of 17 free-ranging grey kangaroos. Observations were made in the Nadgee Nature Reserve in New South Wales. There were 18 kangaroos in the original report, but one (number M11) was never observed and is therefore dropped from this network.
#'
#'Two kinds of dominance ranks are included. One, ss, is the ratio of an animal's number of "successes" to its number of "involvements." The other, ps, is calculated by assigning an animal 2 points for each other animal it bests on more than 50\% of their contacts. One point is given for a tie and none for less than 50\% successes. Since, except for a juvenile male (M2), there were no cross-sex contests, males and females are ranked seperately, but M2 is ranked with the females.
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data.html#kangaroo
#' @references
#' T. R. Grant, "Dominance and association among members of a captive and a free-ranging group of grey kangaroos (Macropus giganthus)," *Animal Behaviour*, 1973, 21: 449-456.
"kangaroo"
#' Hens Pecking order
#' @description Records the "peck order" of a flock of 32 White Leghorn hens studied in 1946. A tie from hen a to b means that hen a can peck hen b. The author claims that temporal changes are rare; once a hen dominates another, that pattern persists.
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data.html#hens
#' @references
#' Guhl, A. M., 1953. Social Behavior of the Domestic Fowl. Manhattan, Kansas: Kansas State College, Agricultural Experiment Station, Technical Bulletin 73.
"hens"
#' Joint Senate Press Releases
#' @description These data are from Justin Grimmer's doctoral dissertation in political science at Harvard. They record instances of joint press releases issued by U. S. Senators.
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data.html#jpr
#' @references
#' http://people.fas.harvard.edu/~jgrimmer/
"jpr"
#' Bighorn Sheep Dominance
#' @description Data record wins and losses for 28 female bighorn sheep observed on the National Bison Range in 1984. the weight of a tie from a to b is the number of occasions on which a was observed dominating b. Ages are listed, but those assigned an age of 9 are at least 9 years old; they may be older.
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data.html#sheep
#' @references
#' Christine Hass, "Social status in female bighorn sheep (Ovis canadensis): expression, development and reproductive correlates." *Journal of the Zoological Society of London*, 1991, 225: 509-523.Station, Technical Bulletin 73.
"sheep"
#' Protein-Protein Interactions (probably yeast)
#' @description
#' One research area in biology in which centralities have been applied is protein-protein interaction. Interactions between proteins are common. They play an important part in every process involving living cells. Knowledge about how they interact can lead to better understanding of a great many diseases and it can help in the design of appropriate therapies.
#'
#' Often studies of protein-protein interaction generate huge data sets. In the letter in Nature that was mentioned above, Jeong, Mason, Barabasi and Oltvai (2001) examined a data matrix that contained interactions linking 2114 proteins contained in yeast. Earlier experimental work had demonstrated that some of the protein molecules in yeast were lethal; if they were removed the yeast would die. Removing others, however, had no such dramatic effect. So Jeong et al. examined the question of whether the structural properties of those proteins, in particular their degree centralities, could predict which proteins were lethal and which ones were not. Their results showed that proteins of high degree were far more likely to be lethal than those of lower degree.
#'
#' Subsequent articles (cited below) questioned these results. The argument was that gaps in the data called the whole analysis into question.
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data.html#pro-pro
#' @references
#'Jeong, H., S. P. Mason, A.-L. Barabasi and Z. N. Oltvai. (2001). "Lethality and centrality in protein networks." *Nature* 411(6833): 41-42.
#'
#'S. Coulomb, M. Bauer, D. Bernard, and M.-C. Marsolier-Kergoat. (2005). "Gene essentiality and the topology of protein interaction networks", *Proceedings of the Royal Society B: Biological Sciences*, Volume 272, Number 1573:1721-1725.
#'
#'J-D. Han, D. Dupuy, N. Bertin, M. E. Cusick, and M. Vidal. (2005). "Effect of sampling on topology predictions of protein-protein interaction networks", *Nature Biotechnology* 23 (7):839-844.
#'
#'M. Stumpf, C. Wiuf, and R. May. (2005). "Subnets of scale-free networks are not scale-free: Sampling properties of networks", *PNAS* 102 (12):4221-4224.
"protein"
#' French Financial Elite (influence)
#' @description In 1990 Kadushin collected data from 127 members of the French financial elite. He used various criteria to determine the top 28 and recorded their who-to-whom responses to questions about who was influencential, who were members of the elite and who were friends. He also recorded a large amount of information on their individual backgrounds and characteristics.
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data#ffe
#' @seealso [ffe_elite],[ffe_friends]
#' @references
#' Kadushin, C. 1995. "Friendship among the French financial elite." *American Sociological Review* 60:202-221.
"ffe_influence"
#' French Financial Elite (elite)
#' @description In 1990 Kadushin collected data from 127 members of the French financial elite. He used various criteria to determine the top 28 and recorded their who-to-whom responses to questions about who was influencential, who were members of the elite and who were friends. He also recorded a large amount of information on their individual backgrounds and characteristics.
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data#ffe
#' @seealso [ffe_influence],[ffe_friends]
#' @references
#' Kadushin, C. 1995. "Friendship among the French financial elite." *American Sociological Review* 60:202-221.
"ffe_elite"
#' French Financial Elite (friendships)
#' @description In 1990 Kadushin collected data from 127 members of the French financial elite. He used various criteria to determine the top 28 and recorded their who-to-whom responses to questions about who was influencential, who were members of the elite and who were friends. He also recorded a large amount of information on their individual backgrounds and characteristics.
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data#ffe
#' @seealso [ffe_influence],[ffe_elite]
#' @references
#' Kadushin, C. 1995. "Friendship among the French financial elite." *American Sociological Review* 60:202-221.
"ffe_friends"
#' Kapferer - Mine
#' @description Bruce Kapferer (1969) collected data on men working on the surface in a mining operation in Zambia (then Northern Rhodesia). He wanted to account for the development and resolution of a conflict among the workers. The conflict centered on two men, Abraham and Donald; most workers ended up supporting Abraham.
#'
#'Kapferer observed and recorded several types of interactions among the workers, including conversation, joking, job assistance, cash assistance and personal assistance. Two miners are connected if they are connected by any of these relations.
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data#kapmine
#' @references
#' Kapferer B. (1969). Norms and the manipulation of relationships in a work context. In J Mitchell (ed), Social networks in urban situations. Manchester: Manchester University Press.
#'
#' Doreian P. (1974). On the connectivity of social networks. *Journal of Mathematical Sociology*, 3, 245-258.
"mine"
#' Kapferer - Tailor-Shop (work)
#' @description Bruce Kapferer (1972) observed interactions in a tailor shop in Zambia (then Northern Rhodesia) over a period of ten months. His focus was the changing patterns of alliance among workers during extended negotiations for higher wages.
#'
#'Kapferer recorded two two different types of interaction, recorded at two different times (seven months apart) over a period of one month. This network includes the"instrumental" (work- and assistance-related) interactions at the two times
#'
#'The data are particularly interesting since an abortive strike occurred after the first set of observations, and a successful strike took place after the second.
#'
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data#kaptail
#' @seealso [tailor_social]
#' @references
#' Kapferer B. (1972). Strategy and transaction in an African factory. Manchester: Manchester University Press.
"tailor_work"
#' Kapferer - Tailor-Shop (social)
#' @description Bruce Kapferer (1972) observed interactions in a tailor shop in Zambia (then Northern Rhodesia) over a period of ten months. His focus was the changing patterns of alliance among workers during extended negotiations for higher wages.
#'
#'Kapferer recorded two two different types of interaction, recorded at two different times (seven months apart) over a period of one month. This network includes the"sociational" (friendship, socioemotional) interactions.
#'
#'The data are particularly interesting since an abortive strike occurred after the first set of observations, and a successful strike took place after the second.
#'
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data#kaptail
#' @seealso [tailor_work]
#' @references
#' Kapferer B. (1972). Strategy and transaction in an African factory. Manchester: Manchester University Press.
"tailor_social"
#' Les Miserables co-appearances
#' @description Weighted network of co-appearances of characters in Victor Hugo's novel "Les Miserables". Nodes represent characters as indicated by the labels and edges connect any pair of characters that appear in the same chapter of the book. The values on the edges are the number of such coappearances.
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data#lesmis
#' @references
#' D. E. Knuth. (1993). The Stanford GraphBase: A Platform for Combinatorial Computing, Addison-Wesley, Reading, MA
"miserables"
#' High-tech Managers (Advice)
#' @description These data were collected from the managers of a high-tec company. The company manufactured high-tech equipment on the west coast of the United States and had just over 100 employees with 21 managers. Each manager was asked "To whom do you go to for advice?" and "Who is your friend?" Data for the item "To whom do you report?" was taken from company documents. In addition attribute information was collected. This consisted of the managers age (in years), length of service or tenure (in years), level in the corporate hierarchy (coded 1,2 and 3; 1=CEO, 2 = Vice President, 3 = manager) and department (coded 1,2,3,4 with the CEO in department 0 ie not in a department).
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data#krackht
#' @seealso [ht_friends],[ht_reports]
#' @references
#' Krackhardt D. (1987). Cognitive social structures. *Social Networks*, 9, 104-134.
"ht_advice"
#' High-tech Managers (Friendships)
#' @description These data were collected from the managers of a high-tec company. The company manufactured high-tech equipment on the west coast of the United States and had just over 100 employees with 21 managers. Each manager was asked "To whom do you go to for advice?" and "Who is your friend?" Data for the item "To whom do you report?" was taken from company documents. In addition attribute information was collected. This consisted of the managers age (in years), length of service or tenure (in years), level in the corporate hierarchy (coded 1,2 and 3; 1=CEO, 2 = Vice President, 3 = manager) and department (coded 1,2,3,4 with the CEO in department 0 ie not in a department).
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data#krackht
#' @seealso [ht_advice],[ht_reports]
#' @references
#' Krackhardt D. (1987). Cognitive social structures. *Social Networks*, 9, 104-134.
"ht_friends"
#' High-tech Managers (Reports to)
#' @description These data were collected from the managers of a high-tec company. The company manufactured high-tech equipment on the west coast of the United States and had just over 100 employees with 21 managers. Each manager was asked "To whom do you go to for advice?" and "Who is your friend?" Data for the item "To whom do you report?" was taken from company documents. In addition attribute information was collected. This consisted of the managers age (in years), length of service or tenure (in years), level in the corporate hierarchy (coded 1,2 and 3; 1=CEO, 2 = Vice President, 3 = manager) and department (coded 1,2,3,4 with the CEO in department 0 ie not in a department).
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data#krackht
#' @seealso [ht_advice],[ht_friends]
#' @references
#' Krackhardt D. (1987). Cognitive social structures. *Social Networks*, 9, 104-134.
"ht_reports"
#' Political Books
#' @description Nodes represent books about US politics sold by the online bookseller Amazon.com. Edges represent frequent co-purchasing of books by the same buyers, as indicated by the "customers who bought this book also bought these other books" feature on Amazon.
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data#books
#' @references
#' Valdis Krebs, unpublished, http://www.orgnet.com/
"polbooks"
#' Law Firm (Advice)
#' @description This data set comes from a network study of corporate law partnership that was carried out in a Northeastern US corporate law firm, referred to as SG&R, 1988-1991 in New England. It includes (among others) measurements of networks among the 71 attorneys (partners and associates) of this firm, i.e. their strong-coworker network, advice network, friendship network, and indirect control networks. Various members' attributes are also part of the dataset, including seniority, formal status, office in which they work, gender, lawschool attended. The ethnography, organizational and network analyses of this case are available in Lazega (2001).
#'
#' **Basic advice network**:
#'"Think back over the past year, consider all the lawyers in your Firm. To whom did you go for basic professional advice? For instance, you want to make sure that you are handling a case right, making a proper decision, and you want to consult someone whose professional opinions are in general of great value to you. By advice I do not mean simply technical advice."
#'
#'\preformatted{
#'Coding:
#'The first 36 respondents are the partners in the firm. The attribute variables are:
#'1. status (1=partner; 2=associate)
#'2. gender (1=man; 2=woman)
#'3. office (1=Boston; 2=Hartford; 3=Providence)
#'4. years with the firm
#'5. age
#'6. practice (1=litigation; 2=corporate)
#'7. law school (1: harvard, yale; 2: ucon; 3: other)
#'}
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data#lazega
#' @seealso [law_friends],[law_cowork]
#' @references
#' Emmanuel Lazega, The Collegial Phenomenon: The Social Mechanisms of Cooperation Among Peers in a Corporate Law Partnership, Oxford University Press (2001).
#'
#' Tom A.B. Snijders, Philippa E. Pattison, Garry L. Robins, and Mark S. Handcock. New specifications for exponential random graph models. *Sociological Methodology* (2006), 99-153.
"law_advice"
#' Law Firm (Friendship)
#' @description This data set comes from a network study of corporate law partnership that was carried out in a Northeastern US corporate law firm, referred to as SG&R, 1988-1991 in New England. It includes (among others) measurements of networks among the 71 attorneys (partners and associates) of this firm, i.e. their strong-coworker network, advice network, friendship network, and indirect control networks. Various members' attributes are also part of the dataset, including seniority, formal status, office in which they work, gender, lawschool attended. The ethnography, organizational and network analyses of this case are available in Lazega (2001).
#'
#'**Friendship network:**
#'"Would you go through this list, and check the names of those you socialize with outside work. You know their family, they know yours, for instance. I do not mean all the people you are simply on a friendly level with, or people you happen to meet at Firm functions."
#'
#'\preformatted{
#'Coding:
#'The first 36 respondents are the partners in the firm. The attribute variables are:
#'1. status (1=partner; 2=associate)
#'2. gender (1=man; 2=woman)
#'3. office (1=Boston; 2=Hartford; 3=Providence)
#'4. years with the firm
#'5. age
#'6. practice (1=litigation; 2=corporate)
#'7. law school (1: harvard, yale; 2: ucon; 3: other)
#'}
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data#lazega
#' @seealso [law_advice],[law_cowork]
#' @references
#' Emmanuel Lazega, The Collegial Phenomenon: The Social Mechanisms of Cooperation Among Peers in a Corporate Law Partnership, Oxford University Press (2001).
#'
#' Tom A.B. Snijders, Philippa E. Pattison, Garry L. Robins, and Mark S. Handcock. New specifications for exponential random graph models. *Sociological Methodology* (2006), 99-153.
"law_friends"
#' Law Firm (Co-work)
#' @description This data set comes from a network study of corporate law partnership that was carried out in a Northeastern US corporate law firm, referred to as SG&R, 1988-1991 in New England. It includes (among others) measurements of networks among the 71 attorneys (partners and associates) of this firm, i.e. their strong-coworker network, advice network, friendship network, and indirect control networks. Various members' attributes are also part of the dataset, including seniority, formal status, office in which they work, gender, lawschool attended. The ethnography, organizational and network analyses of this case are available in Lazega (2001).
#'
#' **Strong coworkers network:**
#' "Because most firms like yours are also organized very informally, it is difficult to get a clear idea of how the members really work together. Think back over the past year, consider all the lawyers in your Firm. Would you go through this list and check the names of those with whom you have worked with. (By "worked with" I mean that you have spent time together on at least one case, that you have been assigned to the same case, that they read or used your work product or that you have read or used their work product; this includes professional work done within the Firm like Bar association work, administration, etc.)"
#'\preformatted{
#'Coding:
#'The first 36 respondents are the partners in the firm. The attribute variables are:
#'1. status (1=partner; 2=associate)
#'2. gender (1=man; 2=woman)
#'3. office (1=Boston; 2=Hartford; 3=Providence)
#'4. years with the firm
#'5. age
#'6. practice (1=litigation; 2=corporate)
#'7. law school (1: harvard, yale; 2: ucon; 3: other)
#'}
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data#lazega
#' @seealso [law_advice],[law_friends]
#' @references
#' Emmanuel Lazega, The Collegial Phenomenon: The Social Mechanisms of Cooperation Among Peers in a Corporate Law Partnership, Oxford University Press (2001).
#'
#' Tom A.B. Snijders, Philippa E. Pattison, Garry L. Robins, and Mark S. Handcock. New specifications for exponential random graph models. *Sociological Methodology* (2006), 99-153.
"law_cowork"
#' Social Networks Coauthors
#' @description Chris McCarty prepared a data set for the 2008 INSNA meeting in St. Pete. He recorded all the coauthorships in the Social Networks journal from the beginning to provide a network of networkers. The result was a t-shirt with a graphic design that was sold at the meeting.
#' After the meeting, Lin Freeman cleaned the data set and made it available on his website. It takes the form of a matrix that records coauthorship among 475 authors who were involved in the production of 295 articles. Cell entries report the number of coaurherships displayed by pairs of authors.
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data#auth
"sn_auth"
#' Newcomb Fraternity
#' @description The networksrecord weekly sociometric preference rankings from 17 men attending the University of Michigan in the fall of 1956; data from week 9 are missing. A "1" indicates first preference, and no ties were allowed.
#'
#' The men were recruited to live in off-campus (fraternity) housing, rented for them as part of the Michigan Group Study Project supervised by Theodore Newcomb from 1953 to 1956. All were incoming transfer students with no prior acquaintance of one another.
#' After the meeting, Lin Freeman cleaned the data set and made it available on his website. It takes the form of a matrix that records coauthorship among 475 authors who were involved in the production of 295 articles. Cell entries report the number of coaurherships displayed by pairs of authors.
#' @format list of 15 igraph objects
#' @source http://moreno.ss.uci.edu/data#newfrat
#' @references
#' Newcomb T. (1961). The acquaintance process. New York: Holt, Reinhard & Winston.
#'
#' Nordlie P. (1958). A longitudinal study of interpersonal attraction in a natural group setting. Unpublished doctoral dissertation, University of Michigan.
#'
#' White H., Boorman S. and Breiger R. (1977). Social structure from multiple networks, I. Blockmodels of roles and positions. *American Journal of Sociology*, 81, 730-780.
"fraternity"
#' Netscience Coauthorship
#' @description coauthorship network of scientists working on network theory and experiment, as compiled by Mark Newman in May 2006. The network was compiled from the bibliographies of two review articles on networks, M. E. J. Newman, SIAM Review 45, 167-256 (2003) and S. Boccaletti et al., Physics Reports 424, 175-308 (2006), with a few additional references added by hand. The version given here contains all components of the network, for a total of 1589 scientists, and not just the largest component of 379 scientists previously published. The network is weighted, with weights assigned directly in terms of the number of collaborations between authors and inversely in terms of the number of other authors involved. This weighting is described in M. E. J. Newman, Phys. Rev. E 64, 016132 (2001).
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data#netsci
#' @references
#' M. E. J. Newman, Finding community structure in networks using the eigenvectors of matrices, Preprint physics/0605087 (2006).
"netsci"
#' Florentine Families (Business)
#' @description Breiger & Pattison (1986), in their discussion of local role analysis, use a subset of data on the social relations among Renaissance Florentine families (person aggregates) collected by John Padgett from historical documents. The two relations are business ties (specifically, recorded financial ties such as loans, credits and joint partnerships) and marriage alliances.
#'
#' As Breiger & Pattison point out, the original data are symmetrically coded. This is acceptable perhaps for marital ties, but is unfortunate for the financial ties (which are almost certainly directed). To remedy this, the financial ties can be recoded as directed relations using some external measure of power - for instance, a measure of wealth. PADGW provides information on (1) each family's net wealth in 1427 (in thousands of lira); (2) the number of priorates (seats on the civic council) held between 1282- 1344; and (3) the total number of business or marriage ties in the total dataset of 116 families (see Breiger & Pattison (1986), p 239).
#'
#' Substantively, the data include families who were locked in a struggle for political control of the city of Florence in around 1430. Two factions were dominant in this struggle: one revolved around the infamous Medicis (9), the other around the powerful Strozzis (15).
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data#padgett
#' @seealso [flo_marriage]
#' @references
#' Breiger R. and Pattison P. (1986). Cumulated social roles: The duality of persons and their algebras. *Social Networks*, 8, 215-256.
#'
#' Kent D. (1978). The rise of the Medici: Faction in Florence, 1426-1434. Oxford: Oxford University Press.
"flo_business"
#' Florentine Families (Marriage)
#' @description Breiger & Pattison (1986), in their discussion of local role analysis, use a subset of data on the social relations among Renaissance Florentine families (person aggregates) collected by John Padgett from historical documents. The two relations are business ties (specifically, recorded financial ties such as loans, credits and joint partnerships) and marriage alliances.
#'
#' As Breiger & Pattison point out, the original data are symmetrically coded. This is acceptable perhaps for marital ties, but is unfortunate for the financial ties (which are almost certainly directed). To remedy this, the financial ties can be recoded as directed relations using some external measure of power - for instance, a measure of wealth. PADGW provides information on (1) each family's net wealth in 1427 (in thousands of lira); (2) the number of priorates (seats on the civic council) held between 1282- 1344; and (3) the total number of business or marriage ties in the total dataset of 116 families (see Breiger & Pattison (1986), p 239).
#'
#' Substantively, the data include families who were locked in a struggle for political control of the city of Florence in around 1430. Two factions were dominant in this struggle: one revolved around the infamous Medicis (9), the other around the powerful Strozzis (15).
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data#padgett
#' @seealso [flo_business]
#' @references
#' Breiger R. and Pattison P. (1986). Cumulated social roles: The duality of persons and their algebras. *Social Networks*, 8, 215-256.
#'
#' Kent D. (1978). The rise of the Medici: Faction in Florence, 1426-1434. Oxford: Oxford University Press.
"flo_marriage"
#' Madrid Train Bombing
#' @description
#' \preformatted{
#' Jose A. Rodriguez of the University of Barcelona created a network of the individuals involved in the bombing of commuter trains in Madrid on March 11, 2004. Rodriguez used press accounts in the two major Spanish daily newspapers (El Pais and El Mundo) to reconstruct the terrorist network. The names included were of those people suspected of having participated and their relatves. Four relations were recorded:
#'
#' Rodriguez specified 4 kinds of ties linking theindividuals involved:
#'
#' 1. Trust--friendship (contact, kinship, links in the telephone center).
#' 2. Ties to Al Qaeda and to Osama Bin Laden.
#' 3. Co-participation in training camps and/or wars.
#' 4. Co-participation in previous terrorist Attacks (Sept 11, Casablanca).
#'
#' These four were added together providing a "strength of connection" index that ranges from 1 to 4.
#' }
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data#train
#' @references
#'Hayes, Brian. 2006. "Connecting the dots." *American Scientist* 94 (5):400-404.
"train"
#' Bank Wiring Room
#' @description These are the observational data on 14 Western Electric (Hawthorne Plant) employees from the bank wiring room first presented in Roethlisberger & Dickson (1939). The data are better known through a scrutiny made of the interactions in Homans (1950), and the CONCOR analyses presented in Breiger et al (1975).
#'
#' The employees worked in a single room and include two inspectors (I1 and I3), three solderers (S1, S2 and S3), and nine wiremen or assemblers (W1 to W9). The interaction categories include: RDGAM, participation in horseplay; RDCON, participation in arguments about open windows; RDPOS, friendship; RDNEG, antagonistic (negative) behavior; RDHLP, helping others with work; and RDJOB, the number of times workers traded job assignments.
#'
#' **The dataset only includes the positive and negative ties, making it a signed network**
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data#wiring
#' @references
#' Breiger R., Boorman S. and Arabie P. (1975). An algorithm for clustering relational data with applications to social network analysis and comparison with multidimensional scaling. *Journal of Mathematical Psychology*, 12, 328-383.
#'
#' Homans G. (1950). The human group. New York: Harcourt-Brace.
#'
#' Roethlisberger F. and Dickson W. (1939). Management and the worker. Cambridge: Cambridge University Press.
"wiring"
#' Nouns in the King's James Bible
#' @description Christoph Romhild recorded 1773 proper nouns--people and places--in the King James Bible. He tallied 63,779 occasions in which pairs of these proper nouns appeared in the same verse in the bible. Many of these, of course, appeared more than once. So the data presented here are tallies, for each pair of proper nouns, of the number of verses in which they appeared together. Romhild worked with Chris Harrison, and together, they produced some elegant visual images of the data. They are displayed in the source listed below.
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data#bible
#' @references
#' http://chrisharrison.net/projects/bibleviz/index.html
"bible"
#' St. Louis Crimes
#' @description In the 1990s Rick Rosenfeld and Norm White used police records to collect data on crime in St. Louis. They began with five homicides and recorded the names of all the individuals who had been involved as victims, suspects or witnesses. They then explored the files and recorded all the other crimes in which those same individuals appeared. This snowball process was continued until they had data on 557 crime events. Those events involved 870 participants of which: 569 appeared as victims 682 appeared as suspects 195 appeared as witnesses, and 41 were dual (they were recorded both as victims and suspects in the same crime. Their data appear, then, as an 870 by 557, individual by crime event matrix. Victims are coded as 1, suspects as 2, witnesses as 3 and duals as 4.
#' @format (bipartite) igraph object
#' @source http://moreno.ss.uci.edu/data#crime
"crime"
#' Swedish Literary Criticism
#' @description Rosengren collected data on Swedish literary critics writing during the stylistic revolution in Swedish literature in 1881 to 1883. He recorded sets of authors, other than the author being reviewed, who were mentioned together in any published literary review in the Swedish press during those years. Then he dropped any pairs that were mentioned together less than five times and he included only those pairs of authors whose proportion of co-mentions was more than three standard errors above its expectation.
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data#swedish
#' @references
#' Rosengren, K. E. (1968). Sociological Aspects of the Literary System. Stockholm: Natur och Culture.
#'
#' Rosengren, K. E. (1983). The Climate of Literature: Sweden's Literary Frume of Reference, 1953-1976. Lund: Studentlitteratur.
#'
#' Freeman, Linton C. "Boxicity and the Social Context of Swedish Literary Criticism, 1881-1883." *Journal of Social and Biological Structures*, 9, 1986, 141-149.
"literary"
#' Rhesus Monkey Grooming
#' @description observed grooming episodes in a community of free ranging rhesus monkeys in Cayo Santiago observed in June and July of 1963. Seven are males (066, ER, R006, EZ, EC, CY and CN) and the other nine are females.
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data#rhesus
#' @references
#' D. S. Sade, "Sociometrics of Macaca mulatta: Linkages and cliques in grooming matrices," *Folia Primatologica*, 1972, 18: 196-223.
"rhesus"
#' Monastery
#' @description Sampson recorded the social interactions among a group of monks while resident as an experimenter on vision, and collected numerous sociometric rankings. During his stay, a political "crisis in the cloister" resulted in the expulsion of four monks (Nos. 2, 3, 17, and 18) and the voluntary departure of several others - most immediately, Nos. 1, 7, 14, 15, and 16. (In the end, only 5, 6, 9, and 11 remained).
#'
#' Most of the present data are retrospective, collected after the breakup occurred. They concern a period during which a new cohort entered the monastery near the end of the study but before the major conflict began. The exceptions are "liking" data gathered at three times: SAMPLK1 to SAMPLK3 - that reflect changes in group sentiment over time (SAMPLK3 was collected in the same wave as the data described below). Information about the senior monks was not included.
#'
#' Four relations are coded, with separate matrices for positive and negative ties on the relation. Each member ranked only his top three choices on that tie. The relations are esteem (SAMPES) and disesteem (SAMPDES), liking (SAMPLK) and disliking (SAMPDLK), positive influence (SAMPIN) and negative influence (SAMPNIN), praise (SAMPPR) and blame (SAMPNPR). In all rankings 3 indicates the highest or first choice and 1 the last choice. (Some subjects offered tied ranks for their top four choices).
#' @details the different relations are given in a list of networks in the same order as given in the description.
#' @format list of igraph objects
#' @source http://moreno.ss.uci.edu/data#sampson
#' @references
#' Breiger R., Boorman S. and Arabie P. (1975). An algorithm for clustering relational data with applications to social network analysis and comparison with multidimensional scaling. *Journal of Mathematical Psychology*, 12, 328-383.
#'
#' Sampson, S. (1969). Crisis in a cloister. Unpublished doctoral dissertation, Cornell University.
"sampson"
#' Taro Exchange
#' @description These data represent the relation of gift-giving (taro exchange) among 22 households in a Papuan village. Hage & Harary (1983) used them to illustrate a graph hamiltonian cycle. Schwimmer points out how these ties function to define the appropriate persons to mediate the act of asking for or receiving assistance among group members.
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data#taro
#' @references
#' Hage P. and Harary F. (1983). Structural models in anthropology. Cambridge: Cambridge University Press.
#'
#' Schwimmer E. (1973). Exchange in the social structure of the Orokaiva. New York: St Martins.
"taro"
#' Macaque Dominance
#' @description records dominance relations (a directed tie from a to b means a dominates b) in a colony of 62 adult female Japanese macaques (Macaca fuscata fuscata). They are known as the "Arashiyama B group." Records were made during the non-mating season, April to early October, 1976. Approach-retreat episodes involving food were recorded.
#'
#'In addition, the presence of six lineages was reported. The first 4 animals belong to a lineage, and the next 14 belong to another. The following 31 are in a third lineage, and the next 6 are in the fourth. The following 6 are the fifth lineage, and the remaining animal is unrelated to the others.
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data#mac
#' @references
#' Y. Takahata, "Diachronic changes in the dominance relations of adult female Japanese monkeys of the Arashiyama B group," in Linda Marie Fedigan and Pamela J. Asquith, eds., The Monkeys of Arashiyama. Albany: State University of New York Press, 1991, pp. 124-139.
"macaque"
#' Residence Hall Friendship
#' @description Cynthia Webster collected friendship data among the 217 residents living at a residence hall located on the Australian National University campus. Residents were interviewed individually at the start of the second semester.
#'
#'First, they were asked to recall all of their friends who currently lived in the residence hall. They then were provided with a list of all residents and were asked to add anyone whom they also considered a friend, but had forgotten to include. From the complete list of friends, they were asked to indicate the strength of each friendship tie. Most specified three levels of friendship, "best friend," "close friend," and "friend." The data were combined to form a valued, actor-by-actor matrix of reported friendship relations.
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data#oz
#' @references
#' L. C. Freeman, C. M. Webster and D. M. Kirke (1998) "Exploring social structure using dynamic three-dimensional color images." *Social Networks* 20, 109-118
"hall"
#' INSNA Teacher Student
#' @description When Barry Wellman founded the International Network for Social Network Analysis (INSNA) in 1977, he sent a questionnaire to all the founding members. Included were questions on who taught each founder and who each founder taught. This data set is based on their responses.
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data#ts
#' @references
#' K. Reitz and D. R. White, 1989 "Rethinking the Role Concept: Homomorphisms on Social Networks" pp. 429-488 in L.C.Freeman, D.R. White, A.K.Romney, eds., Research Methods in Social Network Analysis. George Mason Press. Reprinted 1992 Transaction Publishers: New Brunswick, NJ.
"insna"
#' Karate Club (binary)
#' @description These are data collected from the members of a university karate club by Wayne Zachary (presence or absence of ties among the members of the club)
#'
#' Zachary (1977) used these data and an information flow model of network conflict resolution to explain the split-up of this group following disputes among the members.
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data#zachary
#' @seealso [karate_weight]
#' @references
#' Zachary W. (1977). An information flow model for conflict and fission in small groups. *Journal of Anthropological Research*, 33, 452-473.
"karate"
#' Karate Club (weighted)
#' @description These are data collected from the members of a university karate club by Wayne Zachary (relative strength of the associations, i.e. number of situations in and outside the club in which interactions occurred).
#'
#' Zachary (1977) used these data and an information flow model of network conflict resolution to explain the split-up of this group following disputes among the members.
#' @format igraph object
#' @source http://moreno.ss.uci.edu/data#zachary
#' @seealso [karate]
#' @references
#' Zachary W. (1977). An information flow model for conflict and fission in small groups. *Journal of Anthropological Research*, 33, 452-473.
"karate_weight"
================================================
FILE: R/data-konnect.R
================================================
#' Arenas Email
#' @description This is the email communication network at the University Rovira i Virgili in Tarragona in the south of Catalonia in Spain. Nodes are users and each edge represents that at least one email was sent. The direction of emails or the number of emails are not stored.
#' @format igraph object
#' @source Data downloaded from http://konect.uni-koblenz.de/
#'orginaly from http://deim.urv.cat/~aarenas/data/welcome.htm
#' @references Jerome Kunegis. KONECT - The Koblenz Network Collection. In Proc. Int. Web Observatory Workshop, pages 1343-1350, 2013.
#'
#' Roger Guimerà, Leon Danon, Albert Díaz-Guilera, Francesc Giralt, and Alex Arenas. Self-similar community structure in a network of human interactions. Phys. Rev. E, 68(6):065103, 2003.
"arenas_email"
#' Arenas Metabolic
#' @description This is the metabolic network of the roundworm Caenorhabditis elegans. Nodes are metabolites (e.g., proteins), and edges are interactions between them. Since a metabolite can iteract with itself, the network contains loops. The interactions are undirected. There may be multiple interactions between any two metabolites.
#' @format igraph object
#' @source Data downloaded from http://konect.uni-koblenz.de/
#'orginaly from http://deim.urv.cat/~aarenas/data/welcome.htm
#' @references Jerome Kunegis. KONECT - The Koblenz Network Collection. In Proc. Int. Web Observatory Workshop, pages 1343-1350, 2013.
#'
#' Jordi Duch and Alex Arenas. Community detection in complex networks using extremal optimization. Phys. Rev. E, 72(2):027104, 2005.
"arenas_meta"
#' Brunson Club Membership
#' @description This bipartite network contains membership information of corporate executive officers in social organisations such as clubs and boards. Left nodes represent persons and right nodes represent social organisations. An edge between a person and a social organization shows that the person has a memberstatus.
#' @format igraph object
#' @source Data downloaded from http://konect.uni-koblenz.de/
#'orginaly from https://github.com/corybrunson/triadic
#' @references Jerome Kunegis. KONECT - The Koblenz Network Collection. In Proc. Int. Web Observatory Workshop, pages 1343-1350, 2013.
#'
#' Katherine Faust. Centrality in affiliation networks. Social Networks, 19(2):157--191, 1997.
"brunson_club_membership"
#' Brunson Corporate Leadership
#' @description This bipartite network contains person–company leadership information between companies and 20 corporate directors. The data was collected in 1962. Left nodes represent persons and right nodes represent companies. An edge between a person and a company shows that the person had a leadership position in that company.
#' @format igraph object
#' @source Data downloaded from http://konect.uni-koblenz.de/
#'orginaly from https://github.com/corybrunson/triadic
#' @references Jerome Kunegis. KONECT - The Koblenz Network Collection. In Proc. Int. Web Observatory Workshop, pages 1343-1350, 2013.
#'
#' Roy Barnes and Tracy Burkett. Structural redundancy and multiplicity in corporate networks. International Network for Social Network Analysis, 30(2), 2010.
"brunson_corporate_leadership"
#' Brunson Revolution
#' @description This bipartite network contains membership information of 136 people in 5 organisations dating back to the time before the American Revolution. The list includes well-known people such as the American activist Paul Revere. Left nodes represent persons and right nodes represent organisations. An edge between a person and an organization shows that the person was a member of the organisation.
#' @format igraph object
#' @source Data downloaded from http://konect.uni-koblenz.de/
#'orginaly from https://github.com/corybrunson/triadic
#' @references Jerome Kunegis. KONECT - The Koblenz Network Collection. In Proc. Int. Web Observatory Workshop, pages 1343-1350, 2013.
#'
#' NA
"brunson_revolution"
#' Brunson South Africa
#' @description This bipartite network contains person–company shared leadership relations of "the five most representative companies" that are claimed to represent "the small inner ring of South African Finance". Left nodes represent persons and right nodes represent companies. An edge between a person and a company shows that the person had a leadership position in that company.
#' @format igraph object
#' @source Data downloaded from http://konect.uni-koblenz.de/
#'orginaly from https://github.com/corybrunson/triadic
#' @references Jerome Kunegis. KONECT - The Koblenz Network Collection. In Proc. Int. Web Observatory Workshop, pages 1343-1350, 2013.
#'
#' John Atkinson Hobson. The Evolution of Modern Capitalism: A Study of Machine Production. The Walter Scott publishing co., ltd., 1919.
"brunson_south_africa"
#' USA Bordering States
#' @description These are the 48 contiguous states and the District of Columbia of the United States of America (the USA). They include all states except the states of Alaska and Hawaii, which are not connected by land with the other states, and include the District of Columbia (DC). An edge denotes that two states share a border. The US states in the configuration given by this dataset exist since February 14, 1912, when Arizona was admitted as the 48th state, and is current as of 2014. The states of Alaska and Hawaii were admitted as the 49th and 50th states in 1959, but are not contiguous with the other states, and are not reflected in this dataset.
#' @format igraph object
#' @source Data downloaded from http://konect.uni-koblenz.de/
#'orginaly from http://www-cs-faculty.stanford.edu/~uno/sgb.html
#' @references Jerome Kunegis. KONECT - The Koblenz Network Collection. In Proc. Int. Web Observatory Workshop, pages 1343-1350, 2013.
#'
#' Donald E. Knuth. The Art of Computer Programming, Volume 4, Fascicle 0: Introduction to Combinatorial and Boolean Functions. Addison-Wesley, 2008.
"usa_borders"
#' DNC Email (Corecipients)
#' @description This is the undirected network of people having received the same email in the 2016 Democratic National Committee email leak. The Democratic National Committee (DNC) is the formal governing body for the United States Democratic Party. A dump of emails of the DNC was leaked in 2016, and this dataset contains persons from that dump as nodes, and an edge when two persons received the same email, i.e., when two persons were on the recipient list of the same email. Multiple edges indicate multiple emails.
#' @format igraph object
#' @seealso [dnc_temporalGraph]
#' @source Data downloaded from http://konect.uni-koblenz.de/
#'orginaly from http://www.rene-pickhardt.de/extracting-2-social-network-graphs-from-the-democratic-national-committee-email-corpus-on-wikileaks/
#' @references Jerome Kunegis. KONECT - The Koblenz Network Collection. In Proc. Int. Web Observatory Workshop, pages 1343-1350, 2013.
#'
"dnc_corecipient"
#' DNC Email (Temporal)
#' @description This is the directed network of emails in the 2016 Democratic National Committee email leak. The Democratic National Committee (DNC) is the formal governing body for the United States Democratic Party. A dump of emails of the DNC was leaked in 2016. Nodes in the network correspond to persons in the dataset. A directed edge in the dataset denotes that a person has sent an email to another person. Since an email can have any number of recipients, a single email is mapped to multiple edges in this dataset, resulting in the number of edges in this network being about twice the number of emails in the dump.
#' @format igraph object
#' @seealso [dnc_corecipient]
#' @source Data downloaded from http://konect.uni-koblenz.de/
#'orginaly from http://www.rene-pickhardt.de/extracting-2-social-network-graphs-from-the-democratic-national-committee-email-corpus-on-wikileaks/
#' @references Jerome Kunegis. KONECT - The Koblenz Network Collection. In Proc. Int. Web Observatory Workshop, pages 1343-1350, 2013.
#'
"dnc_temporalGraph"
#' FAA Preferred Routes
#' @description This network was constructed from the USA's FAA (Federal Aviation Administration) National Flight Data Center (NFDC), Preferred Routes Database. Nodes in this network represent airports or service centers and links are created from strings of preferred routes recommended by the NFDC.
#' @format igraph object
#' @source Data downloaded from http://konect.uni-koblenz.de/
#'orginaly from http://research.mssm.edu/maayan/datasets/qualitative_networks.shtml
#' @references Jerome Kunegis. KONECT - The Koblenz Network Collection. In Proc. Int. Web Observatory Workshop, pages 1343-1350, 2013.
#'
#' Federal Aviation Administration. Air traffic control system command center. http://www.fly.faa.gov/.
"maayan_faa"
#' Maayan Pdzbase
#' @description This is a network of protein–protein interactions from PDZBase.
#' @format igraph object
#' @source Data downloaded from http://konect.uni-koblenz.de/
#'orginaly from http://research.mssm.edu/maayan/datasets/qualitative_networks.shtml
#' @references Jerome Kunegis. KONECT - The Koblenz Network Collection. In Proc. Int. Web Observatory Workshop, pages 1343-1350, 2013.
#'
#' Thijs Beuming, Lucy Skrabanek, Masha Y. Niv, Piali Mukherjee, and Harel Weinstein. PDZBase: A protein--protein interaction database for PDZ-domains. Bioinformatics, 21(6):827--828, 2005.
"maayan_pdzbase"
#' Powergrid
#' @description This undirected network contains information about the power grid of the Western States of the United States of America. An edge represents a power supply line. A node is either a generator, a transformator or a substation.
#' @format igraph object
#' @source Data downloaded from http://konect.uni-koblenz.de/
#'orginaly from http://toreopsahl.com/datasets/#uspowergrid
#' @references Jerome Kunegis. KONECT - The Koblenz Network Collection. In Proc. Int. Web Observatory Workshop, pages 1343-1350, 2013.
#'
#' Duncan J. Watts and Steven H. Strogatz. Collective dynamics of `small-world' networks. Nature, 393(1):440--442, 1998.
"powergrid"
#' UC forum (2-mode)
#' @description This bipartite network contains user posts to forums. The users are students at the University of California, Irvine. An edge represents the number of times a (P)erson posted in a (F)orum.
#'
#' @format igraph object
#' @seealso [ucsocial]
#' @source Data downloaded from http://konect.uni-koblenz.de/
#'orginaly from http://toreopsahl.com/datasets/#online_forum_network
#' @references Jerome Kunegis. KONECT - The Koblenz Network Collection. In Proc. Int. Web Observatory Workshop, pages 1343-1350, 2013.
#'
#' Tore Opsahl and Pietro Panzarasa. Triadic closure in two-mode networks: Redefining the global and local clustering coefficients. Social Networks, 34, 2011.
"ucforum"
#' UC forum (messages sent)
#' @description This directed network contains sent messages between the users of an online community of students from the University of California, Irvine. A node represents a user. A directed edge represents a sent message. Multiple edges denote multiple messages.
#' @format igraph object
#' @seealso [ucforum]
#' @source Data downloaded from http://konect.uni-koblenz.de/
#'orginaly from http://toreopsahl.com/datasets/#online_social_network
#' @references Jerome Kunegis. KONECT - The Koblenz Network Collection. In Proc. Int. Web Observatory Workshop, pages 1343-1350, 2013.
#'
#' Tore Opsahl and Pietro Panzarasa. Clustering in weighted networks. Social Networks, 31(2):155--163, 2009.
"ucsocial"
#' US Flights 2010
#' @description This is the directed network of flights between US airports in 2010. Each edge represents a connection from one airport to another, and the weight of an edge shows the number of flights on that connection in the given direction, in 2010.
#' @format igraph object
#' @source Data downloaded from http://konect.uni-koblenz.de/
#'orginaly from http://toreopsahl.com/datasets/#usairports
#' @references Jerome Kunegis. KONECT - The Koblenz Network Collection. In Proc. Int. Web Observatory Workshop, pages 1343-1350, 2013.
#'
#' Tore Opsahl. Why anchorage is not (that) important: Binary ties and sample selection, 2011.
"usflights"
#' Petster Friendships
#' @description This Network contains friendships between users of the website hamsterster.com. The network contains many vertex attributes about the pet.
#' @format igraph object
#' @source Data downloaded from http://konect.uni-koblenz.de/
#' @references Jerome Kunegis. KONECT - The Koblenz Network Collection. In Proc. Int. Web Observatory Workshop, pages 1343-1350, 2013.
#'
"petster"
#' Radoslaw - Email Network
#' @description This is the internal email communication network between employees of a mid-sized manufacturing company. The network is directed and nodes represent employees. The left node represents the sender and the right node represents the recipient. Edges between two nodes are individual emails.
#' @format igraph object
#' @source Data downloaded from http://konect.uni-koblenz.de/
#'orginaly from http://www.ii.pwr.wroc.pl/~michalski/index.php?content=datasets#manufacturing
#' @references Jerome Kunegis. KONECT - The Koblenz Network Collection. In Proc. Int. Web Observatory Workshop, pages 1343-1350, 2013.
#'
#' Radoslaw Michalski, Sebastian Palus, and Przemyslaw Kazienko. Matching organizational structure and social network extracted from email communication. In Lecture Notes in Business Information Processing, volume 87, pages 197--206. Springer Berlin Heidelberg, 2011.
"radoslaw_email"
#' Face-2-face contacts at Hypertext
#' @description This is the network of face-to-face contacts of the attendees of the ACM Hypertext 2009 conference. The ACM Conference on Hypertext and Hypermedia 2009 (HT 2009, http://www.ht2009.org/) was held in Turin, Italy over three days from June 29 to July 1, 2009. In the network, a node represents a conference visitor, and an edge represents a face-to-face contact that was active for at least 20 seconds. Multiple edges denote multiple contacts. Each edge is annotated with the time at which the contact took place.
#' @format igraph object
#' @source Data downloaded from http://konect.uni-koblenz.de/
#'orginaly from http://www.sociopatterns.org/
#' @references Jerome Kunegis. KONECT - The Koblenz Network Collection. In Proc. Int. Web Observatory Workshop, pages 1343-1350, 2013.
#'
#' Lorenzo Isella, Juliette Stehlé, Alain Barrat, Ciro Cattuto, Jean-François Pinton, and Wouter Van den Broeck. What's in a crowd? analysis of face-to-face behavioral networks. J. of Theoretical Biology, 271(1):166--180, 2011.
"f2f_hypertext"
#' Face-2-face contacts at Infectious
#' @description This network describes the face-to-face behavior of people during the exhibition INFECTIOUS: STAY AWAY in 2009 at the Science Gallery in Dublin. Nodes represent exhibition visitors; edges represent face-to-face contacts that were active for at least 20 seconds. Multiple edges between two nodes are possible and denote multiple contacts. The network contains the data from the day with the most interactions.
#' @format igraph object
#' @source Data downloaded from http://konect.uni-koblenz.de/
#'orginaly from http://www.sociopatterns.org/
#' @references Jerome Kunegis. KONECT - The Koblenz Network Collection. In Proc. Int. Web Observatory Workshop, pages 1343-1350, 2013.
#'
#' Lorenzo Isella, Juliette Stehlé, Alain Barrat, Ciro Cattuto, Jean-François Pinton, and Wouter Van den Broeck. What's in a crowd? analysis of face-to-face behavioral networks. J. of Theoretical Biology, 271(1):166--180, 2011.
"f2f_infectious"
#' Road network Europe
#' @description This is the international E-road network, a road network located mostly in Europe. The network is undirected; nodes represent cities and an edge between two nodes denotes that they are connected by an E-road.
#' @format igraph object
#' @source Data downloaded from http://konect.uni-koblenz.de/
#'orginaly from http://lovro.lpt.fri.uni-lj.si/support.jsp
#' @references Jerome Kunegis. KONECT - The Koblenz Network Collection. In Proc. Int. Web Observatory Workshop, pages 1343-1350, 2013.
#'
#' Lovro Subelj and Marko Bajec. Robust network community detection using balanced propagation. Eur. Phys. J. B, 81(3):353--362, 2011.
"euroroad"
#' Road Transportation Network Chicago
#' @description This is the road transportation network of the Chicago region (USA). Nodes are transport nodes, and edges are connections.
#' @format igraph object
#' @source Data downloaded from http://konect.uni-koblenz.de/
#'orginaly from http://www.bgu.ac.il/~bargera/tntp/
#' @references Jerome Kunegis. KONECT - The Koblenz Network Collection. In Proc. Int. Web Observatory Workshop, pages 1343-1350, 2013.
#'
#' R. W. Eash, K. S. Chon, Y. J. Lee, and D. E. Boyce. Equilibrium traffic assignment on an aggregated highway network for sketch planning. Transportation Research Record, 994:30--37, 1983.
"chicagoroad"
#' Unicodelang
#' @description This bipartite network denotes which languages are spoken in which countries. Nodes are countries and languages; edge weights denote the proportion (between zero and one) of the population of a given country speaking a given language. To quote the Unicode data description: "The main goal is to provide approximate figures for the literate, functional population for each language in each territory: that is, the population that is able to read and write each language, and is comfortable enough to use it with computers."
#' @format igraph object
#' @source Data downloaded from http://konect.uni-koblenz.de/
#'orginaly from http://www.unicode.org/cldr/charts/25/supplemental/territory_language_information.html
#' @references Jerome Kunegis. KONECT - The Koblenz Network Collection. In Proc. Int. Web Observatory Workshop, pages 1343-1350, 2013.
#'
"unicodelang"
================================================
FILE: R/data-misc.R
================================================
#' Grey's Anatomy Hook-ups
#' @description Network of hook-ups of characters in "Grey's Anatomy".
#' @format igraph object
"greys"
#' Centrality literature network
#' @description In 1979, Linton Freeman published a paper which defined several kinds of centrality. His typology has become the standard for network analysis. Freeman, however, was not the first to publish on centrality in networks. His paper is part of a discussion which dates back to the 1940s. The network shows the papers that discuss network centrality and their cross- references until 1979. Arcs represent citations; they point from the cited paper to the citing paper.
#'
#' In principle, papers can only cite papers which appeared earlier, so the network is acyclic. Arcs never point back to older papers just like parents cannot be younger than their children. However, there are usually some exceptions in a citation network: papers which cite one another, e.g., papers appearing at about the same time and written by one author. We eliminated these exceptions by shrinking the papers by an author which are connected by cyclic citations. In the centrality literature network, we used the latter approach (e.g., two publications by Gilch in 1954 are shrunk to one paper #GilchSW-54).
#' @format igraph object
#' @source https://sites.google.com/site/ucinetsoftware/datasets/centralityliteraturenetwork
#' @references
#' N.P. Hummon, P. Doreian, & L.C. Freeman, 'Analyzing the structure of the centrality-productivity literature created between 1948 and 1979' (in: Knowledge-Creation Diffusion Utilization, 11 (1990), 459-480).
#'
#' W. de Nooy, A. Mrvar, & V. Batagelj, Exploratory Social Network Analysis with Pajek (Cambridge: Cambridge University Press, 2004), Chapter 11.
"cent_lit"
#' Words in David Copperfield
#' @description A network of common adjective and noun adjacencies for the novel "David Copperfield" by Charles Dickens, as described by M. Newman. Nodes represent the most commonly occurring adjectives and nouns in the book. Edges connect any pair of words that occur in adjacent position in the text of the book.
#' @format igraph object
#' @source http://www-personal.umich.edu/~mejn/netdata/
#' @references
#' Newman, Mark EJ. "Finding community structure in networks using the eigenvectors of matrices." *Physical Review E* 74.3 (2006): 036104.
"adjnoun"
#' Game of Thrones Interactions
#' @description Character Interaction Networks for the HBO Series "Game of Thrones" (Season 1-7).
#' These networks were created by parsing fan-generated scripts from https://genius.com/artists/Game-of-thrones.
#' Pairs of characters are connected by (undirected) edges weighted by the number of interactions.
#'
#' \preformatted{
#' There are five interaction types. Character A and Character B are connected whenever:
#'
#' Character A speaks directly after Character B
#' Character A speaks about Character B
#' Character C speaks about Character A and Character B
#' Character A and Character B are mentioned in the same stage direction
#' Character A and Character B appear in a scene together
#' }
#' @format list of igraph objects
#' @source https://networkofthrones.wordpress.com, Downloaded from https://github.com/mathbeveridge/gameofthrones/
#' @references Andrew Beveridge and Michael Chemers, "The Game of 'The Game of Thrones': Networked Concordances and Fractal Dramaturgy", in: Paola Brembilla and Ilaria De Pacalis (eds.), Reading Contemporary Serial Television Universes: A Narrative Ecosystem Framework, Routledge, 2018.
"got"
#' ATP Tennis (1968-2021)
#' @description The dataset includes all ATP tennis matches from 1968-2021 The networks are directed pointing from the loser to the winner.
#' Each network contains the following attributes:
#' \preformatted{
#' Edge attributes:
#' - surface: on which surface the match(es) took place (e.g. "Hard", "Grass", "Clay")
#' - weight: number of times Player A lost to Player B on surface X
#' Vertex attributes:
#' - hand: if player is (L)eft or (R)ight handed (or (U)nknown)
#' - age: age of player during the season
#' - country: home country of player
#' }
#' Check out \url{https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0017249} for a potential use case.
#' @format list of igraph objects
#' @seealso [wta]
#' @source Networks constructed from data that was gathered and compiled by Jeff Sackmann (https://github.com/JeffSackmann). Please give credit to him if you use this data.
"atp"
#' WTA Tennis (1968-2021)
#' @description The dataset includes all WTA tennis matches from 1968-2021 The networks are directed pointing from the loser to the winner.
#' Each network contains the following attributes:
#' \preformatted{
#' Edge attributes:
#' - surface: on which surface the match(es) took place (e.g. "Hard", "Grass", "Clay")
#' - weight: number of times Player A lost to Player B on surface X
#' Vertex attributes:
#' - hand: if player is (L)eft or (R)ight handed (or (U)nknown)
#' - age: age of player during the season
#' - country: home country of player
#' }
#' Check out \url{https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0017249} for a potential use case.
#' @format list of igraph objects
#' @seealso [atp]
#' @source Networks constructed from data that was gathered and compiled by Jeff Sackmann (https://github.com/JeffSackmann). Please give credit to him if you use this data.
"wta"
#' Star Wars Episode 1-7
#' @description Scene Co-occurrence of Star Wars Characters (Episode 1-7)
#' @source Data downloaded from https://github.com/evelinag/StarWars-social-network
#' @format list of igraph objects
"starwars"
#' 50-actor excerpt from the Teenage Friends and Lifestyle Study data
#' @description longitudinal, 3 waves, networks and behavior
#' @source Data downloaded from https://www.stats.ox.ac.uk/~snijders/siena/siena_datasets.htm
#' @format list of igraph objects
"s50"
#' Simplified football results
#' @description A list of 112 networks of football leagues.
#' A directed link between team A and B indicates that A won a match against B. Note that there can also be an edge from B to A, since most leagues play a double round robin. For the sake of simplicity, all draws were deleted so that there could also be null ties between two teams if both games ended in a draw.
#' The data can be used to experiment with the triad census
#' @source soccerverse.com
#' @format list of igraph objects
"football_triad"
#' Illustrating cliques
#' @description A small graph to illustrate the concept of cliques and k-cores.
#' @format igraph object
"clique_graph"
#' Illustrating core-periphery
#' @description A graph to illustrate the concept of core-periphery.
#' @format igraph object
"core_graph"
#' Coleman's High School Friendship Data
#' @description James Coleman (1964) reports research on self-reported friendship ties among 73 boys in a small high school in Illinois over the 1957-1958 academic year. Networks of reported ties for all 73 informants are provided for two time points (fall and spring).
#' @format list of two igraph objects
#' @references Coleman, J. S. (1964). Introduction to Mathematical Sociology. New York: Free Press.
"coleman"
#' Knecht's School Data
#' @description This data is about a friendship network in a Dutch school class. The data were collected between September 2003 and June 2004 by Andrea Knecht, supervised by Chris Baerveldt, at the Department of Sociology of the University of Utrecht (NL). The entire study is reported in Knecht (2008).
#'
#' The 26 students were followed over their first year at secondary school during which friendship networks as well as other data were assessed at four time points at intervals of three months. There were 17 girls and 9 boys in the class, aged 11-13 at the beginning of the school year. Network data were assessed by asking students to indicate up to twelve classmates which they considered good friends.
#'
#' Delinquency is defined as a rounded average over four types of minor delinquency (stealing, vandalism, graffiti, and fighting), measured in each of the four waves of data collection. The five-point scale ranged from `never' to `more than 10 times', and the distribution is highly skewed. In a range of 1-5, the mode was 1 at all four waves, the average rose over time from 1.4 to 2.0, and the value 5 was never observed.
#' @details The network contains the following attributes:
#' \itemize{
#' \item{"deli"}{ rounded average of four items (stealing, vandalizing, fighting, graffiti);
#' categories: frequency over last three months,
#' 1 = never, 2 = once, 3 = 2-4 times, 4 = 5-10 times, 5 = more than 10 times;
#' 0 = missing}
#' \item{alcohol}{alcohol "How often did you drink alcohol with friends in the last three months?"
#' categories: 1 = never, 2 = once, 3 = 2-4 times, 4 = 5-10 times, 5 = more than 10 times;
#' 0 = missing. Only waves 2-4}
#' \item{"gender"}{(1 = girl, 2 = boy)}
#' \item{"age"}{years}
#' \item{"ethnic"}{Ethnicity (1 = Dutch, 2 = other, 0 = missing)}
#' \item{"religion"}{(1 = Christian, 2 = non-religious, 3 = non-Christian religion, 0 = missing)}
#' }
#' @format list of four igraph objects
#' @references Andrea Knecht (2006), Networks and actor attributes in early adolescence, Utrecht, The Netherlands Research School ICS, Department of Sociology, Utrecht University
"knecht"
#' Senat 2015 Bill cosponsorship
#' @description Bill cosponsorship network for the 115th Senate obtained from govtrack.us
#' @format two-mode network as igraph object
"cosponsor"
#' Teenage Friends and Lifestyle Study data
#' @description longitudinal, 3 waves, networks and behavior. For the codebook, see the link provided as source. Only sex.F was recoded to F/M.
#' @source Data downloaded from https://www.stats.ox.ac.uk/~snijders/siena/Glasgow_data.htm
#' @format list of igraph objects
"glasgow129"
#' General Social Survey 2004 Egocentric Network Data
#' @description A stratified sample of 594 respondents from the 2004 General Social Survey (GSS) containing egocentric network data. The dataset includes information about respondents (egos), their network members (alters), and the relationships between those network members (alter-alter ties).
#'
#' The sample was drawn using stratified random sampling without replacement across five stratification variables: age category (3 levels), race, sex, marital status, and number of alters named.
#'
#' @format An egor object (from the egor package) containing three data frames:
#' \describe{
#' \item{ego}{594 respondents with the following variables:
#' \itemize{
#' \item .egoID: Unique ego identifier
#' \item vpsu: Variance primary sampling unit
#' \item vstrat: Variance stratum
#' \item wtssall: Sample weight
#' \item age: Respondent's age in years
#' \item race: Race of respondent
#' \item sex: Sex of respondent
#' \item marital: Marital status
#' \item numgiven: Number of alters named
#' }
#' }
#' \item{alter}{555 alters (network members) with variables:
#' \itemize{
#' \item .egoID: Link to ego
#' \item .alterID: Alter identifier (1-5)
#' \item age: Alter's age
#' \item race: Alter's race
#' \item sex: Alter's sex
#' \item spouse: Whether alter is spouse/partner
#' \item intrace: Race of alter (alternative coding)
#' \item sexsex: Sex of alter (alternative coding)
#' }
#' }
#' \item{aatie}{640 alter-alter ties with variables:
#' \itemize{
#' \item .egoID: Link to ego
#' \item .srcID: Source alter ID
#' \item .tgtID: Target alter ID
#' \item weight: Closeness (1 = especially close, 2 = know each other, 3 = total strangers)
#' }
#' }
#' }
#' @source General Social Survey 2004. NORC at the University of Chicago.
#' Data retrieved from \url{https://gss.norc.org/}
#' @references
#' Smith, Tom W., Michael Davern, Jeremy Freese, and Stephen Morgan. General Social Surveys, 1972-2021. Chicago: NORC, 2022.
#' @seealso \code{\link[egor]{egor}} for working with egocentric network data
"gss_egor"
#' Flights within the US
#'
#' @description A network of flights between US airports
"us_flights"
================================================
FILE: R/data-movie.R
================================================
#'10 Things I Hate About You
#' @description Interactions of characters in the movie "10 Things I Hate About You" (1999)
#' @format igraph object
#' @details The networks were built with a movie script parser. Even after multiple manual checks, the data set can still contain minor errors (e.g. typos in character names or wrongly parsed names). This may require some additional manual checks before using the data. Please report any such issues (https://github.com/schochastics/networkdata/issues/)
#' @source https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/T4HBA3
#'
#' https://www.imdb.com/title/tt0147800
#' @references Kaminski, Jermain; Schober, Michael; Albaladejo, Raymond; Zastupailo, Oleksandr; Hidalgo, César, 2018, Moviegalaxies - Social Networks in Movies, https://doi.org/10.7910/DVN/T4HBA3, Harvard Dataverse, V3
"movie_1"
#'12
#' @description Interactions of characters in the movie "12" (2007)
#' @format igraph object
#' @details The networks were built with a movie script parser. Even after multiple manual checks, the data set can still contain minor errors (e.g. typos in character names or wrongly parsed names). This may require some additional manual checks before using the data. Please report any such issues (https://github.com/schochastics/networkdata/issues/)
#' @source https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/T4HBA3
#'
#' https://www.imdb.com/title/tt0488478
#' @references Kaminski, Jermain; Schober, Michael; Albaladejo, Raymond; Zastupailo, Oleksandr; Hidalgo, César, 2018, Moviegalaxies - Social Networks in Movies, https://doi.org/10.7910/DVN/T4HBA3, Harvard Dataverse, V3
"movie_2"
#'Twelve and Holding
#' @description Interactions of characters in the movie "Twelve and Holding" (2005)
#' @format igraph object
#' @details The networks were built with a movie script parser. Even after multiple manual checks, the data set can still contain minor errors (e.g. typos in character names or wrongly parsed names). This may require some additional manual checks before using the data. Please report any such issues (https://github.com/schochastics/networkdata/issues/)
#' @source https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/T4HBA3
#'
#' https://www.imdb.com/title/tt0417385
#' @references Kaminski, Jermain; Schober, Michael; Albaladejo, Raymond; Zastupailo, Oleksandr; Hidalgo, César, 2018, Moviegalaxies - Social Networks in Movies, https://doi.org/10.7910/DVN/T4HBA3, Harvard Dataverse, V3
"movie_3"
#'127 Hours
#' @description Interactions of characters in the movie "127 Hours" (2010)
#' @format igraph object
#' @details The networks were built with a movie script parser. Even after multiple manual checks, the data set can still contain minor errors (e.g. typos in character names or wrongly parsed names). This may require some additional manual checks before using the data. Please report any such issues (https://github.com/schochastics/networkdata/issues/)
#' @source https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/T4HBA3
#'
#' https://www.imdb.com/title/tt1542344
#' @references Kaminski, Jermain; Schober, Michael; Albaladejo, Raymond; Zastupailo, Oleksandr; Hidalgo, César, 2018, Moviegalaxies - Social Networks in Movies, https://doi.org/10.7910/DVN/T4HBA3, Harvard Dataverse, V3
"movie_4"
#'1492: Conquest of Paradise
#' @description Interactions of characters in the movie "1492: Conquest of Paradise" (1992)
#' @format igraph object
#' @details The networks were built with a movie script parser. Even after multiple manual checks, the data set can still contain minor errors (e.g. typos in character names or wrongly parsed names). This may require some additional manual checks before using the data. Please report any such issues (https://github.com/schochastics/networkdata/issues/)
#' @source https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/T4HBA3
#'
#' https://www.imdb.com/title/tt0103594
#' @references Kaminski, Jermain; Schober, Michael; Albaladejo, Raymond; Zastupailo, Oleksandr; Hidalgo, César, 2018, Moviegalaxies - Social Networks in Movies, https://doi.org/10.7910/DVN/T4HBA3, Harvard Dataverse, V3
"movie_5"
#'15 Minutes
#' @description Interactions of characters in the movie "15 Minutes" (2001)
#' @format igraph object
#' @details The networks were built with a movie script parser. Even after multiple manual checks, the data set can still contain minor errors (e.g. typos in character names or wrongly parsed names). This may require some additional manual checks before using the data. Please report any such issues (https://github.com/schochastics/networkdata/issues/)
#' @source https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/T4HBA3
#'
#' https://www.imdb.com/title/t
gitextract_wgt_nhtu/
├── .Rbuildignore
├── .github/
│ ├── .gitignore
│ └── workflows/
│ └── pkgdown.yaml
├── .gitignore
├── DESCRIPTION
├── LICENSE
├── LICENSE.md
├── NAMESPACE
├── NEWS.md
├── R/
│ ├── data-animals.R
│ ├── data-covert.R
│ ├── data-freeman.R
│ ├── data-konnect.R
│ ├── data-misc.R
│ ├── data-movie.R
│ ├── data-shakespeare.R
│ └── networkdata.R
├── README.Rmd
├── README.md
├── _pkgdown.yml
├── data/
│ ├── adjnoun.rda
│ ├── animal_1.rda
│ ├── animal_10.rda
│ ├── animal_11.rda
│ ├── animal_12.rda
│ ├── animal_13.rda
│ ├── animal_14.rda
│ ├── animal_15.rda
│ ├── animal_16.rda
│ ├── animal_17.rda
│ ├── animal_18.rda
│ ├── animal_19.rda
│ ├── animal_2.rda
│ ├── animal_20.rda
│ ├── animal_21.rda
│ ├── animal_22.rda
│ ├── animal_23.rda
│ ├── animal_24.rda
│ ├── animal_25.rda
│ ├── animal_26.rda
│ ├── animal_27.rda
│ ├── animal_28.rda
│ ├── animal_29.rda
│ ├── animal_3.rda
│ ├── animal_30.rda
│ ├── animal_31.rda
│ ├── animal_32.rda
│ ├── animal_33.rda
│ ├── animal_34.rda
│ ├── animal_35.rda
│ ├── animal_36.rda
│ ├── animal_4.rda
│ ├── animal_5.rda
│ ├── animal_6.rda
│ ├── animal_7.rda
│ ├── animal_8.rda
│ ├── animal_9.rda
│ ├── ants_1.rda
│ ├── ants_2.rda
│ ├── arenas_email.rda
│ ├── arenas_meta.rda
│ ├── atp.rda
│ ├── bible.rda
│ ├── bkfrab.rda
│ ├── bkfrac.rda
│ ├── bkoffb.rda
│ ├── bkoffc.rda
│ ├── bktecb.rda
│ ├── bktecc.rda
│ ├── bott.rda
│ ├── brunson_club_membership.rda
│ ├── brunson_corporate_leadership.rda
│ ├── brunson_revolution.rda
│ ├── brunson_south_africa.rda
│ ├── cent_lit.rda
│ ├── ceos_clubs.rda
│ ├── chicagoroad.rda
│ ├── clique_graph.rda
│ ├── coleman.rda
│ ├── core_graph.rda
│ ├── cosponsor.rda
│ ├── covert_1.rda
│ ├── covert_10.rda
│ ├── covert_11.rda
│ ├── covert_12.rda
│ ├── covert_13.rda
│ ├── covert_14.rda
│ ├── covert_15.rda
│ ├── covert_16.rda
│ ├── covert_17.rda
│ ├── covert_18.rda
│ ├── covert_19.rda
│ ├── covert_2.rda
│ ├── covert_20.rda
│ ├── covert_21.rda
│ ├── covert_22.rda
│ ├── covert_23.rda
│ ├── covert_24.rda
│ ├── covert_25.rda
│ ├── covert_26.rda
│ ├── covert_27.rda
│ ├── covert_28.rda
│ ├── covert_29.rda
│ ├── covert_3.rda
│ ├── covert_30.rda
│ ├── covert_31.rda
│ ├── covert_32.rda
│ ├── covert_33.rda
│ ├── covert_34.rda
│ ├── covert_35.rda
│ ├── covert_36.rda
│ ├── covert_37.rda
│ ├── covert_38.rda
│ ├── covert_39.rda
│ ├── covert_4.rda
│ ├── covert_40.rda
│ ├── covert_41.rda
│ ├── covert_42.rda
│ ├── covert_43.rda
│ ├── covert_44.rda
│ ├── covert_45.rda
│ ├── covert_46.rda
│ ├── covert_47.rda
│ ├── covert_6.rda
│ ├── covert_7.rda
│ ├── covert_8.rda
│ ├── covert_9.rda
│ ├── crime.rda
│ ├── dnc_corecipient.rda
│ ├── dnc_temporalGraph.rda
│ ├── dolphins_1.rda
│ ├── dolphins_2.rda
│ ├── eies_messages.rda
│ ├── eies_relations.rda
│ ├── euroroad.rda
│ ├── f2f_hypertext.rda
│ ├── f2f_infectious.rda
│ ├── ffe_elite.rda
│ ├── ffe_friends.rda
│ ├── ffe_influence.rda
│ ├── flo_business.rda
│ ├── flo_marriage.rda
│ ├── football_triad.rda
│ ├── fraternity.rda
│ ├── giraffe.rda
│ ├── glasgow129.rda
│ ├── got.rda
│ ├── greys.rda
│ ├── gss_egor.rda
│ ├── hall.rda
│ ├── hens.rda
│ ├── highschool_boys.rda
│ ├── ht_advice.rda
│ ├── ht_friends.rda
│ ├── ht_reports.rda
│ ├── insna.rda
│ ├── jazz.rda
│ ├── jpr.rda
│ ├── kangaroo.rda
│ ├── karate.rda
│ ├── karate_weight.rda
│ ├── knecht.rda
│ ├── law_advice.rda
│ ├── law_cowork.rda
│ ├── law_friends.rda
│ ├── literary.rda
│ ├── maayan_faa.rda
│ ├── maayan_pdzbase.rda
│ ├── macaque.rda
│ ├── mine.rda
│ ├── miserables.rda
│ ├── movie_1.rda
│ ├── movie_10.rda
│ ├── movie_100.rda
│ ├── movie_101.rda
│ ├── movie_102.rda
│ ├── movie_103.rda
│ ├── movie_104.rda
│ ├── movie_105.rda
│ ├── movie_106.rda
│ ├── movie_107.rda
│ ├── movie_108.rda
│ ├── movie_109.rda
│ ├── movie_11.rda
│ ├── movie_110.rda
│ ├── movie_111.rda
│ ├── movie_112.rda
│ ├── movie_113.rda
│ ├── movie_114.rda
│ ├── movie_115.rda
│ ├── movie_116.rda
│ ├── movie_117.rda
│ ├── movie_118.rda
│ ├── movie_119.rda
│ ├── movie_12.rda
│ ├── movie_120.rda
│ ├── movie_121.rda
│ ├── movie_122.rda
│ ├── movie_123.rda
│ ├── movie_124.rda
│ ├── movie_125.rda
│ ├── movie_126.rda
│ ├── movie_127.rda
│ ├── movie_128.rda
│ ├── movie_129.rda
│ ├── movie_13.rda
│ ├── movie_130.rda
│ ├── movie_131.rda
│ ├── movie_132.rda
│ ├── movie_133.rda
│ ├── movie_134.rda
│ ├── movie_135.rda
│ ├── movie_136.rda
│ ├── movie_137.rda
│ ├── movie_138.rda
│ ├── movie_139.rda
│ ├── movie_14.rda
│ ├── movie_140.rda
│ ├── movie_141.rda
│ ├── movie_142.rda
│ ├── movie_143.rda
│ ├── movie_144.rda
│ ├── movie_145.rda
│ ├── movie_146.rda
│ ├── movie_147.rda
│ ├── movie_148.rda
│ ├── movie_149.rda
│ ├── movie_15.rda
│ ├── movie_150.rda
│ ├── movie_151.rda
│ ├── movie_152.rda
│ ├── movie_153.rda
│ ├── movie_154.rda
│ ├── movie_155.rda
│ ├── movie_156.rda
│ ├── movie_157.rda
│ ├── movie_158.rda
│ ├── movie_159.rda
│ ├── movie_16.rda
│ ├── movie_160.rda
│ ├── movie_161.rda
│ ├── movie_162.rda
│ ├── movie_163.rda
│ ├── movie_164.rda
│ ├── movie_165.rda
│ ├── movie_166.rda
│ ├── movie_167.rda
│ ├── movie_168.rda
│ ├── movie_169.rda
│ ├── movie_17.rda
│ ├── movie_170.rda
│ ├── movie_171.rda
│ ├── movie_172.rda
│ ├── movie_173.rda
│ ├── movie_174.rda
│ ├── movie_175.rda
│ ├── movie_176.rda
│ ├── movie_177.rda
│ ├── movie_178.rda
│ ├── movie_179.rda
│ ├── movie_18.rda
│ ├── movie_180.rda
│ ├── movie_181.rda
│ ├── movie_182.rda
│ ├── movie_183.rda
│ ├── movie_184.rda
│ ├── movie_185.rda
│ ├── movie_186.rda
│ ├── movie_187.rda
│ ├── movie_188.rda
│ ├── movie_189.rda
│ ├── movie_19.rda
│ ├── movie_190.rda
│ ├── movie_191.rda
│ ├── movie_192.rda
│ ├── movie_193.rda
│ ├── movie_194.rda
│ ├── movie_195.rda
│ ├── movie_196.rda
│ ├── movie_197.rda
│ ├── movie_198.rda
│ ├── movie_199.rda
│ ├── movie_2.rda
│ ├── movie_20.rda
│ ├── movie_200.rda
│ ├── movie_201.rda
│ ├── movie_202.rda
│ ├── movie_203.rda
│ ├── movie_204.rda
│ ├── movie_205.rda
│ ├── movie_206.rda
│ ├── movie_207.rda
│ ├── movie_208.rda
│ ├── movie_209.rda
│ ├── movie_21.rda
│ ├── movie_210.rda
│ ├── movie_211.rda
│ ├── movie_212.rda
│ ├── movie_213.rda
│ ├── movie_214.rda
│ ├── movie_215.rda
│ ├── movie_216.rda
│ ├── movie_217.rda
│ ├── movie_218.rda
│ ├── movie_219.rda
│ ├── movie_22.rda
│ ├── movie_220.rda
│ ├── movie_221.rda
│ ├── movie_222.rda
│ ├── movie_223.rda
│ ├── movie_224.rda
│ ├── movie_225.rda
│ ├── movie_226.rda
│ ├── movie_227.rda
│ ├── movie_228.rda
│ ├── movie_229.rda
│ ├── movie_23.rda
│ ├── movie_230.rda
│ ├── movie_231.rda
│ ├── movie_232.rda
│ ├── movie_233.rda
│ ├── movie_234.rda
│ ├── movie_235.rda
│ ├── movie_236.rda
│ ├── movie_237.rda
│ ├── movie_238.rda
│ ├── movie_239.rda
│ ├── movie_24.rda
│ ├── movie_240.rda
│ ├── movie_241.rda
│ ├── movie_242.rda
│ ├── movie_243.rda
│ ├── movie_244.rda
│ ├── movie_245.rda
│ ├── movie_246.rda
│ ├── movie_247.rda
│ ├── movie_248.rda
│ ├── movie_249.rda
│ ├── movie_25.rda
│ ├── movie_250.rda
│ ├── movie_251.rda
│ ├── movie_252.rda
│ ├── movie_253.rda
│ ├── movie_254.rda
│ ├── movie_255.rda
│ ├── movie_256.rda
│ ├── movie_257.rda
│ ├── movie_258.rda
│ ├── movie_259.rda
│ ├── movie_26.rda
│ ├── movie_260.rda
│ ├── movie_261.rda
│ ├── movie_262.rda
│ ├── movie_263.rda
│ ├── movie_264.rda
│ ├── movie_265.rda
│ ├── movie_266.rda
│ ├── movie_267.rda
│ ├── movie_268.rda
│ ├── movie_269.rda
│ ├── movie_27.rda
│ ├── movie_270.rda
│ ├── movie_271.rda
│ ├── movie_272.rda
│ ├── movie_273.rda
│ ├── movie_274.rda
│ ├── movie_275.rda
│ ├── movie_276.rda
│ ├── movie_277.rda
│ ├── movie_278.rda
│ ├── movie_279.rda
│ ├── movie_28.rda
│ ├── movie_280.rda
│ ├── movie_281.rda
│ ├── movie_282.rda
│ ├── movie_283.rda
│ ├── movie_284.rda
│ ├── movie_285.rda
│ ├── movie_286.rda
│ ├── movie_287.rda
│ ├── movie_288.rda
│ ├── movie_289.rda
│ ├── movie_29.rda
│ ├── movie_290.rda
│ ├── movie_291.rda
│ ├── movie_292.rda
│ ├── movie_293.rda
│ ├── movie_294.rda
│ ├── movie_295.rda
│ ├── movie_296.rda
│ ├── movie_297.rda
│ ├── movie_298.rda
│ ├── movie_299.rda
│ ├── movie_3.rda
│ ├── movie_30.rda
│ ├── movie_300.rda
│ ├── movie_301.rda
│ ├── movie_302.rda
│ ├── movie_303.rda
│ ├── movie_304.rda
│ ├── movie_305.rda
│ ├── movie_306.rda
│ ├── movie_307.rda
│ ├── movie_308.rda
│ ├── movie_309.rda
│ ├── movie_31.rda
│ ├── movie_310.rda
│ ├── movie_311.rda
│ ├── movie_312.rda
│ ├── movie_313.rda
│ ├── movie_314.rda
│ ├── movie_315.rda
│ ├── movie_316.rda
│ ├── movie_317.rda
│ ├── movie_318.rda
│ ├── movie_319.rda
│ ├── movie_32.rda
│ ├── movie_320.rda
│ ├── movie_321.rda
│ ├── movie_322.rda
│ ├── movie_323.rda
│ ├── movie_324.rda
│ ├── movie_325.rda
│ ├── movie_326.rda
│ ├── movie_327.rda
│ ├── movie_328.rda
│ ├── movie_329.rda
│ ├── movie_33.rda
│ ├── movie_330.rda
│ ├── movie_331.rda
│ ├── movie_332.rda
│ ├── movie_333.rda
│ ├── movie_334.rda
│ ├── movie_335.rda
│ ├── movie_336.rda
│ ├── movie_337.rda
│ ├── movie_338.rda
│ ├── movie_339.rda
│ ├── movie_34.rda
│ ├── movie_340.rda
│ ├── movie_341.rda
│ ├── movie_342.rda
│ ├── movie_343.rda
│ ├── movie_344.rda
│ ├── movie_345.rda
│ ├── movie_346.rda
│ ├── movie_347.rda
│ ├── movie_348.rda
│ ├── movie_349.rda
│ ├── movie_35.rda
│ ├── movie_350.rda
│ ├── movie_351.rda
│ ├── movie_352.rda
│ ├── movie_353.rda
│ ├── movie_354.rda
│ ├── movie_355.rda
│ ├── movie_356.rda
│ ├── movie_357.rda
│ ├── movie_358.rda
│ ├── movie_359.rda
│ ├── movie_36.rda
│ ├── movie_360.rda
│ ├── movie_361.rda
│ ├── movie_362.rda
│ ├── movie_363.rda
│ ├── movie_364.rda
│ ├── movie_365.rda
│ ├── movie_366.rda
│ ├── movie_367.rda
│ ├── movie_368.rda
│ ├── movie_369.rda
│ ├── movie_37.rda
│ ├── movie_370.rda
│ ├── movie_371.rda
│ ├── movie_372.rda
│ ├── movie_373.rda
│ ├── movie_374.rda
│ ├── movie_375.rda
│ ├── movie_376.rda
│ ├── movie_377.rda
│ ├── movie_378.rda
│ ├── movie_379.rda
│ ├── movie_38.rda
│ ├── movie_380.rda
│ ├── movie_381.rda
│ ├── movie_382.rda
│ ├── movie_383.rda
│ ├── movie_384.rda
│ ├── movie_385.rda
│ ├── movie_386.rda
│ ├── movie_387.rda
│ ├── movie_388.rda
│ ├── movie_389.rda
│ ├── movie_39.rda
│ ├── movie_390.rda
│ ├── movie_391.rda
│ ├── movie_392.rda
│ ├── movie_393.rda
│ ├── movie_394.rda
│ ├── movie_395.rda
│ ├── movie_396.rda
│ ├── movie_397.rda
│ ├── movie_398.rda
│ ├── movie_399.rda
│ ├── movie_4.rda
│ ├── movie_40.rda
│ ├── movie_400.rda
│ ├── movie_401.rda
│ ├── movie_402.rda
│ ├── movie_403.rda
│ ├── movie_404.rda
│ ├── movie_405.rda
│ ├── movie_406.rda
│ ├── movie_407.rda
│ ├── movie_408.rda
│ ├── movie_409.rda
│ ├── movie_41.rda
│ ├── movie_410.rda
│ ├── movie_411.rda
│ ├── movie_412.rda
│ ├── movie_413.rda
│ ├── movie_414.rda
│ ├── movie_415.rda
│ ├── movie_416.rda
│ ├── movie_417.rda
│ ├── movie_418.rda
│ ├── movie_419.rda
│ ├── movie_42.rda
│ ├── movie_420.rda
│ ├── movie_421.rda
│ ├── movie_422.rda
│ ├── movie_423.rda
│ ├── movie_424.rda
│ ├── movie_425.rda
│ ├── movie_426.rda
│ ├── movie_427.rda
│ ├── movie_428.rda
│ ├── movie_429.rda
│ ├── movie_43.rda
│ ├── movie_430.rda
│ ├── movie_431.rda
│ ├── movie_432.rda
│ ├── movie_433.rda
│ ├── movie_434.rda
│ ├── movie_435.rda
│ ├── movie_436.rda
│ ├── movie_437.rda
│ ├── movie_438.rda
│ ├── movie_439.rda
│ ├── movie_44.rda
│ ├── movie_440.rda
│ ├── movie_441.rda
│ ├── movie_442.rda
│ ├── movie_443.rda
│ ├── movie_444.rda
│ ├── movie_445.rda
│ ├── movie_446.rda
│ ├── movie_447.rda
│ ├── movie_448.rda
│ ├── movie_449.rda
│ ├── movie_45.rda
│ ├── movie_450.rda
│ ├── movie_451.rda
│ ├── movie_452.rda
│ ├── movie_453.rda
│ ├── movie_454.rda
│ ├── movie_455.rda
│ ├── movie_456.rda
│ ├── movie_457.rda
│ ├── movie_458.rda
│ ├── movie_459.rda
│ ├── movie_46.rda
│ ├── movie_460.rda
│ ├── movie_461.rda
│ ├── movie_462.rda
│ ├── movie_463.rda
│ ├── movie_464.rda
│ ├── movie_465.rda
│ ├── movie_466.rda
│ ├── movie_467.rda
│ ├── movie_468.rda
│ ├── movie_469.rda
│ ├── movie_47.rda
│ ├── movie_470.rda
│ ├── movie_471.rda
│ ├── movie_472.rda
│ ├── movie_473.rda
│ ├── movie_474.rda
│ ├── movie_475.rda
│ ├── movie_476.rda
│ ├── movie_477.rda
│ ├── movie_478.rda
│ ├── movie_479.rda
│ ├── movie_48.rda
│ ├── movie_480.rda
│ ├── movie_481.rda
│ ├── movie_482.rda
│ ├── movie_483.rda
│ ├── movie_484.rda
│ ├── movie_485.rda
│ ├── movie_486.rda
│ ├── movie_487.rda
│ ├── movie_488.rda
│ ├── movie_489.rda
│ ├── movie_49.rda
│ ├── movie_490.rda
│ ├── movie_491.rda
│ ├── movie_492.rda
│ ├── movie_493.rda
│ ├── movie_494.rda
│ ├── movie_495.rda
│ ├── movie_496.rda
│ ├── movie_497.rda
│ ├── movie_498.rda
│ ├── movie_499.rda
│ ├── movie_5.rda
│ ├── movie_50.rda
│ ├── movie_500.rda
│ ├── movie_501.rda
│ ├── movie_502.rda
│ ├── movie_503.rda
│ ├── movie_504.rda
│ ├── movie_505.rda
│ ├── movie_506.rda
│ ├── movie_507.rda
│ ├── movie_508.rda
│ ├── movie_509.rda
│ ├── movie_51.rda
│ ├── movie_510.rda
│ ├── movie_511.rda
│ ├── movie_512.rda
│ ├── movie_513.rda
│ ├── movie_514.rda
│ ├── movie_515.rda
│ ├── movie_516.rda
│ ├── movie_517.rda
│ ├── movie_518.rda
│ ├── movie_519.rda
│ ├── movie_52.rda
│ ├── movie_520.rda
│ ├── movie_521.rda
│ ├── movie_522.rda
│ ├── movie_523.rda
│ ├── movie_524.rda
│ ├── movie_525.rda
│ ├── movie_526.rda
│ ├── movie_527.rda
│ ├── movie_528.rda
│ ├── movie_529.rda
│ ├── movie_53.rda
│ ├── movie_530.rda
│ ├── movie_531.rda
│ ├── movie_532.rda
│ ├── movie_533.rda
│ ├── movie_534.rda
│ ├── movie_535.rda
│ ├── movie_536.rda
│ ├── movie_537.rda
│ ├── movie_538.rda
│ ├── movie_539.rda
│ ├── movie_54.rda
│ ├── movie_540.rda
│ ├── movie_541.rda
│ ├── movie_542.rda
│ ├── movie_543.rda
│ ├── movie_544.rda
│ ├── movie_545.rda
│ ├── movie_546.rda
│ ├── movie_547.rda
│ ├── movie_548.rda
│ ├── movie_549.rda
│ ├── movie_55.rda
│ ├── movie_550.rda
│ ├── movie_551.rda
│ ├── movie_552.rda
│ ├── movie_553.rda
│ ├── movie_554.rda
│ ├── movie_555.rda
│ ├── movie_556.rda
│ ├── movie_557.rda
│ ├── movie_558.rda
│ ├── movie_559.rda
│ ├── movie_56.rda
│ ├── movie_560.rda
│ ├── movie_561.rda
│ ├── movie_562.rda
│ ├── movie_563.rda
│ ├── movie_564.rda
│ ├── movie_565.rda
│ ├── movie_566.rda
│ ├── movie_567.rda
│ ├── movie_568.rda
│ ├── movie_569.rda
│ ├── movie_57.rda
│ ├── movie_570.rda
│ ├── movie_571.rda
│ ├── movie_572.rda
│ ├── movie_573.rda
│ ├── movie_574.rda
│ ├── movie_575.rda
│ ├── movie_576.rda
│ ├── movie_577.rda
│ ├── movie_578.rda
│ ├── movie_579.rda
│ ├── movie_58.rda
│ ├── movie_580.rda
│ ├── movie_581.rda
│ ├── movie_582.rda
│ ├── movie_583.rda
│ ├── movie_584.rda
│ ├── movie_585.rda
│ ├── movie_586.rda
│ ├── movie_587.rda
│ ├── movie_588.rda
│ ├── movie_589.rda
│ ├── movie_59.rda
│ ├── movie_590.rda
│ ├── movie_591.rda
│ ├── movie_592.rda
│ ├── movie_593.rda
│ ├── movie_594.rda
│ ├── movie_595.rda
│ ├── movie_596.rda
│ ├── movie_597.rda
│ ├── movie_598.rda
│ ├── movie_599.rda
│ ├── movie_6.rda
│ ├── movie_60.rda
│ ├── movie_600.rda
│ ├── movie_601.rda
│ ├── movie_602.rda
│ ├── movie_603.rda
│ ├── movie_604.rda
│ ├── movie_605.rda
│ ├── movie_606.rda
│ ├── movie_607.rda
│ ├── movie_608.rda
│ ├── movie_609.rda
│ ├── movie_61.rda
│ ├── movie_610.rda
│ ├── movie_611.rda
│ ├── movie_612.rda
│ ├── movie_613.rda
│ ├── movie_614.rda
│ ├── movie_615.rda
│ ├── movie_616.rda
│ ├── movie_617.rda
│ ├── movie_618.rda
│ ├── movie_619.rda
│ ├── movie_62.rda
│ ├── movie_620.rda
│ ├── movie_621.rda
│ ├── movie_622.rda
│ ├── movie_623.rda
│ ├── movie_624.rda
│ ├── movie_625.rda
│ ├── movie_626.rda
│ ├── movie_627.rda
│ ├── movie_628.rda
│ ├── movie_629.rda
│ ├── movie_63.rda
│ ├── movie_630.rda
│ ├── movie_631.rda
│ ├── movie_632.rda
│ ├── movie_633.rda
│ ├── movie_634.rda
│ ├── movie_635.rda
│ ├── movie_636.rda
│ ├── movie_637.rda
│ ├── movie_638.rda
│ ├── movie_639.rda
│ ├── movie_64.rda
│ ├── movie_640.rda
│ ├── movie_641.rda
│ ├── movie_642.rda
│ ├── movie_643.rda
│ ├── movie_644.rda
│ ├── movie_645.rda
│ ├── movie_646.rda
│ ├── movie_647.rda
│ ├── movie_648.rda
│ ├── movie_649.rda
│ ├── movie_65.rda
│ ├── movie_650.rda
│ ├── movie_651.rda
│ ├── movie_652.rda
│ ├── movie_653.rda
│ ├── movie_654.rda
│ ├── movie_655.rda
│ ├── movie_656.rda
│ ├── movie_657.rda
│ ├── movie_658.rda
│ ├── movie_659.rda
│ ├── movie_66.rda
│ ├── movie_660.rda
│ ├── movie_661.rda
│ ├── movie_662.rda
│ ├── movie_663.rda
│ ├── movie_664.rda
│ ├── movie_665.rda
│ ├── movie_666.rda
│ ├── movie_667.rda
│ ├── movie_668.rda
│ ├── movie_669.rda
│ ├── movie_67.rda
│ ├── movie_670.rda
│ ├── movie_671.rda
│ ├── movie_672.rda
│ ├── movie_673.rda
│ ├── movie_674.rda
│ ├── movie_675.rda
│ ├── movie_676.rda
│ ├── movie_677.rda
│ ├── movie_678.rda
│ ├── movie_679.rda
│ ├── movie_68.rda
│ ├── movie_680.rda
│ ├── movie_681.rda
│ ├── movie_682.rda
│ ├── movie_683.rda
│ ├── movie_684.rda
│ ├── movie_685.rda
│ ├── movie_686.rda
│ ├── movie_687.rda
│ ├── movie_688.rda
│ ├── movie_689.rda
│ ├── movie_69.rda
│ ├── movie_690.rda
│ ├── movie_691.rda
│ ├── movie_692.rda
│ ├── movie_693.rda
│ ├── movie_694.rda
│ ├── movie_695.rda
│ ├── movie_696.rda
│ ├── movie_697.rda
│ ├── movie_698.rda
│ ├── movie_699.rda
│ ├── movie_7.rda
│ ├── movie_70.rda
│ ├── movie_700.rda
│ ├── movie_701.rda
│ ├── movie_702.rda
│ ├── movie_703.rda
│ ├── movie_704.rda
│ ├── movie_705.rda
│ ├── movie_706.rda
│ ├── movie_707.rda
│ ├── movie_708.rda
│ ├── movie_709.rda
│ ├── movie_71.rda
│ ├── movie_710.rda
│ ├── movie_711.rda
│ ├── movie_712.rda
│ ├── movie_713.rda
│ ├── movie_714.rda
│ ├── movie_715.rda
│ ├── movie_716.rda
│ ├── movie_717.rda
│ ├── movie_718.rda
│ ├── movie_719.rda
│ ├── movie_72.rda
│ ├── movie_720.rda
│ ├── movie_721.rda
│ ├── movie_722.rda
│ ├── movie_723.rda
│ ├── movie_724.rda
│ ├── movie_725.rda
│ ├── movie_726.rda
│ ├── movie_727.rda
│ ├── movie_728.rda
│ ├── movie_729.rda
│ ├── movie_73.rda
│ ├── movie_730.rda
│ ├── movie_731.rda
│ ├── movie_732.rda
│ ├── movie_733.rda
│ ├── movie_734.rda
│ ├── movie_735.rda
│ ├── movie_736.rda
│ ├── movie_737.rda
│ ├── movie_738.rda
│ ├── movie_739.rda
│ ├── movie_74.rda
│ ├── movie_740.rda
│ ├── movie_741.rda
│ ├── movie_742.rda
│ ├── movie_743.rda
│ ├── movie_744.rda
│ ├── movie_745.rda
│ ├── movie_746.rda
│ ├── movie_747.rda
│ ├── movie_748.rda
│ ├── movie_749.rda
│ ├── movie_75.rda
│ ├── movie_750.rda
│ ├── movie_751.rda
│ ├── movie_752.rda
│ ├── movie_753.rda
│ ├── movie_754.rda
│ ├── movie_755.rda
│ ├── movie_756.rda
│ ├── movie_757.rda
│ ├── movie_758.rda
│ ├── movie_759.rda
│ ├── movie_76.rda
│ ├── movie_760.rda
│ ├── movie_761.rda
│ ├── movie_762.rda
│ ├── movie_763.rda
│ ├── movie_764.rda
│ ├── movie_765.rda
│ ├── movie_766.rda
│ ├── movie_767.rda
│ ├── movie_768.rda
│ ├── movie_769.rda
│ ├── movie_77.rda
│ ├── movie_770.rda
│ ├── movie_771.rda
│ ├── movie_772.rda
│ ├── movie_773.rda
│ ├── movie_78.rda
│ ├── movie_79.rda
│ ├── movie_8.rda
│ ├── movie_80.rda
│ ├── movie_81.rda
│ ├── movie_82.rda
│ ├── movie_83.rda
│ ├── movie_84.rda
│ ├── movie_85.rda
│ ├── movie_86.rda
│ ├── movie_87.rda
│ ├── movie_88.rda
│ ├── movie_89.rda
│ ├── movie_9.rda
│ ├── movie_90.rda
│ ├── movie_91.rda
│ ├── movie_92.rda
│ ├── movie_93.rda
│ ├── movie_94.rda
│ ├── movie_95.rda
│ ├── movie_96.rda
│ ├── movie_97.rda
│ ├── movie_98.rda
│ ├── movie_99.rda
│ ├── netsci.rda
│ ├── petster.rda
│ ├── physicians.rda
│ ├── polblogs.rda
│ ├── polbooks.rda
│ ├── pony.rda
│ ├── powergrid.rda
│ ├── protein.rda
│ ├── radoslaw_email.rda
│ ├── rhesus.rda
│ ├── s50.rda
│ ├── sampson.rda
│ ├── shakespeare_1.rda
│ ├── shakespeare_10.rda
│ ├── shakespeare_11.rda
│ ├── shakespeare_12.rda
│ ├── shakespeare_13.rda
│ ├── shakespeare_14.rda
│ ├── shakespeare_15.rda
│ ├── shakespeare_16.rda
│ ├── shakespeare_17.rda
│ ├── shakespeare_18.rda
│ ├── shakespeare_19.rda
│ ├── shakespeare_2.rda
│ ├── shakespeare_20.rda
│ ├── shakespeare_21.rda
│ ├── shakespeare_22.rda
│ ├── shakespeare_23.rda
│ ├── shakespeare_24.rda
│ ├── shakespeare_25.rda
│ ├── shakespeare_26.rda
│ ├── shakespeare_27.rda
│ ├── shakespeare_28.rda
│ ├── shakespeare_29.rda
│ ├── shakespeare_3.rda
│ ├── shakespeare_30.rda
│ ├── shakespeare_31.rda
│ ├── shakespeare_32.rda
│ ├── shakespeare_33.rda
│ ├── shakespeare_34.rda
│ ├── shakespeare_35.rda
│ ├── shakespeare_36.rda
│ ├── shakespeare_4.rda
│ ├── shakespeare_5.rda
│ ├── shakespeare_6.rda
│ ├── shakespeare_7.rda
│ ├── shakespeare_8.rda
│ ├── shakespeare_9.rda
│ ├── sheep.rda
│ ├── sn_auth.rda
│ ├── southern_women.rda
│ ├── starwars.rda
│ ├── surfersb.rda
│ ├── surfersc.rda
│ ├── tailor_social.rda
│ ├── tailor_work.rda
│ ├── taro.rda
│ ├── train.rda
│ ├── ucforum.rda
│ ├── ucsocial.rda
│ ├── unicodelang.rda
│ ├── us_flights.rda
│ ├── usa_borders.rda
│ ├── usflights.rda
│ ├── wiring.rda
│ └── wta.rda
├── data-raw/
│ ├── glasgow129.R
│ └── gss.R
├── inst/
│ └── CITATION
└── man/
├── adjnoun.Rd
├── animal_1.Rd
├── animal_10.Rd
├── animal_11.Rd
├── animal_12.Rd
├── animal_13.Rd
├── animal_14.Rd
├── animal_15.Rd
├── animal_16.Rd
├── animal_17.Rd
├── animal_18.Rd
├── animal_19.Rd
├── animal_2.Rd
├── animal_20.Rd
├── animal_21.Rd
├── animal_22.Rd
├── animal_23.Rd
├── animal_24.Rd
├── animal_25.Rd
├── animal_26.Rd
├── animal_27.Rd
├── animal_28.Rd
├── animal_29.Rd
├── animal_3.Rd
├── animal_30.Rd
├── animal_31.Rd
├── animal_32.Rd
├── animal_33.Rd
├── animal_34.Rd
├── animal_35.Rd
├── animal_36.Rd
├── animal_4.Rd
├── animal_5.Rd
├── animal_6.Rd
├── animal_7.Rd
├── animal_8.Rd
├── animal_9.Rd
├── ants_1.Rd
├── ants_2.Rd
├── arenas_email.Rd
├── arenas_meta.Rd
├── atp.Rd
├── bible.Rd
├── bkfrab.Rd
├── bkfrac.Rd
├── bkoffb.Rd
├── bkoffc.Rd
├── bktecb.Rd
├── bktecc.Rd
├── bott.Rd
├── brunson_club_membership.Rd
├── brunson_corporate_leadership.Rd
├── brunson_revolution.Rd
├── brunson_south_africa.Rd
├── cent_lit.Rd
├── ceos_clubs.Rd
├── chicagoroad.Rd
├── clique_graph.Rd
├── coleman.Rd
├── core_graph.Rd
├── cosponsor.Rd
├── covert_1.Rd
├── covert_10.Rd
├── covert_11.Rd
├── covert_12.Rd
├── covert_13.Rd
├── covert_14.Rd
├── covert_15.Rd
├── covert_16.Rd
├── covert_17.Rd
├── covert_18.Rd
├── covert_19.Rd
├── covert_2.Rd
├── covert_20.Rd
├── covert_21.Rd
├── covert_22.Rd
├── covert_23.Rd
├── covert_24.Rd
├── covert_25.Rd
├── covert_26.Rd
├── covert_27.Rd
├── covert_28.Rd
├── covert_29.Rd
├── covert_3.Rd
├── covert_30.Rd
├── covert_31.Rd
├── covert_32.Rd
├── covert_33.Rd
├── covert_34.Rd
├── covert_35.Rd
├── covert_36.Rd
├── covert_37.Rd
├── covert_38.Rd
├── covert_39.Rd
├── covert_4.Rd
├── covert_40.Rd
├── covert_41.Rd
├── covert_42.Rd
├── covert_43.Rd
├── covert_44.Rd
├── covert_45.Rd
├── covert_46.Rd
├── covert_47.Rd
├── covert_6.Rd
├── covert_7.Rd
├── covert_8.Rd
├── covert_9.Rd
├── crime.Rd
├── dnc_corecipient.Rd
├── dnc_temporalGraph.Rd
├── dolphins_1.Rd
├── dolphins_2.Rd
├── eies_messages.Rd
├── eies_relations.Rd
├── euroroad.Rd
├── f2f_hypertext.Rd
├── f2f_infectious.Rd
├── ffe_elite.Rd
├── ffe_friends.Rd
├── ffe_influence.Rd
├── flo_business.Rd
├── flo_marriage.Rd
├── football_triad.Rd
├── fraternity.Rd
├── giraffe.Rd
├── glasgow129.Rd
├── got.Rd
├── greys.Rd
├── gss_egor.Rd
├── hall.Rd
├── hens.Rd
├── highschool_boys.Rd
├── ht_advice.Rd
├── ht_friends.Rd
├── ht_reports.Rd
├── insna.Rd
├── jazz.Rd
├── jpr.Rd
├── kangaroo.Rd
├── karate.Rd
├── karate_weight.Rd
├── knecht.Rd
├── law_advice.Rd
├── law_cowork.Rd
├── law_friends.Rd
├── literary.Rd
├── maayan_faa.Rd
├── maayan_pdzbase.Rd
├── macaque.Rd
├── mine.Rd
├── miserables.Rd
├── movie_1.Rd
├── movie_10.Rd
├── movie_100.Rd
├── movie_101.Rd
├── movie_102.Rd
├── movie_103.Rd
├── movie_104.Rd
├── movie_105.Rd
├── movie_106.Rd
├── movie_107.Rd
├── movie_108.Rd
├── movie_109.Rd
├── movie_11.Rd
├── movie_110.Rd
├── movie_111.Rd
├── movie_112.Rd
├── movie_113.Rd
├── movie_114.Rd
├── movie_115.Rd
├── movie_116.Rd
├── movie_117.Rd
├── movie_118.Rd
├── movie_119.Rd
├── movie_12.Rd
├── movie_120.Rd
├── movie_121.Rd
├── movie_122.Rd
├── movie_123.Rd
├── movie_124.Rd
├── movie_125.Rd
├── movie_126.Rd
├── movie_127.Rd
├── movie_128.Rd
├── movie_129.Rd
├── movie_13.Rd
├── movie_130.Rd
├── movie_131.Rd
├── movie_132.Rd
├── movie_133.Rd
├── movie_134.Rd
├── movie_135.Rd
├── movie_136.Rd
├── movie_137.Rd
├── movie_138.Rd
├── movie_139.Rd
├── movie_14.Rd
├── movie_140.Rd
├── movie_141.Rd
├── movie_142.Rd
├── movie_143.Rd
├── movie_144.Rd
├── movie_145.Rd
├── movie_146.Rd
├── movie_147.Rd
├── movie_148.Rd
├── movie_149.Rd
├── movie_15.Rd
├── movie_150.Rd
├── movie_151.Rd
├── movie_152.Rd
├── movie_153.Rd
├── movie_154.Rd
├── movie_155.Rd
├── movie_156.Rd
├── movie_157.Rd
├── movie_158.Rd
├── movie_159.Rd
├── movie_16.Rd
├── movie_160.Rd
├── movie_161.Rd
├── movie_162.Rd
├── movie_163.Rd
├── movie_164.Rd
├── movie_165.Rd
├── movie_166.Rd
├── movie_167.Rd
├── movie_168.Rd
├── movie_169.Rd
├── movie_17.Rd
├── movie_170.Rd
├── movie_171.Rd
├── movie_172.Rd
├── movie_173.Rd
├── movie_174.Rd
├── movie_175.Rd
├── movie_176.Rd
├── movie_177.Rd
├── movie_178.Rd
├── movie_179.Rd
├── movie_18.Rd
├── movie_180.Rd
├── movie_181.Rd
├── movie_182.Rd
├── movie_183.Rd
├── movie_184.Rd
├── movie_185.Rd
├── movie_186.Rd
├── movie_187.Rd
├── movie_188.Rd
├── movie_189.Rd
├── movie_19.Rd
├── movie_190.Rd
├── movie_191.Rd
├── movie_192.Rd
├── movie_193.Rd
├── movie_194.Rd
├── movie_195.Rd
├── movie_196.Rd
├── movie_197.Rd
├── movie_198.Rd
├── movie_199.Rd
├── movie_2.Rd
├── movie_20.Rd
├── movie_200.Rd
├── movie_201.Rd
├── movie_202.Rd
├── movie_203.Rd
├── movie_204.Rd
├── movie_205.Rd
├── movie_206.Rd
├── movie_207.Rd
├── movie_208.Rd
├── movie_209.Rd
├── movie_21.Rd
├── movie_210.Rd
├── movie_211.Rd
├── movie_212.Rd
├── movie_213.Rd
├── movie_214.Rd
├── movie_215.Rd
├── movie_216.Rd
├── movie_217.Rd
├── movie_218.Rd
├── movie_219.Rd
├── movie_22.Rd
├── movie_220.Rd
├── movie_221.Rd
├── movie_222.Rd
├── movie_223.Rd
├── movie_224.Rd
├── movie_225.Rd
├── movie_226.Rd
├── movie_227.Rd
├── movie_228.Rd
├── movie_229.Rd
├── movie_23.Rd
├── movie_230.Rd
├── movie_231.Rd
├── movie_232.Rd
├── movie_233.Rd
├── movie_234.Rd
├── movie_235.Rd
├── movie_236.Rd
├── movie_237.Rd
├── movie_238.Rd
├── movie_239.Rd
├── movie_24.Rd
├── movie_240.Rd
├── movie_241.Rd
├── movie_242.Rd
├── movie_243.Rd
├── movie_244.Rd
├── movie_245.Rd
├── movie_246.Rd
├── movie_247.Rd
├── movie_248.Rd
├── movie_249.Rd
├── movie_25.Rd
├── movie_250.Rd
├── movie_251.Rd
├── movie_252.Rd
├── movie_253.Rd
├── movie_254.Rd
├── movie_255.Rd
├── movie_256.Rd
├── movie_257.Rd
├── movie_258.Rd
├── movie_259.Rd
├── movie_26.Rd
├── movie_260.Rd
├── movie_261.Rd
├── movie_262.Rd
├── movie_263.Rd
├── movie_264.Rd
├── movie_265.Rd
├── movie_266.Rd
├── movie_267.Rd
├── movie_268.Rd
├── movie_269.Rd
├── movie_27.Rd
├── movie_270.Rd
├── movie_271.Rd
├── movie_272.Rd
├── movie_273.Rd
├── movie_274.Rd
├── movie_275.Rd
├── movie_276.Rd
├── movie_277.Rd
├── movie_278.Rd
├── movie_279.Rd
├── movie_28.Rd
├── movie_280.Rd
├── movie_281.Rd
├── movie_282.Rd
├── movie_283.Rd
├── movie_284.Rd
├── movie_285.Rd
├── movie_286.Rd
├── movie_287.Rd
├── movie_288.Rd
├── movie_289.Rd
├── movie_29.Rd
├── movie_290.Rd
├── movie_291.Rd
├── movie_292.Rd
├── movie_293.Rd
├── movie_294.Rd
├── movie_295.Rd
├── movie_296.Rd
├── movie_297.Rd
├── movie_298.Rd
├── movie_299.Rd
├── movie_3.Rd
├── movie_30.Rd
├── movie_300.Rd
├── movie_301.Rd
├── movie_302.Rd
├── movie_303.Rd
├── movie_304.Rd
├── movie_305.Rd
├── movie_306.Rd
├── movie_307.Rd
├── movie_308.Rd
├── movie_309.Rd
├── movie_31.Rd
├── movie_310.Rd
├── movie_311.Rd
├── movie_312.Rd
├── movie_313.Rd
├── movie_314.Rd
├── movie_315.Rd
├── movie_316.Rd
├── movie_317.Rd
├── movie_318.Rd
├── movie_319.Rd
├── movie_32.Rd
├── movie_320.Rd
├── movie_321.Rd
├── movie_322.Rd
├── movie_323.Rd
├── movie_324.Rd
├── movie_325.Rd
├── movie_326.Rd
├── movie_327.Rd
├── movie_328.Rd
├── movie_329.Rd
├── movie_33.Rd
├── movie_330.Rd
├── movie_331.Rd
├── movie_332.Rd
├── movie_333.Rd
├── movie_334.Rd
├── movie_335.Rd
├── movie_336.Rd
├── movie_337.Rd
├── movie_338.Rd
├── movie_339.Rd
├── movie_34.Rd
├── movie_340.Rd
├── movie_341.Rd
├── movie_342.Rd
├── movie_343.Rd
├── movie_344.Rd
├── movie_345.Rd
├── movie_346.Rd
├── movie_347.Rd
├── movie_348.Rd
├── movie_349.Rd
├── movie_35.Rd
├── movie_350.Rd
├── movie_351.Rd
├── movie_352.Rd
├── movie_353.Rd
├── movie_354.Rd
├── movie_355.Rd
├── movie_356.Rd
├── movie_357.Rd
├── movie_358.Rd
├── movie_359.Rd
├── movie_36.Rd
├── movie_360.Rd
├── movie_361.Rd
├── movie_362.Rd
├── movie_363.Rd
├── movie_364.Rd
├── movie_365.Rd
├── movie_366.Rd
├── movie_367.Rd
├── movie_368.Rd
├── movie_369.Rd
├── movie_37.Rd
├── movie_370.Rd
├── movie_371.Rd
├── movie_372.Rd
├── movie_373.Rd
├── movie_374.Rd
├── movie_375.Rd
├── movie_376.Rd
├── movie_377.Rd
├── movie_378.Rd
├── movie_379.Rd
├── movie_38.Rd
├── movie_380.Rd
├── movie_381.Rd
├── movie_382.Rd
├── movie_383.Rd
├── movie_384.Rd
├── movie_385.Rd
├── movie_386.Rd
├── movie_387.Rd
├── movie_388.Rd
├── movie_389.Rd
├── movie_39.Rd
├── movie_390.Rd
├── movie_391.Rd
├── movie_392.Rd
├── movie_393.Rd
├── movie_394.Rd
├── movie_395.Rd
├── movie_396.Rd
├── movie_397.Rd
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├── movie_9.Rd
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├── movie_96.Rd
├── movie_97.Rd
├── movie_98.Rd
├── movie_99.Rd
├── netsci.Rd
├── networkdata-package.Rd
├── petster.Rd
├── physicians.Rd
├── polblogs.Rd
├── polbooks.Rd
├── pony.Rd
├── powergrid.Rd
├── protein.Rd
├── radoslaw_email.Rd
├── rhesus.Rd
├── s50.Rd
├── sampson.Rd
├── shakespeare_1.Rd
├── shakespeare_10.Rd
├── shakespeare_11.Rd
├── shakespeare_12.Rd
├── shakespeare_13.Rd
├── shakespeare_14.Rd
├── shakespeare_15.Rd
├── shakespeare_16.Rd
├── shakespeare_17.Rd
├── shakespeare_18.Rd
├── shakespeare_19.Rd
├── shakespeare_2.Rd
├── shakespeare_20.Rd
├── shakespeare_21.Rd
├── shakespeare_22.Rd
├── shakespeare_23.Rd
├── shakespeare_24.Rd
├── shakespeare_25.Rd
├── shakespeare_26.Rd
├── shakespeare_27.Rd
├── shakespeare_28.Rd
├── shakespeare_29.Rd
├── shakespeare_3.Rd
├── shakespeare_30.Rd
├── shakespeare_31.Rd
├── shakespeare_32.Rd
├── shakespeare_33.Rd
├── shakespeare_34.Rd
├── shakespeare_35.Rd
├── shakespeare_36.Rd
├── shakespeare_4.Rd
├── shakespeare_5.Rd
├── shakespeare_6.Rd
├── shakespeare_7.Rd
├── shakespeare_8.Rd
├── shakespeare_9.Rd
├── sheep.Rd
├── sn_auth.Rd
├── southern_women.Rd
├── starwars.Rd
├── surfersb.Rd
├── surfersc.Rd
├── tailor_social.Rd
├── tailor_work.Rd
├── taro.Rd
├── train.Rd
├── ucforum.Rd
├── ucsocial.Rd
├── unicodelang.Rd
├── us_flights.Rd
├── usa_borders.Rd
├── usflights.Rd
├── wiring.Rd
└── wta.Rd
Condensed preview — 2004 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (1,984K chars).
[
{
"path": ".Rbuildignore",
"chars": 111,
"preview": "^.*\\.Rproj$\n^\\.Rproj\\.user$\n^LICENSE\\.md$\n^README\\.Rmd$\n^_pkgdown\\.yml$\n^docs$\n^pkgdown$\n^\\.github$\n^data-raw$\n"
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"path": ".github/.gitignore",
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"preview": "*.html\n"
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"path": ".github/workflows/pkgdown.yaml",
"chars": 1301,
"preview": "# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples\n# Need help debugging build failures? Start at"
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"path": ".gitignore",
"chars": 45,
"preview": ".Rproj.user\n.Rhistory\n.RData\n.Ruserdata\ndocs\n"
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"path": "DESCRIPTION",
"chars": 806,
"preview": "Package: networkdata\nType: Package\nTitle: Repository of Network Datasets \nVersion: 0.2.4\nAuthors@R: \n person(given = "
},
{
"path": "LICENSE",
"chars": 42,
"preview": "YEAR: 2019\nCOPYRIGHT HOLDER: David Schoch\n"
},
{
"path": "LICENSE.md",
"chars": 1071,
"preview": "# MIT License\n\nCopyright (c) 2019 David Schoch\n\nPermission is hereby granted, free of charge, to any person obtaining a "
},
{
"path": "NAMESPACE",
"chars": 46,
"preview": "# Generated by roxygen2: do not edit by hand\n\n"
},
{
"path": "NEWS.md",
"chars": 1462,
"preview": "# networkdata 0.2.3\n\n* added gss dataset\n\n# networkdata 0.2.2\n\n* added full Teenage Friends and Lifestyle Study\n\n# netwo"
},
{
"path": "R/data-animals.R",
"chars": 39503,
"preview": "#' Fishstickleback Proximity (weighted)\n#'\n#' @description\n#'\n#' Species: *Gasterosteus aculeatus*\n#'\n#' Taxonomic class"
},
{
"path": "R/data-covert.R",
"chars": 57172,
"preview": "#'17 November Greece Bombing\n#'@description The dataset refers to the 17 November Revolutionary Organisation, a Marxist "
},
{
"path": "R/data-freeman.R",
"chars": 61774,
"preview": "#' Political Blogs\n#' @description The data were compiled by Lada Adamic and Natalie Glance. Links between blogs were au"
},
{
"path": "R/data-konnect.R",
"chars": 17806,
"preview": "#' Arenas Email\n#' @description This is the email communication network at the University Rovira i Virgili in Tarragona"
},
{
"path": "R/data-misc.R",
"chars": 12226,
"preview": "#' Grey's Anatomy Hook-ups\n#' @description Network of hook-ups of characters in \"Grey's Anatomy\".\n#' @format igraph obje"
},
{
"path": "R/data-movie.R",
"chars": 652455,
"preview": "#'10 Things I Hate About You\n#' @description Interactions of characters in the movie \"10 Things I Hate About You\" (1999)"
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"path": "R/data-shakespeare.R",
"chars": 7788,
"preview": "#' A Comedy of Errors\n#' @description scene co-occurences in Shakespeare's \"A Comedy of Errors\"\n#'\n#' @format igraph obj"
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"path": "R/networkdata.R",
"chars": 1300,
"preview": "#' networkdata: A repository of network datasets\n#'\n#' @description The package contains a large collection of network d"
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"path": "README.Rmd",
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"preview": "---\noutput: github_document\n---\n\n<!-- README.md is generated from README.Rmd. Please edit that file -->\n\n```{r, include "
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{
"path": "README.md",
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"preview": "\n<!-- README.md is generated from README.Rmd. Please edit that file -->\n\n# networkdata <img src=\"man/figures/logo.png\" a"
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"chars": 400,
"preview": "url: https://schochastics.github.io/networkdata/\ntemplate:\n bootstrap: 5\n bslib:\n bg: \"#F9F7F7\"\n fg: \"#112D4E\"\n "
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"path": "data-raw/glasgow129.R",
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"preview": "# fl <- list.files(\"~/Downloads/Glasgow_data/\",full.names = TRUE)\n# load(fl[1])\n# load(fl[3])\n# load(fl[4])\n# load(fl[5]"
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{
"path": "data-raw/gss.R",
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"preview": "library(tidyverse)\nlibrary(haven)\nlibrary(sampling)\nlibrary(egor)\n\n# Download and extract GSS data ---------------------"
},
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"path": "inst/CITATION",
"chars": 399,
"preview": "citHeader(\"To cite networkdata in publications use:\")\n\ncitEntry(\n entry = \"Manual\",\n title = \"networkdata: Repos"
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"path": "man/adjnoun.Rd",
"chars": 760,
"preview": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/data-misc.R\n\\docType{data}\n\\name{adjnoun}\n"
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"path": "man/animal_1.Rd",
"chars": 1361,
"preview": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/data-animals.R\n\\docType{data}\n\\name{animal"
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"path": "man/animal_10.Rd",
"chars": 1294,
"preview": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/data-animals.R\n\\docType{data}\n\\name{animal"
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"path": "man/animal_11.Rd",
"chars": 1351,
"preview": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/data-animals.R\n\\docType{data}\n\\name{animal"
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{
"path": "man/animal_12.Rd",
"chars": 1357,
"preview": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/data-animals.R\n\\docType{data}\n\\name{animal"
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{
"path": "man/animal_13.Rd",
"chars": 1138,
"preview": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/data-animals.R\n\\docType{data}\n\\name{animal"
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{
"path": "man/animal_14.Rd",
"chars": 1175,
"preview": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/data-animals.R\n\\docType{data}\n\\name{animal"
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"path": "man/animal_15.Rd",
"chars": 1217,
"preview": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/data-animals.R\n\\docType{data}\n\\name{animal"
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{
"path": "man/animal_16.Rd",
"chars": 1464,
"preview": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/data-animals.R\n\\docType{data}\n\\name{animal"
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"path": "man/animal_17.Rd",
"chars": 1135,
"preview": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/data-animals.R\n\\docType{data}\n\\name{animal"
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{
"path": "man/animal_18.Rd",
"chars": 1089,
"preview": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/data-animals.R\n\\docType{data}\n\\name{animal"
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"path": "man/animal_19.Rd",
"chars": 1042,
"preview": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/data-animals.R\n\\docType{data}\n\\name{animal"
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]
// ... and 1804 more files (download for full content)
About this extraction
This page contains the full source code of the schochastics/networkdata GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 2004 files (1.8 MB), approximately 550.7k tokens. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.