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Repository: benedekrozemberczki/diff2vec
Branch: master
Commit: 12d915539e43
Files: 10
Total size: 2.0 MB

Directory structure:
gitextract_e04igrgu/

├── .github/
│   └── FUNDING.yml
├── LICENSE
├── README.md
├── data/
│   ├── politician_edges.csv
│   └── restaurant_edges.csv
├── output/
│   └── restaurant.csv
└── src/
    ├── diffusion_2_vec.py
    ├── diffusiontrees.py
    ├── helper.py
    └── subgraphcomponents.py

================================================
FILE CONTENTS
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FILE: .github/FUNDING.yml
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github: [benedekrozemberczki]


================================================
FILE: LICENSE
================================================
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    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    GNU General Public License for more details.

    You should have received a copy of the GNU General Public License
    along with this program.  If not, see <https://www.gnu.org/licenses/>.

Also add information on how to contact you by electronic and paper mail.

  If the program does terminal interaction, make it output a short
notice like this when it starts in an interactive mode:

    <program>  Copyright (C) <year>  <name of author>
    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
    This is free software, and you are welcome to redistribute it
    under certain conditions; type `show c' for details.

The hypothetical commands `show w' and `show c' should show the appropriate
parts of the General Public License.  Of course, your program's commands
might be different; for a GUI interface, you would use an "about box".

  You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU GPL, see
<https://www.gnu.org/licenses/>.

  The GNU General Public License does not permit incorporating your program
into proprietary programs.  If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library.  If this is what you want to do, use the GNU Lesser General
Public License instead of this License.  But first, please read
<https://www.gnu.org/licenses/why-not-lgpl.html>.


================================================
FILE: README.md
================================================
Diff2Vec
===============================

[![Arxiv](https://img.shields.io/badge/ArXiv-2001.07463-orange.svg)](https://arxiv.org/pdf/2001.07463.pdf) [![codebeat badge](https://codebeat.co/badges/6050dda6-4bd9-4977-985a-67cd4686d410)](https://codebeat.co/projects/github-com-benedekrozemberczki-diff2vec-master)
[![repo size](https://img.shields.io/github/repo-size/benedekrozemberczki/diff2vec.svg)](https://github.com/benedekrozemberczki/diff2vec/archive/master.zip) [![benedekrozemberczki](https://img.shields.io/twitter/follow/benrozemberczki?style=social&logo=twitter)](https://twitter.com/intent/follow?screen_name=benrozemberczki) 
<p align="justify">
A graph embedding is a representation of graph vertices in a low-dimensional space, which approximately preserves properties such as distances between nodes. Vertex sequence-based embedding procedures use features extracted from linear sequences of nodes to create embeddings using a neural network. In this paper, we propose diffusion graphs as a method to rapidly generate vertex sequences for network embedding. Its computational efficiency is superior to previous methods due to simpler sequence generation, and it produces more accurate results. In experiments, we found that the performance relative to other methods improves with increasing edge density in the graph. In a community detection task, clustering nodes in the embedding space produces better results compared to other sequence-based embedding methods. 
</p>

<p align="center">
  <img width="500" src="diff2vec.jpeg">
</p>

The model is now also available in the package [Karate Club](https://github.com/benedekrozemberczki/karateclub).


This repository provides a reference implementation for **Diff2Vec** as described in the paper:

> [Fast Sequence Based Embedding with Diffusion Graphs](https://arxiv.org/abs/2001.07463)
> [Benedek Rozemberczki](http://homepages.inf.ed.ac.uk/s1668259/) and  [Rik Sarkar](https://homepages.inf.ed.ac.uk/rsarkar/).
> International Conference on Complex Networks, 2018.

----------------------------

### Citing

If you find Diff2Vec useful in your research, please consider citing the following paper:
```bibtex
>@inproceedings{rozemberczki2018fastsequence,  
  title={{Fast Sequence Based Embedding with Diffusion Graphs}},  
  author={Rozemberczki, Benedek and Sarkar, Rik},  
  booktitle={International Conference on Complex Networks},  
  year={2018},  
  pages={99--107}
 }
```
### Requirements

The codebase is implemented in Python 3.5.2 | Anaconda 4.2.0 (64-bit).

```
tqdm              4.28.1
numpy             1.15.4
pandas            0.23.4
texttable         1.5.0
gensim            3.6.0
networkx          2.4
joblib            0.13.0
logging           0.4.9.6  
```

### Datasets
<p align="justify">
The code takes an input graph in a csv file. Every row indicates an edge between two nodes separated by a comma. The first row is a header. A sample graph for the `Facebook Restaurants` dataset is included in the  `data/` directory.</p>

### Options

<p align="justify">
Learning of the embedding is handled by the `src/diffusion_2_vec.py` script which provides the following command line arguments.</p>

#### Input and output options
```
  --input    STR     Path to the edge list csv.            Default is `data/restaurant_edges.csv`
  --output   STR     Path to the embedding features.       Default is `emb/restaurant.csv`
```

#### Model options
```
  --model                    STR      Embedding procedure.                      Default is `non-pooled`
  --dimensions               INT      Number of embedding dimensions.           Default is 128.
  --vertex-set-cardinality   INT      Number of nodes per diffusion tree.       Default is 80.
  --num-diffusions           INT      Number of diffusions per source node.     Default is 10.
  --window-size              INT      Context size for optimization.            Default is 10.
  --iter                     INT      Number of ASGD iterations.                Default is 1.
  --workers                  INT      Number of cores.                          Default is 4.
  --alpha                    FLOAT    Initial learning rate.                    Default is 0.025.
```

### Examples

<p align="justify">
The following commands learns a graph embedding and writes it to disk. The first column in the embedding file is the node ID.
</p>
Creating an embedding of the default dataset with the default hyperparameter settings.

```
python src/diffusion_2_vec.py
```
Creating an embedding of an other dataset the `Facebook Politicians`.

```
python src/diffusion_2_vec.py --input data/politician_edges.csv --output output/politician.csv
```
<p align="justify">
Creating an embedding of the default dataset in 32 dimensions, 5 sequences per source node with maximal vertex set cardinality of 40.</p>

```
python src/diffusion_2_vec.py --dimensions 32 --num-diffusions 5 --vertex-set-cardinality 40
```
-----------------------------------------------------------------

**License**

- [GNU](https://github.com/benedekrozemberczki/diff2vec/blob/master/LICENSE)

-----------------------------------------------------------------


================================================
FILE: data/politician_edges.csv
================================================
node_1,node_2
0,1972
0,5111
0,138
0,3053
0,1473
0,4978
0,4523
0,2334
0,1038
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5360,5699
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904,4151
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907,4711
907,3759
907,3576
907,1881
907,4459
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357,4218
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378,2580
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1038,1972
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1038,5154
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1038,4674
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1038,4020
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1038,4630
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1042,4455
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647,1357
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1080,2347
1080,2160
1080,5584
1080,5543
1080,3752
1080,1340
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1080,3595
1080,5305
1080,1429
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1080,1456
1080,2815
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1080,4835
1080,4071
1080,4918
1080,1648
1080,4920
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3403,2202
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1087,2111
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1248,3717
1248,2412
1248,1768
1248,3132
1248,5633
1248,2386
1248,1707
1248,5867
1248,1602
1248,2321
1248,2166
1248,3541
1248,4409
1248,4225
1249,5305
1249,2815
1249,4835
1249,4918
1249,5119
1249,3994
1249,5584
1249,4158
1249,2060
1249,1429
1249,1456
1249,3595
1249,2160
1249,1263
1249,2546
1249,3752
1249,5560
1249,5538
1249,4071
1249,2347
1249,5543
1249,1340
1249,1409
1250,5705
1250,1983
1250,3229
1250,3305
1250,4789
1250,3315
1250,1397
1250,4333
1250,2035
1250,2736
1250,2379
1250,530
1250,5355
1250,4193
1251,2777
1251,3792
1251,3719
1251,1323
1252,1446
1252,4954
1252,4182
1252,4210
1252,5558
1252,3504
1252,2292
1252,1885
1252,3748
1252,4637
1252,5679
1252,1980
1252,3704
1252,1267
1252,4122
1252,5391
1252,4385
1252,4175
1252,3203
1252,3828
1253,2229
1254,3541
1254,1707
1254,3079
1254,5867
1255,2576
1255,3840
1255,5756
1867,2674
1867,5884
1867,3619
1867,5192
1867,1337
1867,3453
1867,4726
1867,5198
1867,3718
1867,4851
1867,2857
1257,1291
1257,2304
1257,4014
1257,4921
1257,5800
1257,3946
1257,3897
1877,4976
1877,5028
1259,4416
1259,2145
1259,867
1259,4904
1259,1646
1259,4233
1259,2698
1260,1815
1260,3520
1260,4140
1260,2557
1260,1933
1260,4477
1261,2615
1261,4936
1261,4318
1262,2011
1262,2583
1262,4746
1263,5285
1263,4542
1263,4414
1263,5444
1263,541
1263,2330
1263,4835
1263,5305
1263,2217
1263,2060
1263,3169
1263,3523
1263,1372
1263,4036
1263,5119
1263,4920
1263,3994
1263,4918
1263,3752
1263,4158
1263,3530
1263,4159
1263,2820
1263,1429
1263,2131
1263,2417
1263,1456
1263,4395
1263,3325
1263,3595
1263,4617
1263,1409
1263,2797
1263,5625
1263,2389
1263,5584
1263,1386
1263,5560
1263,5538
1263,2920
1263,5481
1263,2394
1263,4296
1263,1439
1263,4071
1263,1648
1263,4143
1263,2013
1263,4766
1263,2347
1263,2160
1263,5572
1263,3297
1263,5543
1263,5615
1263,4601
1263,5900
1263,4627
1263,4872
1263,1340
1263,1626
1263,3308
1263,3498
1263,5692
1263,5300
1263,5396
1263,3239
1263,2928
1264,2266
1264,2811
1264,3765
1265,3852
1265,1357
1265,1952
1266,4980
1266,3755
1266,4240
1266,1556
1266,4258
1267,966
1267,2292
1267,4637
1267,4182
1267,5558
1267,3504
1921,4274
1921,1946
1921,5747
1921,4340
1921,3354
1921,2128
1921,5882
1921,2357
1921,1864
1921,1478
1921,2084
1921,3019
1921,5743
1921,5228
1921,5772
1921,5052
1921,4422
1921,4444
1921,3597
1921,5029
1921,2689
1921,3576
1921,5681
1921,4328
1921,5870
1921,1986
1921,4572
1921,1443
1921,5439
1921,2598
1921,2831
1921,1606
1921,5643
1921,2602
1921,4874
1921,1472
1921,3519
1269,755
1269,2327
1269,1485
1269,3479
1269,2727
1269,2654
1269,5249
1269,3014
1269,4829
1269,4836
1270,5048
1270,4974
1270,5418
1270,4234
1270,1348
1270,1952
1270,3744
1270,1568
1270,3128
1270,5382
1270,3852
1270,4665
1270,2003
1270,3197
1270,4162
1270,5511
1270,2893
1270,4243
1270,2523
1270,3557
1270,5237
1270,3234
1270,2099
1270,4970
1270,1660
1271,1400
1271,5176
1271,2791
1271,4180
1271,1371
1271,3219
1271,3076
1271,4878
1271,3110
1271,1805
1271,1595
1271,2059
1271,2770
1271,3335
1271,3336
1271,4160
1271,2515
1271,3363
1271,4406
1271,5726
1271,4964
1271,5797
1271,1853
1271,1414
1271,1773
1271,2041
1271,5416
1271,5871
1271,4602
1271,3649
1271,2298
1271,4972
1271,3587
1271,5072
4180,4081
4180,1400
4180,4032
4180,3281
4180,4418
4180,1474
4180,3782
4180,3829
4180,5808
4180,1371
4180,3459
4180,3219
4180,2906
4180,4160
4180,1805
4180,4838
4180,1595
4180,2059
4180,1766
4180,1686
4180,5076
4180,2770
4180,2572
4180,1641
4180,4878
4180,4284
4180,2515
4180,3363
4180,2819
4180,2883
4180,1711
4180,4901
4180,3335
4180,5726
4180,5042
4180,5137
4180,5414
4180,2562
4180,1709
4180,4622
4180,2068
4180,3094
4180,1414
4180,2346
4180,2041
4180,3324
4180,5416
4180,5441
4180,4625
4180,1773
4180,3539
4180,4602
4180,5871
4180,3801
4180,4476
4180,4090
4180,5691
4180,1470
4180,5797
4180,4972
4180,3709
4180,5072
1273,1357
1273,1428
1273,5325
1273,3739
1273,4516
1273,3148
1273,2419
1273,1864
1273,2937
1274,4517
1274,1434
1274,5901
1274,3867
1274,5299
1274,5486
1274,3903
1274,4778
1275,1367
1275,5861
1275,5564
1275,5014
1275,1374
1275,1625
1275,3399
1275,4176
1275,5426
1275,4398
1275,2573
1276,2066
1276,2371
1276,2904
1276,2313
1276,2125
1276,4153
1276,3977
1276,3379
1276,2993
1276,4096
1972,5111
1972,3665
1972,3377
1972,3053
1972,4033
1972,2524
1972,253
1972,4978
1972,4523
1972,2334
1972,4185
1972,3874
1972,2676
1972,2998
1972,3622
1972,5292
1972,3489
1972,5455
1972,2755
1972,1578
1972,2569
1972,2639
1972,3842
1972,1473
1972,5866
1972,3883
1972,5154
1972,2759
1972,5800
1972,3372
1972,5566
1972,4157
1972,4674
1972,2900
1972,2852
1972,4537
1972,1547
1972,3099
1972,4924
1972,5855
1972,4253
1972,2051
1972,4630
1278,3680
1278,2607
1278,3445
1278,1864
1278,5066
1279,3665
1279,1632
1279,1344
1279,5779
1279,4761
1279,3170
1279,5093
1279,3242
1279,5885
1279,1796
1279,1675
1279,3489
1279,4749
1279,1849
1279,4185
1279,2713
1279,2759
1279,1831
1279,4598
1279,2015
1279,4047
1279,2524
1279,1909
1279,3099
1279,2527
1279,2647
1279,5785
1279,3374
1279,5855
1279,4253
1279,4911
1279,5599
1280,2965
1280,3152
1281,2319
5524,4081
5524,4032
5524,4707
5524,5489
5524,5880
5524,3829
5524,2791
5524,5280
5524,2026
5524,2153
5524,4023
5524,4266
5524,4570
5524,5180
5524,3569
5524,2770
5524,4113
5524,4080
5524,1800
5524,3243
5524,2843
5524,3999
5524,4427
5524,3409
5524,1846
5524,2105
5524,5148
5524,4643
5524,2485
5524,3879
5524,5562
5524,4211
5524,5778
5524,1489
5524,5826
5524,5415
5524,2882
5524,2232
5524,1723
5524,5434
5524,2393
5524,4406
5524,2453
5524,5753
5524,2059
5524,4602
5524,2872
5524,5326
5524,4517
5524,2645
5524,5151
5524,2894
5524,1678
5524,4595
5524,3417
5524,4386
5524,4831
5280,5148
5280,2485
5280,1723
5280,2153
5280,4494
1282,1931
1282,3162
1282,2979
1282,2527
1282,4804
1282,5763
1282,1996
1282,4253
1282,3242
1283,4811
1284,2190
1284,3208
1284,1306
1284,1779
1284,3421
1284,4904
1284,1790
1284,2145
1284,2698
1284,4391
2017,2075
2017,3719
2017,2739
2017,1812
2017,2536
2017,2878
2017,1323
2017,5110
2017,1997
2017,3173
2017,4745
2017,2293
2017,1845
2017,3721
2017,5453
2017,4247
2017,3693
2017,5344
2017,5297
2017,3067
2017,5033
2017,1806
2017,4303
2017,5036
2017,5061
2017,5485
2017,4227
1286,5120
1287,2238
1287,3008
1287,2705
1288,5713
1288,4340
1288,5333
1288,3354
1288,5072
1288,5882
1288,1864
1288,4459
1288,3617
1288,5125
1288,3576
1288,5480
1288,4874
1288,5191
1288,1299
1288,5419
1288,5643
1288,2646
1288,3925
1288,2855
1288,5041
1289,5163
1289,5416
1289,1453
1289,1595
1289,5816
1289,4166
1290,4733
1290,2077
1290,4707
1290,2474
1290,4651
1290,3101
1290,5865
1290,1469
1290,2592
1290,5327
1290,5328
1291,1291
1291,1463
1291,4975
1291,3703
1291,4879
1291,1488
1291,2062
1291,3396
1291,3663
1291,2056
1292,3558
1292,3537
1292,769
1292,2454
1292,5742
1292,2061
1292,4654
1292,5716
1292,3482
1292,4508
1292,2306
1292,4059
1292,2778
1292,2495
1292,5139
1292,3610
1293,282
1293,4084
1293,1662
1293,2789
1293,1601
1293,4923
1293,5740
1293,5039
1293,2130
1293,3604
1294,2780
1294,3387
1294,3181
1294,3576
1294,4792
1294,5496
1294,3887
1294,2429
1294,3936
1294,5800
1294,3398
1294,2700
1294,5118
1294,4652
1294,2950
1294,1928
1295,3008
1296,1956
4187,1882
4187,1954
4187,5864
1299,4874
1299,1864
1300,3731
1300,2650
1300,3246
1300,2344
1301,5066
1301,1864
1301,2042
1301,4555
1302,5666
1302,1912
1302,4795
1303,2760
1303,709
1303,1333
1303,5644
1303,1950
1303,3833
1304,2338
1304,2111
1304,2363
1304,1707
1304,5494
1304,3567
1304,5867
1304,1725
1304,5800
1304,5633
1304,2386
1304,2546
1305,184
1305,4491
1305,4885
1305,4360
1305,2934
1306,4670
1306,4818
1306,2088
1306,1329
1306,5286
1306,4391
1306,5337
1306,1779
1306,2145
1306,134
1306,4690
1306,5024
1307,4979
1307,4374
1308,4299
1308,3623
1308,3008
1308,3068
1309,2886
1309,5634
1309,2760
1310,4656
1310,4236
1310,4874
1310,2189
1311,4453
1311,3575
1311,2360
1312,1916
1313,4837
1313,2919
1313,3670
1314,1633
1314,4705
1315,1954
1316,1954
1317,3008
1318,2646
1318,1581
1318,5643
1318,4459
1318,3617
1319,2616
4021,3288
4021,2218
4021,1374
4021,1625
4021,5665
4021,2624
4021,3873
1321,5101
1323,1366
1323,5650
1323,5713
1323,5553
1323,1474
1323,4440
1323,2860
1323,2997
1323,2743
1323,4768
1323,4153
1323,1864
1323,5157
1323,1704
1323,1668
1323,3462
1323,2885
1323,3719
1323,4585
1323,2940
1323,3672
1323,3680
1323,4422
1323,3228
1323,3387
1323,2777
1323,5118
1323,5800
1323,2277
1323,3604
1323,4729
1323,2137
1323,5066
1323,4829
1323,4107
1323,3792
225,5416
225,2819
225,1709
225,1595
1326,3008
1327,4318
1327,2049
1327,2615
1327,3326
1327,5732
1327,1586
1327,4342
1327,4395
1327,2108
1328,4413
1328,4796
1328,5471
1328,3317
1328,2448
1328,3699
1328,2948
1328,2565
1328,1618
1328,5729
1329,2190
1329,2908
1329,867
1329,3208
1329,2088
1329,2048
1329,1779
1329,5337
1329,4070
1329,1790
1329,4391
1329,2145
1329,2698
1329,4214
1330,3685
1330,4297
1331,3857
1331,3484
1331,1562
1331,3800
1331,4728
1331,2107
1331,5468
1332,1908
1332,3841
1332,867
1332,2698
2339,4323
2339,5430
2339,3211
2339,5554
2339,5479
2339,2948
2339,2736
2339,2930
2339,5387
2339,5420
2339,3367
2339,2612
2339,4183
2339,3696
1334,3471
1334,2633
1334,1479
1334,2825
1335,4716
1335,2698
1336,4714
1336,4898
1336,3443
1336,3030
1337,2674
1337,5884
1337,3619
1337,5192
1337,5198
1337,3718
1337,3064
1338,4599
1339,2763
1339,1973
1339,1588
1339,4938
1339,5841
1339,5745
1339,1230
1339,4772
1339,2966
1339,4115
1339,2969
1339,5167
1339,5663
1339,5235
1339,4846
1339,1855
1339,5374
1339,5206
1340,5444
1340,3258
1340,5625
1340,5264
1340,2217
1340,4835
1340,4920
1340,3994
1340,2413
1340,1353
1340,1454
1340,2417
1340,1456
1340,4190
1340,3000
1340,5538
1340,3630
1340,5543
1340,5734
1340,2330
1340,3523
1340,3830
1340,2331
1340,2815
1340,1372
1340,5119
1340,3548
1340,4918
1340,2060
1340,2914
1340,3023
1340,5560
1340,2160
1340,2546
1340,3752
1340,3029
1340,5300
1340,3297
1340,4872
1340,2928
1340,4414
1340,5572
1340,2651
1340,5305
1340,4505
1340,1791
1340,1597
1340,1797
1340,1409
1340,5584
1340,2757
1340,3530
1340,5417
1340,1441
1340,5692
1340,3239
1340,541
1340,2197
1340,4523
1340,4158
1340,4159
1340,1429
1340,3595
1340,4617
1340,845
1340,4821
1340,5615
1340,4071
1340,5900
1340,4354
1340,2402
1340,3983
1341,4643
1341,5489
1341,2485
1341,5880
1341,4113
1341,2393
1341,2153
1341,2894
1341,5882
1341,5434
1341,4080
1341,1678
1342,5066
1342,2406
1342,3719
1342,3781
1342,5227
1342,1505
1342,1864
1342,5279
1343,1842
1343,3987
1343,5066
1343,4408
1343,1923
1343,2953
1343,3166
1343,3975
1344,3665
1344,1632
1344,2702
1344,2086
1344,5574
1344,1719
1344,1996
1344,3170
1344,5093
1344,3762
1344,3242
1344,4438
1344,4185
1344,1831
1344,1675
1344,4020
1344,3489
1344,4761
1344,4749
1344,1849
1344,2230
1344,1621
1344,3140
1344,2713
1344,3684
1344,2759
1344,3162
1344,4598
1344,2015
1344,4047
1344,5566
1344,2524
1344,3099
1344,1466
1344,3982
1344,2979
1344,3817
1344,2647
1344,5785
1344,3374
1344,5855
1344,4253
1344,4498
1344,4911
1344,5885
1345,3689
1345,3432
1345,2856
1345,5329
1346,3947
1346,5797
1348,3234
1348,2134
1348,1988
1348,4974
1348,3557
1348,2893
1348,4243
1348,4970
1348,4665
1348,2729
1348,1952
1348,3744
1348,5752
1349,3660
1349,2633
1349,5074
1349,5082
1350,3058
1350,4689
1350,3124
1350,4974
1350,5128
1351,2303
1351,5644
1352,3402
1353,1409
1353,5584
1353,5543
1353,3752
1353,4158
1353,2060
1353,1454
1353,3595
1353,5305
1353,1429
1353,5538
1353,1456
1353,2815
1353,5560
1353,4835
1353,4071
1353,4918
1353,4920
1355,3326
1355,3389
1356,755
1356,5135
1356,1784
1356,5325
1356,3676
1356,2960
1356,3786
1356,5161
1356,5162
1356,3287
1356,1865
1356,3086
1356,1706
1356,2335
1356,5456
1356,5457
1356,3927
1356,1388
1356,3844
1356,5215
1356,1361
1356,2473
1356,1623
1356,1298
1356,2681
1356,4657
1356,4200
1356,2018
1356,2456
1356,3395
1356,2495
1356,4994
1356,3374
1356,2957
1357,4440
1357,3990
1357,3148
1357,3337
1357,1819
1357,1640
1357,2419
1357,5823
1357,1428
1357,5894
1357,2892
1357,3819
1357,2397
1357,3659
1357,3827
1357,4419
1357,1685
1358,1703
1358,1916
1358,3807
1358,3428
1358,4667
1358,2161
1359,3227
1359,4801
1360,5343
1360,2187
1360,1390
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Download .txt
gitextract_e04igrgu/

├── .github/
│   └── FUNDING.yml
├── LICENSE
├── README.md
├── data/
│   ├── politician_edges.csv
│   └── restaurant_edges.csv
├── output/
│   └── restaurant.csv
└── src/
    ├── diffusion_2_vec.py
    ├── diffusiontrees.py
    ├── helper.py
    └── subgraphcomponents.py
Download .txt
SYMBOL INDEX (19 symbols across 4 files)

FILE: src/diffusion_2_vec.py
  function create_features (line 19) | def create_features(seeding, edge_list_path, vertex_set_cardinality):
  function run_parallel_feature_creation (line 30) | def run_parallel_feature_creation(edge_list_path,
  function learn_pooled_embeddings (line 47) | def learn_pooled_embeddings(walks, counts, args):
  function learn_non_pooled_embeddings (line 65) | def learn_non_pooled_embeddings(walks, counts, args):
  function save_embedding (line 83) | def save_embedding(args, model, counts):
  function main (line 101) | def main(args):

FILE: src/diffusiontrees.py
  class EulerianDiffuser (line 6) | class EulerianDiffuser:
    method __init__ (line 10) | def __init__(self, graph, number_of_nodes):
    method run_diffusion_process (line 21) | def run_diffusion_process(self, node):
    method run_diffusions (line 47) | def run_diffusions(self):

FILE: src/helper.py
  function parameter_parser (line 9) | def parameter_parser():
  function argument_printer (line 72) | def argument_printer(args):
  function generation_tab_printer (line 84) | def generation_tab_printer(read_times, generation_times):
  function result_processing (line 101) | def result_processing(results):
  function process_non_pooled_model_data (line 117) | def process_non_pooled_model_data(walks, counts, args):

FILE: src/subgraphcomponents.py
  class SubGraphComponents (line 9) | class SubGraphComponents:
    method __init__ (line 13) | def __init__(self, edge_list_path, seeding, vertex_set_cardinality):
    method separate_subcomponents (line 28) | def separate_subcomponents(self):
    method single_feature_generation_run (line 36) | def single_feature_generation_run(self):
Condensed preview — 10 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (2,105K chars).
[
  {
    "path": ".github/FUNDING.yml",
    "chars": 30,
    "preview": "github: [benedekrozemberczki]\n"
  },
  {
    "path": "LICENSE",
    "chars": 35149,
    "preview": "                    GNU GENERAL PUBLIC LICENSE\n                       Version 3, 29 June 2007\n\n Copyright (C) 2007 Free "
  },
  {
    "path": "README.md",
    "chars": 5159,
    "preview": "Diff2Vec\n===============================\n\n[![Arxiv](https://img.shields.io/badge/ArXiv-2001.07463-orange.svg)](https://a"
  },
  {
    "path": "data/politician_edges.csv",
    "chars": 402074,
    "preview": "node_1,node_2\n0,1972\n0,5111\n0,138\n0,3053\n0,1473\n0,4978\n0,4523\n0,2334\n0,1038\n0,3923\n0,3874\n0,766\n0,98\n0,2676\n0,1547\n0,362"
  },
  {
    "path": "data/restaurant_edges.csv",
    "chars": 15995,
    "preview": "node_1,node_2\n0,276\n0,58\n0,132\n0,603\n0,398\n0,555\n1,265\n1,611\n2,265\n2,182\n2,345\n3,608\n3,377\n3,40\n3,352\n3,450\n3,484\n3,299\n"
  },
  {
    "path": "output/restaurant.csv",
    "chars": 1587069,
    "preview": "node,x_0,x_1,x_2,x_3,x_4,x_5,x_6,x_7,x_8,x_9,x_10,x_11,x_12,x_13,x_14,x_15,x_16,x_17,x_18,x_19,x_20,x_21,x_22,x_23,x_24,"
  },
  {
    "path": "src/diffusion_2_vec.py",
    "chars": 4538,
    "preview": "\"\"\"Diff2Vec model.\"\"\"\n\nimport time\nimport logging\nimport pandas as pd\nfrom tqdm import tqdm\nfrom joblib import Parallel,"
  },
  {
    "path": "src/diffusiontrees.py",
    "chars": 1836,
    "preview": "\"\"\"Eulerian Diffuser.\"\"\"\n\nimport random\nimport networkx as nx\n\nclass EulerianDiffuser:\n    \"\"\"\n    Class to make diffusi"
  },
  {
    "path": "src/helper.py",
    "chars": 4833,
    "preview": "\"\"\"Helper functions.\"\"\"\n\nimport argparse\nimport numpy as np\nfrom tqdm import tqdm\nfrom texttable import Texttable\nfrom g"
  },
  {
    "path": "src/subgraphcomponents.py",
    "chars": 2019,
    "preview": "\"\"\"Subgraph components module.\"\"\"\n\nimport time\nimport random\nimport pandas as pd\nimport networkx as nx\nfrom diffusiontre"
  }
]

About this extraction

This page contains the full source code of the benedekrozemberczki/diff2vec GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 10 files (2.0 MB), approximately 968.8k tokens, and a symbol index with 19 extracted functions, classes, methods, constants, and types. 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.

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