├── .github └── FUNDING.yml ├── LICENSE ├── README.md ├── appnp_run.jpg ├── input ├── cora_edges.csv ├── cora_features.json └── cora_target.csv ├── ppnp.jpg └── src ├── appnp.py ├── appnp_layer.py ├── main.py ├── param_parser.py └── utils.py /.github/FUNDING.yml: -------------------------------------------------------------------------------- 1 | github: [benedekrozemberczki] 2 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | APPNP 2 | ======================== 3 | 4 | [![Arxiv](https://img.shields.io/badge/ArXiv-1810.05997-orange.svg)](https://arxiv.org/abs/1810.05997) [![codebeat badge](https://codebeat.co/badges/b85b29b2-307e-418e-b704-79804e210111)](https://codebeat.co/projects/github-com-benedekrozemberczki-appnp-master) [![repo size](https://img.shields.io/github/repo-size/benedekrozemberczki/APPNP.svg)](https://github.com/benedekrozemberczki/APPNP/archive/master.zip) [![benedekrozemberczki](https://img.shields.io/twitter/follow/benrozemberczki?style=social&logo=twitter)](https://twitter.com/intent/follow?screen_name=benrozemberczki) 5 | 6 | A **PyTorch** implementation of **Predict then Propagate: Graph Neural 7 | Networks meet Personalized PageRank (ICLR 2019).** 8 |

9 | 10 |

11 | 12 | 13 | --------------------- 14 | 15 | ### Abstract 16 | 17 |

18 | Neural message passing algorithms for semi-supervised classification on graphs have recently achieved great success. However, these methods only consider nodes that are a few propagation steps away and the size of this utilized neighborhood cannot be easily extended. In this paper, we use the relationship between graph convolutional networks (GCN) and PageRank to derive an improved propagation scheme based on personalized PageRank. We utilize this propagation procedure to construct personalized propagation of neural predictions (PPNP) and its approximation, APPNP. Our model's training time is on par or faster and its number of parameters on par or lower than previous models. It leverages a large, adjustable neighborhood for classification and can be combined with any neural network. We show that this model outperforms several recently proposed methods for semi-supervised classification on multiple graphs in the most thorough study done so far for GCN-like models.

19 | 20 | A PyTorch and Tensorflow implementation is awailable [[here.]](https://github.com/klicperajo/ppnp). 21 | 22 | This repository provides a PyTorch implementation of PPNP and APPNP as described in the paper: 23 | 24 | > Predict then Propagate: Graph Neural Networks meet Personalized PageRank. 25 | > Johannes Klicpera, Aleksandar Bojchevski, Stephan Günnemann. 26 | > ICLR, 2019. 27 | > [[Paper]](https://arxiv.org/abs/1810.05997) 28 | 29 | ### Requirements 30 | The codebase is implemented in Python 3.5.2. package versions used for development are just below. 31 | ``` 32 | networkx 2.4 33 | tqdm 4.28.1 34 | numpy 1.15.4 35 | pandas 0.23.4 36 | texttable 1.5.0 37 | scipy 1.1.0 38 | argparse 1.1.0 39 | torch 1.1.0 40 | torch-scatter 1.4.0 41 | torch-sparse 0.4.3 42 | torch-cluster 1.4.5 43 | torch-geometric 1.3.2 44 | torchvision 0.3.0 45 | ``` 46 | ### Datasets 47 |

48 | The code takes the **edge list** of the graph in a csv file. Every row indicates an edge between two nodes separated by a comma. The first row is a header. Nodes should be indexed starting with 0. A sample graph for `Cora` is included in the `input/` directory. In addition to the edgelist there is a JSON file with the sparse features and a csv with the target variable.

49 |

50 | The **feature matrix** is a sparse binary one it is stored as a json. Nodes are keys of the json and feature indices are the values. For each node feature column ids are stored as elements of a list. The feature matrix is structured as:

51 | 52 | ```javascript 53 | { 0: [0, 1, 38, 1968, 2000, 52727], 54 | 1: [10000, 20, 3], 55 | 2: [], 56 | ... 57 | n: [2018, 10000]} 58 | ``` 59 | 60 | The **target vector** is a csv with two columns and headers, the first contains the node identifiers the second the targets. This csv is sorted by node identifiers and the target column contains the class meberships indexed from zero. 61 | 62 | | **NODE ID**| **Target** | 63 | | --- | --- | 64 | | 0 | 3 | 65 | | 1 | 1 | 66 | | 2 | 0 | 67 | | 3 | 1 | 68 | | ... | ... | 69 | | n | 3 | 70 | 71 | ### Options 72 | Training an APPNP/PPNP model is handled by the `src/main.py` script which provides the following command line arguments. 73 | 74 | #### Input and output options 75 | ``` 76 | --edge-path STR Edge list csv. Default is `input/cora_edges.csv`. 77 | --features-path STR Features json. Default is `input/cora_features.json`. 78 | --target-path STR Target classes csv. Default is `input/cora_target.csv`. 79 | ``` 80 | #### Model options 81 | ``` 82 | --seed INT Random seed. Defailt is 42. 83 | --model STR Model exact or approximate. Default is `exact`. 84 | --iterations INT APP iterations. Default is 10. 85 | --alpha FLOAT Teleport parameter. Default is 0.1 86 | --epochs INT Number of training epochs. Default is 2000. 87 | --early-stopping INT Early stopping rounds. Default is 5. 88 | --training-size INT Training set size. Default is 1500. 89 | --test-size INT Test set size. Default is 500. 90 | --learning-rate FLOAT Adam learning rate. Default is 0.01 91 | --dropout FLOAT Dropout rate value. Default is 0.5 92 | --lambd FLOAT Rgularization parameter. Default is 0.005. 93 | --layers LST Layer sizes in first layers. Default is [64, 64]. 94 | ``` 95 | ### Examples 96 | The following commands learn a neural network and score on the test set. Training a model on the default dataset. 97 | ``` 98 | python src/main.py 99 | ``` 100 |

101 | 102 |

103 | 104 | Training a PPNP model for a 100 epochs. 105 | ``` 106 | python src/main.py --epochs 100 107 | ``` 108 | Training an APPNP model. 109 | ``` 110 | python src/main.py --model approximate 111 | ``` 112 | Increasing the learning rate and the dropout. 113 | ``` 114 | python src/main.py --learning-rate 0.1 --dropout 0.9 115 | ``` 116 | 117 | -------------------------------------------------------------------------------- 118 | 119 | **License** 120 | 121 | - [GNU License](https://github.com/benedekrozemberczki/APPNP/blob/master/LICENSE) 122 | 123 | ---------------------------------------------------------------------------- 124 | -------------------------------------------------------------------------------- /appnp_run.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/benedekrozemberczki/APPNP/338d98980ee8748e652630dd422341ef8b2a354a/appnp_run.jpg -------------------------------------------------------------------------------- /input/cora_target.csv: -------------------------------------------------------------------------------- 1 | id,target 2 | 0,3 3 | 1,4 4 | 2,4 5 | 3,0 6 | 4,3 7 | 5,2 8 | 6,0 9 | 7,3 10 | 8,3 11 | 9,2 12 | 10,0 13 | 11,0 14 | 12,4 15 | 13,3 16 | 14,3 17 | 15,3 18 | 16,2 19 | 17,3 20 | 18,1 21 | 19,3 22 | 20,5 23 | 21,3 24 | 22,4 25 | 23,6 26 | 24,3 27 | 25,3 28 | 26,6 29 | 27,3 30 | 28,2 31 | 29,4 32 | 30,3 33 | 31,6 34 | 32,0 35 | 33,4 36 | 34,2 37 | 35,0 38 | 36,1 39 | 37,5 40 | 38,4 41 | 39,4 42 | 40,3 43 | 41,6 44 | 42,6 45 | 43,4 46 | 44,3 47 | 45,3 48 | 46,2 49 | 47,5 50 | 48,3 51 | 49,4 52 | 50,5 53 | 51,3 54 | 52,0 55 | 53,2 56 | 54,1 57 | 55,4 58 | 56,6 59 | 57,3 60 | 58,2 61 | 59,2 62 | 60,0 63 | 61,0 64 | 62,0 65 | 63,4 66 | 64,2 67 | 65,0 68 | 66,4 69 | 67,5 70 | 68,2 71 | 69,6 72 | 70,5 73 | 71,2 74 | 72,2 75 | 73,2 76 | 74,0 77 | 75,4 78 | 76,5 79 | 77,6 80 | 78,4 81 | 79,0 82 | 80,0 83 | 81,0 84 | 82,4 85 | 83,2 86 | 84,4 87 | 85,1 88 | 86,4 89 | 87,6 90 | 88,0 91 | 89,4 92 | 90,2 93 | 91,4 94 | 92,6 95 | 93,6 96 | 94,0 97 | 95,0 98 | 96,6 99 | 97,5 100 | 98,0 101 | 99,6 102 | 100,0 103 | 101,2 104 | 102,1 105 | 103,1 106 | 104,1 107 | 105,2 108 | 106,6 109 | 107,5 110 | 108,6 111 | 109,1 112 | 110,2 113 | 111,2 114 | 112,1 115 | 113,5 116 | 114,5 117 | 115,5 118 | 116,6 119 | 117,5 120 | 118,6 121 | 119,5 122 | 120,5 123 | 121,1 124 | 122,6 125 | 123,6 126 | 124,1 127 | 125,5 128 | 126,1 129 | 127,6 130 | 128,5 131 | 129,5 132 | 130,5 133 | 131,1 134 | 132,5 135 | 133,1 136 | 134,1 137 | 135,1 138 | 136,1 139 | 137,1 140 | 138,1 141 | 139,1 142 | 140,4 143 | 141,3 144 | 142,0 145 | 143,3 146 | 144,6 147 | 145,6 148 | 146,0 149 | 147,3 150 | 148,4 151 | 149,0 152 | 150,3 153 | 151,4 154 | 152,4 155 | 153,1 156 | 154,2 157 | 155,2 158 | 156,2 159 | 157,3 160 | 158,3 161 | 159,3 162 | 160,3 163 | 161,0 164 | 162,4 165 | 163,5 166 | 164,0 167 | 165,3 168 | 166,4 169 | 167,3 170 | 168,3 171 | 169,3 172 | 170,2 173 | 171,3 174 | 172,3 175 | 173,2 176 | 174,2 177 | 175,6 178 | 176,1 179 | 177,4 180 | 178,3 181 | 179,3 182 | 180,3 183 | 181,6 184 | 182,3 185 | 183,3 186 | 184,3 187 | 185,3 188 | 186,0 189 | 187,4 190 | 188,2 191 | 189,2 192 | 190,6 193 | 191,5 194 | 192,3 195 | 193,5 196 | 194,4 197 | 195,0 198 | 196,4 199 | 197,3 200 | 198,4 201 | 199,4 202 | 200,3 203 | 201,3 204 | 202,2 205 | 203,4 206 | 204,0 207 | 205,3 208 | 206,2 209 | 207,3 210 | 208,3 211 | 209,4 212 | 210,4 213 | 211,0 214 | 212,3 215 | 213,6 216 | 214,0 217 | 215,3 218 | 216,3 219 | 217,4 220 | 218,3 221 | 219,3 222 | 220,5 223 | 221,2 224 | 222,3 225 | 223,2 226 | 224,4 227 | 225,1 228 | 226,3 229 | 227,2 230 | 228,2 231 | 229,3 232 | 230,3 233 | 231,3 234 | 232,3 235 | 233,5 236 | 234,1 237 | 235,3 238 | 236,1 239 | 237,3 240 | 238,5 241 | 239,0 242 | 240,3 243 | 241,5 244 | 242,0 245 | 243,4 246 | 244,2 247 | 245,4 248 | 246,2 249 | 247,4 250 | 248,4 251 | 249,5 252 | 250,4 253 | 251,3 254 | 252,5 255 | 253,3 256 | 254,3 257 | 255,4 258 | 256,3 259 | 257,0 260 | 258,4 261 | 259,5 262 | 260,0 263 | 261,3 264 | 262,6 265 | 263,2 266 | 264,5 267 | 265,5 268 | 266,5 269 | 267,3 270 | 268,2 271 | 269,3 272 | 270,0 273 | 271,4 274 | 272,5 275 | 273,3 276 | 274,0 277 | 275,4 278 | 276,0 279 | 277,3 280 | 278,3 281 | 279,0 282 | 280,0 283 | 281,3 284 | 282,5 285 | 283,4 286 | 284,4 287 | 285,3 288 | 286,4 289 | 287,3 290 | 288,3 291 | 289,2 292 | 290,2 293 | 291,3 294 | 292,0 295 | 293,3 296 | 294,1 297 | 295,3 298 | 296,2 299 | 297,3 300 | 298,3 301 | 299,4 302 | 300,5 303 | 301,2 304 | 302,1 305 | 303,1 306 | 304,0 307 | 305,0 308 | 306,1 309 | 307,6 310 | 308,1 311 | 309,3 312 | 310,3 313 | 311,3 314 | 312,2 315 | 313,3 316 | 314,3 317 | 315,0 318 | 316,3 319 | 317,4 320 | 318,1 321 | 319,3 322 | 320,4 323 | 321,3 324 | 322,2 325 | 323,0 326 | 324,0 327 | 325,4 328 | 326,2 329 | 327,3 330 | 328,2 331 | 329,1 332 | 330,4 333 | 331,6 334 | 332,3 335 | 333,2 336 | 334,0 337 | 335,3 338 | 336,3 339 | 337,2 340 | 338,3 341 | 339,4 342 | 340,4 343 | 341,2 344 | 342,1 345 | 343,3 346 | 344,5 347 | 345,3 348 | 346,2 349 | 347,0 350 | 348,4 351 | 349,5 352 | 350,1 353 | 351,3 354 | 352,3 355 | 353,2 356 | 354,0 357 | 355,2 358 | 356,4 359 | 357,2 360 | 358,2 361 | 359,2 362 | 360,5 363 | 361,4 364 | 362,4 365 | 363,2 366 | 364,2 367 | 365,0 368 | 366,3 369 | 367,2 370 | 368,4 371 | 369,4 372 | 370,5 373 | 371,5 374 | 372,1 375 | 373,0 376 | 374,3 377 | 375,4 378 | 376,5 379 | 377,3 380 | 378,4 381 | 379,5 382 | 380,3 383 | 381,4 384 | 382,3 385 | 383,3 386 | 384,1 387 | 385,4 388 | 386,3 389 | 387,3 390 | 388,5 391 | 389,2 392 | 390,3 393 | 391,2 394 | 392,5 395 | 393,5 396 | 394,4 397 | 395,3 398 | 396,3 399 | 397,3 400 | 398,3 401 | 399,1 402 | 400,5 403 | 401,3 404 | 402,3 405 | 403,2 406 | 404,6 407 | 405,0 408 | 406,1 409 | 407,3 410 | 408,0 411 | 409,1 412 | 410,5 413 | 411,3 414 | 412,6 415 | 413,3 416 | 414,6 417 | 415,0 418 | 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499,6 502 | 500,3 503 | 501,2 504 | 502,0 505 | 503,3 506 | 504,6 507 | 505,1 508 | 506,6 509 | 507,3 510 | 508,1 511 | 509,3 512 | 510,3 513 | 511,3 514 | 512,3 515 | 513,2 516 | 514,5 517 | 515,4 518 | 516,5 519 | 517,5 520 | 518,3 521 | 519,1 522 | 520,3 523 | 521,3 524 | 522,4 525 | 523,4 526 | 524,2 527 | 525,0 528 | 526,2 529 | 527,0 530 | 528,5 531 | 529,4 532 | 530,0 533 | 531,0 534 | 532,3 535 | 533,2 536 | 534,2 537 | 535,2 538 | 536,2 539 | 537,6 540 | 538,4 541 | 539,6 542 | 540,5 543 | 541,5 544 | 542,1 545 | 543,0 546 | 544,0 547 | 545,4 548 | 546,3 549 | 547,3 550 | 548,1 551 | 549,3 552 | 550,6 553 | 551,6 554 | 552,2 555 | 553,3 556 | 554,3 557 | 555,3 558 | 556,1 559 | 557,2 560 | 558,2 561 | 559,5 562 | 560,4 563 | 561,3 564 | 562,2 565 | 563,1 566 | 564,2 567 | 565,2 568 | 566,3 569 | 567,2 570 | 568,3 571 | 569,2 572 | 570,3 573 | 571,3 574 | 572,0 575 | 573,5 576 | 574,3 577 | 575,3 578 | 576,3 579 | 577,4 580 | 578,5 581 | 579,3 582 | 580,2 583 | 581,1 584 | 582,4 585 | 583,4 586 | 584,4 587 | 585,4 588 | 586,0 589 | 587,5 590 | 588,4 591 | 589,1 592 | 590,3 593 | 591,0 594 | 592,3 595 | 593,4 596 | 594,6 597 | 595,3 598 | 596,6 599 | 597,3 600 | 598,3 601 | 599,3 602 | 600,6 603 | 601,3 604 | 602,4 605 | 603,3 606 | 604,6 607 | 605,3 608 | 606,0 609 | 607,3 610 | 608,1 611 | 609,2 612 | 610,5 613 | 611,6 614 | 612,5 615 | 613,2 616 | 614,0 617 | 615,2 618 | 616,2 619 | 617,3 620 | 618,3 621 | 619,0 622 | 620,3 623 | 621,5 624 | 622,3 625 | 623,4 626 | 624,0 627 | 625,3 628 | 626,2 629 | 627,4 630 | 628,5 631 | 629,2 632 | 630,3 633 | 631,2 634 | 632,2 635 | 633,3 636 | 634,5 637 | 635,2 638 | 636,0 639 | 637,3 640 | 638,4 641 | 639,3 642 | 640,3 643 | 641,3 644 | 642,0 645 | 643,5 646 | 644,5 647 | 645,5 648 | 646,5 649 | 647,5 650 | 648,5 651 | 649,3 652 | 650,2 653 | 651,0 654 | 652,4 655 | 653,3 656 | 654,4 657 | 655,1 658 | 656,1 659 | 657,2 660 | 658,3 661 | 659,0 662 | 660,1 663 | 661,5 664 | 662,3 665 | 663,6 666 | 664,3 667 | 665,4 668 | 666,0 669 | 667,0 670 | 668,5 671 | 669,3 672 | 670,3 673 | 671,5 674 | 672,2 675 | 673,3 676 | 674,3 677 | 675,4 678 | 676,5 679 | 677,4 680 | 678,3 681 | 679,0 682 | 680,0 683 | 681,3 684 | 682,6 685 | 683,1 686 | 684,2 687 | 685,1 688 | 686,2 689 | 687,2 690 | 688,4 691 | 689,2 692 | 690,3 693 | 691,4 694 | 692,3 695 | 693,0 696 | 694,5 697 | 695,3 698 | 696,3 699 | 697,3 700 | 698,4 701 | 699,3 702 | 700,3 703 | 701,5 704 | 702,6 705 | 703,5 706 | 704,2 707 | 705,4 708 | 706,4 709 | 707,0 710 | 708,3 711 | 709,5 712 | 710,3 713 | 711,0 714 | 712,6 715 | 713,3 716 | 714,4 717 | 715,4 718 | 716,3 719 | 717,0 720 | 718,0 721 | 719,1 722 | 720,5 723 | 721,2 724 | 722,3 725 | 723,2 726 | 724,6 727 | 725,0 728 | 726,4 729 | 727,3 730 | 728,5 731 | 729,3 732 | 730,0 733 | 731,0 734 | 732,2 735 | 733,0 736 | 734,0 737 | 735,5 738 | 736,0 739 | 737,5 740 | 738,0 741 | 739,5 742 | 740,4 743 | 741,1 744 | 742,2 745 | 743,3 746 | 744,2 747 | 745,3 748 | 746,3 749 | 747,5 750 | 748,2 751 | 749,4 752 | 750,5 753 | 751,0 754 | 752,2 755 | 753,0 756 | 754,2 757 | 755,5 758 | 756,3 759 | 757,2 760 | 758,2 761 | 759,4 762 | 760,2 763 | 761,4 764 | 762,2 765 | 763,0 766 | 764,2 767 | 765,3 768 | 766,3 769 | 767,0 770 | 768,3 771 | 769,0 772 | 770,3 773 | 771,0 774 | 772,6 775 | 773,1 776 | 774,4 777 | 775,3 778 | 776,4 779 | 777,0 780 | 778,6 781 | 779,6 782 | 780,4 783 | 781,3 784 | 782,4 785 | 783,4 786 | 784,3 787 | 785,3 788 | 786,4 789 | 787,4 790 | 788,3 791 | 789,4 792 | 790,3 793 | 791,3 794 | 792,3 795 | 793,5 796 | 794,0 797 | 795,3 798 | 796,2 799 | 797,2 800 | 798,4 801 | 799,3 802 | 800,2 803 | 801,5 804 | 802,4 805 | 803,5 806 | 804,4 807 | 805,4 808 | 806,2 809 | 807,5 810 | 808,4 811 | 809,0 812 | 810,4 813 | 811,3 814 | 812,3 815 | 813,4 816 | 814,4 817 | 815,0 818 | 816,5 819 | 817,2 820 | 818,3 821 | 819,2 822 | 820,2 823 | 821,3 824 | 822,5 825 | 823,2 826 | 824,2 827 | 825,2 828 | 826,5 829 | 827,3 830 | 828,4 831 | 829,1 832 | 830,6 833 | 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1855,3 1858 | 1856,1 1859 | 1857,3 1860 | 1858,3 1861 | 1859,3 1862 | 1860,3 1863 | 1861,3 1864 | 1862,3 1865 | 1863,3 1866 | 1864,3 1867 | 1865,3 1868 | 1866,3 1869 | 1867,3 1870 | 1868,3 1871 | 1869,3 1872 | 1870,3 1873 | 1871,3 1874 | 1872,3 1875 | 1873,3 1876 | 1874,3 1877 | 1875,3 1878 | 1876,3 1879 | 1877,3 1880 | 1878,5 1881 | 1879,5 1882 | 1880,5 1883 | 1881,5 1884 | 1882,5 1885 | 1883,5 1886 | 1884,5 1887 | 1885,5 1888 | 1886,2 1889 | 1887,2 1890 | 1888,2 1891 | 1889,4 1892 | 1890,4 1893 | 1891,4 1894 | 1892,0 1895 | 1893,3 1896 | 1894,3 1897 | 1895,2 1898 | 1896,5 1899 | 1897,5 1900 | 1898,5 1901 | 1899,5 1902 | 1900,6 1903 | 1901,5 1904 | 1902,5 1905 | 1903,5 1906 | 1904,5 1907 | 1905,0 1908 | 1906,4 1909 | 1907,4 1910 | 1908,4 1911 | 1909,0 1912 | 1910,0 1913 | 1911,5 1914 | 1912,0 1915 | 1913,0 1916 | 1914,6 1917 | 1915,6 1918 | 1916,6 1919 | 1917,6 1920 | 1918,6 1921 | 1919,6 1922 | 1920,0 1923 | 1921,0 1924 | 1922,0 1925 | 1923,0 1926 | 1924,3 1927 | 1925,0 1928 | 1926,0 1929 | 1927,0 1930 | 1928,3 1931 | 1929,3 1932 | 1930,0 1933 | 1931,3 1934 | 1932,3 1935 | 1933,3 1936 | 1934,3 1937 | 1935,3 1938 | 1936,3 1939 | 1937,3 1940 | 1938,3 1941 | 1939,3 1942 | 1940,3 1943 | 1941,3 1944 | 1942,3 1945 | 1943,3 1946 | 1944,3 1947 | 1945,3 1948 | 1946,3 1949 | 1947,3 1950 | 1948,3 1951 | 1949,3 1952 | 1950,3 1953 | 1951,3 1954 | 1952,3 1955 | 1953,5 1956 | 1954,5 1957 | 1955,5 1958 | 1956,5 1959 | 1957,3 1960 | 1958,5 1961 | 1959,5 1962 | 1960,5 1963 | 1961,5 1964 | 1962,5 1965 | 1963,5 1966 | 1964,4 1967 | 1965,4 1968 | 1966,4 1969 | 1967,4 1970 | 1968,4 1971 | 1969,4 1972 | 1970,4 1973 | 1971,4 1974 | 1972,6 1975 | 1973,6 1976 | 1974,5 1977 | 1975,6 1978 | 1976,6 1979 | 1977,3 1980 | 1978,5 1981 | 1979,5 1982 | 1980,5 1983 | 1981,0 1984 | 1982,5 1985 | 1983,0 1986 | 1984,4 1987 | 1985,4 1988 | 1986,3 1989 | 1987,3 1990 | 1988,3 1991 | 1989,2 1992 | 1990,2 1993 | 1991,1 1994 | 1992,3 1995 | 1993,3 1996 | 1994,3 1997 | 1995,3 1998 | 1996,3 1999 | 1997,3 2000 | 1998,5 2001 | 1999,3 2002 | 2000,3 2003 | 2001,4 2004 | 2002,4 2005 | 2003,3 2006 | 2004,3 2007 | 2005,3 2008 | 2006,3 2009 | 2007,3 2010 | 2008,3 2011 | 2009,3 2012 | 2010,0 2013 | 2011,3 2014 | 2012,3 2015 | 2013,6 2016 | 2014,3 2017 | 2015,6 2018 | 2016,0 2019 | 2017,5 2020 | 2018,0 2021 | 2019,0 2022 | 2020,4 2023 | 2021,0 2024 | 2022,6 2025 | 2023,5 2026 | 2024,5 2027 | 2025,0 2028 | 2026,1 2029 | 2027,3 2030 | 2028,3 2031 | 2029,5 2032 | 2030,6 2033 | 2031,5 2034 | 2032,3 2035 | 2033,3 2036 | 2034,4 2037 | 2035,3 2038 | 2036,3 2039 | 2037,3 2040 | 2038,3 2041 | 2039,3 2042 | 2040,4 2043 | 2041,3 2044 | 2042,3 2045 | 2043,4 2046 | 2044,3 2047 | 2045,1 2048 | 2046,1 2049 | 2047,0 2050 | 2048,1 2051 | 2049,0 2052 | 2050,6 2053 | 2051,0 2054 | 2052,0 2055 | 2053,0 2056 | 2054,0 2057 | 2055,0 2058 | 2056,0 2059 | 2057,0 2060 | 2058,5 2061 | 2059,0 2062 | 2060,5 2063 | 2061,5 2064 | 2062,5 2065 | 2063,3 2066 | 2064,3 2067 | 2065,3 2068 | 2066,3 2069 | 2067,3 2070 | 2068,0 2071 | 2069,0 2072 | 2070,0 2073 | 2071,2 2074 | 2072,0 2075 | 2073,0 2076 | 2074,0 2077 | 2075,3 2078 | 2076,3 2079 | 2077,3 2080 | 2078,3 2081 | 2079,1 2082 | 2080,1 2083 | 2081,1 2084 | 2082,1 2085 | 2083,2 2086 | 2084,1 2087 | 2085,1 2088 | 2086,1 2089 | 2087,1 2090 | 2088,1 2091 | 2089,0 2092 | 2090,1 2093 | 2091,3 2094 | 2092,1 2095 | 2093,1 2096 | 2094,1 2097 | 2095,1 2098 | 2096,1 2099 | 2097,0 2100 | 2098,0 2101 | 2099,0 2102 | 2100,5 2103 | 2101,5 2104 | 2102,5 2105 | 2103,5 2106 | 2104,3 2107 | 2105,5 2108 | 2106,1 2109 | 2107,1 2110 | 2108,3 2111 | 2109,6 2112 | 2110,6 2113 | 2111,5 2114 | 2112,6 2115 | 2113,2 2116 | 2114,3 2117 | 2115,3 2118 | 2116,0 2119 | 2117,3 2120 | 2118,3 2121 | 2119,3 2122 | 2120,4 2123 | 2121,4 2124 | 2122,4 2125 | 2123,4 2126 | 2124,3 2127 | 2125,3 2128 | 2126,3 2129 | 2127,4 2130 | 2128,3 2131 | 2129,3 2132 | 2130,4 2133 | 2131,0 2134 | 2132,6 2135 | 2133,0 2136 | 2134,6 2137 | 2135,6 2138 | 2136,0 2139 | 2137,0 2140 | 2138,3 2141 | 2139,3 2142 | 2140,3 2143 | 2141,3 2144 | 2142,3 2145 | 2143,1 2146 | 2144,1 2147 | 2145,1 2148 | 2146,3 2149 | 2147,3 2150 | 2148,3 2151 | 2149,3 2152 | 2150,5 2153 | 2151,6 2154 | 2152,3 2155 | 2153,4 2156 | 2154,6 2157 | 2155,0 2158 | 2156,0 2159 | 2157,6 2160 | 2158,6 2161 | 2159,6 2162 | 2160,6 2163 | 2161,6 2164 | 2162,3 2165 | 2163,3 2166 | 2164,6 2167 | 2165,6 2168 | 2166,5 2169 | 2167,2 2170 | 2168,1 2171 | 2169,2 2172 | 2170,1 2173 | 2171,0 2174 | 2172,0 2175 | 2173,6 2176 | 2174,6 2177 | 2175,2 2178 | 2176,3 2179 | 2177,3 2180 | 2178,5 2181 | 2179,0 2182 | 2180,0 2183 | 2181,0 2184 | 2182,0 2185 | 2183,0 2186 | 2184,5 2187 | 2185,5 2188 | 2186,0 2189 | 2187,3 2190 | 2188,5 2191 | 2189,0 2192 | 2190,6 2193 | 2191,3 2194 | 2192,6 2195 | 2193,0 2196 | 2194,0 2197 | 2195,0 2198 | 2196,0 2199 | 2197,0 2200 | 2198,0 2201 | 2199,0 2202 | 2200,0 2203 | 2201,0 2204 | 2202,0 2205 | 2203,0 2206 | 2204,3 2207 | 2205,3 2208 | 2206,3 2209 | 2207,3 2210 | 2208,1 2211 | 2209,6 2212 | 2210,1 2213 | 2211,0 2214 | 2212,3 2215 | 2213,3 2216 | 2214,3 2217 | 2215,3 2218 | 2216,3 2219 | 2217,6 2220 | 2218,1 2221 | 2219,0 2222 | 2220,2 2223 | 2221,2 2224 | 2222,4 2225 | 2223,4 2226 | 2224,4 2227 | 2225,4 2228 | 2226,4 2229 | 2227,5 2230 | 2228,6 2231 | 2229,3 2232 | 2230,3 2233 | 2231,0 2234 | 2232,0 2235 | 2233,0 2236 | 2234,0 2237 | 2235,5 2238 | 2236,4 2239 | 2237,4 2240 | 2238,4 2241 | 2239,4 2242 | 2240,4 2243 | 2241,3 2244 | 2242,3 2245 | 2243,3 2246 | 2244,3 2247 | 2245,3 2248 | 2246,0 2249 | 2247,3 2250 | 2248,4 2251 | 2249,4 2252 | 2250,4 2253 | 2251,1 2254 | 2252,1 2255 | 2253,3 2256 | 2254,1 2257 | 2255,1 2258 | 2256,5 2259 | 2257,1 2260 | 2258,3 2261 | 2259,4 2262 | 2260,4 2263 | 2261,4 2264 | 2262,4 2265 | 2263,4 2266 | 2264,4 2267 | 2265,4 2268 | 2266,0 2269 | 2267,0 2270 | 2268,0 2271 | 2269,5 2272 | 2270,5 2273 | 2271,5 2274 | 2272,5 2275 | 2273,5 2276 | 2274,0 2277 | 2275,5 2278 | 2276,3 2279 | 2277,0 2280 | 2278,6 2281 | 2279,2 2282 | 2280,0 2283 | 2281,5 2284 | 2282,3 2285 | 2283,3 2286 | 2284,5 2287 | 2285,5 2288 | 2286,5 2289 | 2287,5 2290 | 2288,5 2291 | 2289,4 2292 | 2290,4 2293 | 2291,0 2294 | 2292,4 2295 | 2293,0 2296 | 2294,4 2297 | 2295,0 2298 | 2296,3 2299 | 2297,4 2300 | 2298,4 2301 | 2299,4 2302 | 2300,1 2303 | 2301,3 2304 | 2302,3 2305 | 2303,3 2306 | 2304,3 2307 | 2305,3 2308 | 2306,4 2309 | 2307,2 2310 | 2308,3 2311 | 2309,3 2312 | 2310,3 2313 | 2311,0 2314 | 2312,0 2315 | 2313,2 2316 | 2314,3 2317 | 2315,3 2318 | 2316,3 2319 | 2317,3 2320 | 2318,1 2321 | 2319,1 2322 | 2320,3 2323 | 2321,0 2324 | 2322,1 2325 | 2323,4 2326 | 2324,1 2327 | 2325,1 2328 | 2326,1 2329 | 2327,1 2330 | 2328,1 2331 | 2329,1 2332 | 2330,0 2333 | 2331,1 2334 | 2332,0 2335 | 2333,0 2336 | 2334,2 2337 | 2335,4 2338 | 2336,4 2339 | 2337,4 2340 | 2338,3 2341 | 2339,3 2342 | 2340,3 2343 | 2341,4 2344 | 2342,0 2345 | 2343,3 2346 | 2344,3 2347 | 2345,3 2348 | 2346,3 2349 | 2347,0 2350 | 2348,3 2351 | 2349,3 2352 | 2350,4 2353 | 2351,4 2354 | 2352,4 2355 | 2353,4 2356 | 2354,4 2357 | 2355,4 2358 | 2356,0 2359 | 2357,4 2360 | 2358,3 2361 | 2359,2 2362 | 2360,0 2363 | 2361,3 2364 | 2362,4 2365 | 2363,5 2366 | 2364,0 2367 | 2365,2 2368 | 2366,2 2369 | 2367,3 2370 | 2368,3 2371 | 2369,3 2372 | 2370,3 2373 | 2371,3 2374 | 2372,2 2375 | 2373,3 2376 | 2374,5 2377 | 2375,5 2378 | 2376,4 2379 | 2377,1 2380 | 2378,4 2381 | 2379,4 2382 | 2380,4 2383 | 2381,3 2384 | 2382,4 2385 | 2383,4 2386 | 2384,0 2387 | 2385,4 2388 | 2386,4 2389 | 2387,4 2390 | 2388,5 2391 | 2389,2 2392 | 2390,2 2393 | 2391,2 2394 | 2392,2 2395 | 2393,4 2396 | 2394,6 2397 | 2395,6 2398 | 2396,6 2399 | 2397,6 2400 | 2398,3 2401 | 2399,4 2402 | 2400,4 2403 | 2401,4 2404 | 2402,1 2405 | 2403,3 2406 | 2404,0 2407 | 2405,3 2408 | 2406,3 2409 | 2407,5 2410 | 2408,0 2411 | 2409,2 2412 | 2410,3 2413 | 2411,3 2414 | 2412,3 2415 | 2413,3 2416 | 2414,3 2417 | 2415,2 2418 | 2416,4 2419 | 2417,4 2420 | 2418,0 2421 | 2419,0 2422 | 2420,3 2423 | 2421,2 2424 | 2422,6 2425 | 2423,6 2426 | 2424,0 2427 | 2425,3 2428 | 2426,3 2429 | 2427,3 2430 | 2428,5 2431 | 2429,1 2432 | 2430,3 2433 | 2431,4 2434 | 2432,4 2435 | 2433,2 2436 | 2434,4 2437 | 2435,4 2438 | 2436,4 2439 | 2437,3 2440 | 2438,3 2441 | 2439,2 2442 | 2440,2 2443 | 2441,2 2444 | 2442,2 2445 | 2443,2 2446 | 2444,2 2447 | 2445,2 2448 | 2446,2 2449 | 2447,2 2450 | 2448,2 2451 | 2449,0 2452 | 2450,2 2453 | 2451,2 2454 | 2452,2 2455 | 2453,0 2456 | 2454,6 2457 | 2455,6 2458 | 2456,5 2459 | 2457,6 2460 | 2458,6 2461 | 2459,3 2462 | 2460,2 2463 | 2461,6 2464 | 2462,3 2465 | 2463,4 2466 | 2464,4 2467 | 2465,4 2468 | 2466,2 2469 | 2467,6 2470 | 2468,6 2471 | 2469,0 2472 | 2470,0 2473 | 2471,3 2474 | 2472,0 2475 | 2473,4 2476 | 2474,4 2477 | 2475,3 2478 | 2476,2 2479 | 2477,3 2480 | 2478,1 2481 | 2479,6 2482 | 2480,6 2483 | 2481,5 2484 | 2482,3 2485 | 2483,4 2486 | 2484,3 2487 | 2485,5 2488 | 2486,3 2489 | 2487,1 2490 | 2488,1 2491 | 2489,3 2492 | 2490,4 2493 | 2491,5 2494 | 2492,2 2495 | 2493,3 2496 | 2494,3 2497 | 2495,3 2498 | 2496,4 2499 | 2497,5 2500 | 2498,4 2501 | 2499,0 2502 | 2500,3 2503 | 2501,3 2504 | 2502,0 2505 | 2503,2 2506 | 2504,1 2507 | 2505,1 2508 | 2506,5 2509 | 2507,2 2510 | 2508,3 2511 | 2509,3 2512 | 2510,5 2513 | 2511,0 2514 | 2512,2 2515 | 2513,3 2516 | 2514,2 2517 | 2515,2 2518 | 2516,5 2519 | 2517,5 2520 | 2518,4 2521 | 2519,3 2522 | 2520,4 2523 | 2521,3 2524 | 2522,2 2525 | 2523,2 2526 | 2524,4 2527 | 2525,2 2528 | 2526,4 2529 | 2527,5 2530 | 2528,5 2531 | 2529,3 2532 | 2530,2 2533 | 2531,3 2534 | 2532,1 2535 | 2533,0 2536 | 2534,3 2537 | 2535,3 2538 | 2536,4 2539 | 2537,5 2540 | 2538,4 2541 | 2539,3 2542 | 2540,3 2543 | 2541,3 2544 | 2542,3 2545 | 2543,3 2546 | 2544,0 2547 | 2545,1 2548 | 2546,2 2549 | 2547,4 2550 | 2548,4 2551 | 2549,4 2552 | 2550,3 2553 | 2551,3 2554 | 2552,3 2555 | 2553,5 2556 | 2554,2 2557 | 2555,3 2558 | 2556,2 2559 | 2557,2 2560 | 2558,2 2561 | 2559,3 2562 | 2560,2 2563 | 2561,2 2564 | 2562,0 2565 | 2563,4 2566 | 2564,4 2567 | 2565,3 2568 | 2566,3 2569 | 2567,3 2570 | 2568,3 2571 | 2569,3 2572 | 2570,3 2573 | 2571,3 2574 | 2572,3 2575 | 2573,3 2576 | 2574,3 2577 | 2575,0 2578 | 2576,0 2579 | 2577,3 2580 | 2578,0 2581 | 2579,3 2582 | 2580,0 2583 | 2581,2 2584 | 2582,3 2585 | 2583,4 2586 | 2584,1 2587 | 2585,2 2588 | 2586,5 2589 | 2587,4 2590 | 2588,3 2591 | 2589,3 2592 | 2590,3 2593 | 2591,1 2594 | 2592,5 2595 | 2593,3 2596 | 2594,4 2597 | 2595,3 2598 | 2596,2 2599 | 2597,2 2600 | 2598,1 2601 | 2599,3 2602 | 2600,3 2603 | 2601,3 2604 | 2602,3 2605 | 2603,3 2606 | 2604,6 2607 | 2605,3 2608 | 2606,3 2609 | 2607,3 2610 | 2608,6 2611 | 2609,3 2612 | 2610,3 2613 | 2611,3 2614 | 2612,2 2615 | 2613,3 2616 | 2614,2 2617 | 2615,4 2618 | 2616,2 2619 | 2617,4 2620 | 2618,2 2621 | 2619,2 2622 | 2620,1 2623 | 2621,5 2624 | 2622,6 2625 | 2623,4 2626 | 2624,3 2627 | 2625,3 2628 | 2626,3 2629 | 2627,2 2630 | 2628,5 2631 | 2629,3 2632 | 2630,3 2633 | 2631,4 2634 | 2632,3 2635 | 2633,3 2636 | 2634,3 2637 | 2635,3 2638 | 2636,3 2639 | 2637,4 2640 | 2638,6 2641 | 2639,0 2642 | 2640,3 2643 | 2641,2 2644 | 2642,2 2645 | 2643,2 2646 | 2644,5 2647 | 2645,4 2648 | 2646,4 2649 | 2647,4 2650 | 2648,4 2651 | 2649,6 2652 | 2650,3 2653 | 2651,2 2654 | 2652,2 2655 | 2653,0 2656 | 2654,2 2657 | 2655,2 2658 | 2656,2 2659 | 2657,2 2660 | 2658,2 2661 | 2659,3 2662 | 2660,4 2663 | 2661,4 2664 | 2662,4 2665 | 2663,3 2666 | 2664,3 2667 | 2665,4 2668 | 2666,4 2669 | 2667,3 2670 | 2668,3 2671 | 2669,3 2672 | 2670,4 2673 | 2671,4 2674 | 2672,4 2675 | 2673,4 2676 | 2674,4 2677 | 2675,4 2678 | 2676,3 2679 | 2677,4 2680 | 2678,4 2681 | 2679,4 2682 | 2680,4 2683 | 2681,4 2684 | 2682,4 2685 | 2683,4 2686 | 2684,4 2687 | 2685,2 2688 | 2686,3 2689 | 2687,3 2690 | 2688,3 2691 | 2689,2 2692 | 2690,6 2693 | 2691,2 2694 | 2692,3 2695 | 2693,3 2696 | 2694,4 2697 | 2695,4 2698 | 2696,3 2699 | 2697,3 2700 | 2698,3 2701 | 2699,3 2702 | 2700,3 2703 | 2701,3 2704 | 2702,0 2705 | 2703,3 2706 | 2704,3 2707 | 2705,3 2708 | 2706,3 2709 | 2707,3 2710 | -------------------------------------------------------------------------------- /ppnp.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/benedekrozemberczki/APPNP/338d98980ee8748e652630dd422341ef8b2a354a/ppnp.jpg -------------------------------------------------------------------------------- /src/appnp.py: -------------------------------------------------------------------------------- 1 | """Training an APPNP model.""" 2 | 3 | import random 4 | import torch 5 | import numpy as np 6 | from tqdm import trange 7 | from appnp_layer import APPNPModel 8 | 9 | class APPNPTrainer(object): 10 | """ 11 | Method to train PPNP/APPNP model. 12 | """ 13 | def __init__(self, args, graph, features, target): 14 | """ 15 | :param args: Arguments object. 16 | :param graph: Networkx graph. 17 | :param features: Feature matrix. 18 | :param target: Target vector with labels. 19 | """ 20 | self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') 21 | self.args = args 22 | self.graph = graph 23 | self.features = features 24 | self.target = target 25 | self.create_model() 26 | self.train_test_split() 27 | self.transfer_node_sets() 28 | self.process_features() 29 | self.transfer_features() 30 | 31 | def create_model(self): 32 | """ 33 | Defining a model and transfering it to GPU/CPU. 34 | """ 35 | self.node_count = self.graph.number_of_nodes() 36 | self.number_of_labels = np.max(self.target)+1 37 | self.number_of_features = max([f for _, feats in self.features.items() for f in feats])+1 38 | 39 | self.model = APPNPModel(self.args, 40 | self.number_of_labels, 41 | self.number_of_features, 42 | self.graph, 43 | self.device) 44 | 45 | self.model = self.model.to(self.device) 46 | 47 | def train_test_split(self): 48 | """ 49 | Creating a train/test split. 50 | """ 51 | random.seed(self.args.seed) 52 | nodes = [node for node in range(self.node_count)] 53 | random.shuffle(nodes) 54 | self.train_nodes = nodes[0:self.args.train_size] 55 | self.test_nodes = nodes[self.args.train_size:self.args.train_size+self.args.test_size] 56 | self.validation_nodes = nodes[self.args.train_size+self.args.test_size:] 57 | 58 | def transfer_node_sets(self): 59 | """ 60 | Transfering the node sets to the device. 61 | """ 62 | self.train_nodes = torch.LongTensor(self.train_nodes).to(self.device) 63 | self.test_nodes = torch.LongTensor(self.test_nodes).to(self.device) 64 | self.validation_nodes = torch.LongTensor(self.validation_nodes).to(self.device) 65 | 66 | def process_features(self): 67 | """ 68 | Creating a sparse feature matrix and a vector for the target labels. 69 | """ 70 | index_1 = [node for node in self.graph.nodes() for fet in self.features[node]] 71 | index_2 = [fet for node in self.graph.nodes() for fet in self.features[node]] 72 | values = [1.0/len(self.features[node]) for node in self.graph.nodes() for fet in self.features[node]] 73 | self.feature_indices = torch.LongTensor([index_1, index_2]) 74 | self.feature_values = torch.FloatTensor(values) 75 | self.target = torch.LongTensor(self.target) 76 | 77 | def transfer_features(self): 78 | """ 79 | Transfering the features and the target matrix to the device. 80 | """ 81 | self.target = self.target.to(self.device) 82 | self.feature_indices = self.feature_indices.to(self.device) 83 | self.feature_values = self.feature_values.to(self.device) 84 | 85 | def score(self, index_set): 86 | """ 87 | Calculating the accuracy for a given node set. 88 | :param index_set: Index of nodes to be included in calculation. 89 | :parm acc: Accuracy score. 90 | """ 91 | self.model.eval() 92 | _, pred = self.model(self.feature_indices, self.feature_values).max(dim=1) 93 | correct = pred[index_set].eq(self.target[index_set]).sum().item() 94 | acc = correct / index_set.size()[0] 95 | return acc 96 | 97 | def do_a_step(self): 98 | """ 99 | Doing an optimization step. 100 | """ 101 | self.model.train() 102 | self.optimizer.zero_grad() 103 | prediction = self.model(self.feature_indices, self.feature_values) 104 | loss = torch.nn.functional.nll_loss(prediction[self.train_nodes], 105 | self.target[self.train_nodes]) 106 | loss = loss+(self.args.lambd/2)*(torch.sum(self.model.layer_2.weight_matrix**2)) 107 | loss.backward() 108 | self.optimizer.step() 109 | 110 | def train_neural_network(self): 111 | """ 112 | Training a neural network. 113 | """ 114 | print("\nTraining.\n") 115 | self.optimizer = torch.optim.Adam(self.model.parameters(), lr=self.args.learning_rate) 116 | self.best_accuracy = 0 117 | self.step_counter = 0 118 | iterator = trange(self.args.epochs, desc='Validation accuracy: ', leave=True) 119 | for _ in iterator: 120 | self.do_a_step() 121 | accuracy = self.score(self.validation_nodes) 122 | iterator.set_description("Validation accuracy: {:.4f}".format(accuracy)) 123 | if accuracy >= self.best_accuracy: 124 | self.best_accuracy = accuracy 125 | self.test_accuracy = self.score(self.test_nodes) 126 | self.step_counter = 0 127 | else: 128 | self.step_counter = self.step_counter + 1 129 | if self.step_counter > self.args.early_stopping_rounds: 130 | iterator.close() 131 | break 132 | 133 | def fit(self): 134 | """ 135 | Fitting the network and calculating the test accuracy. 136 | """ 137 | self.train_neural_network() 138 | print("\nBreaking from training process because of early stopping.\n") 139 | print("Test accuracy: {:.4f}".format(self.test_accuracy)) 140 | -------------------------------------------------------------------------------- /src/appnp_layer.py: -------------------------------------------------------------------------------- 1 | """APPNP and PPNP layers.""" 2 | 3 | import math 4 | import torch 5 | from torch_sparse import spmm 6 | from utils import create_propagator_matrix 7 | 8 | def uniform(size, tensor): 9 | """ 10 | Uniform weight initialization. 11 | :param size: Size of the tensor. 12 | :param tensor: Tensor initialized. 13 | """ 14 | stdv = 1.0 / math.sqrt(size) 15 | if tensor is not None: 16 | tensor.data.uniform_(-stdv, stdv) 17 | 18 | class DenseFullyConnected(torch.nn.Module): 19 | """ 20 | Abstract class for PageRank and Approximate PageRank networks. 21 | :param in_channels: Number of input channels. 22 | :param out_channels: Number of output channels. 23 | :param density: Feature matrix structure. 24 | """ 25 | def __init__(self, in_channels, out_channels): 26 | super(DenseFullyConnected, self).__init__() 27 | self.in_channels = in_channels 28 | self.out_channels = out_channels 29 | self.define_parameters() 30 | self.init_parameters() 31 | 32 | def define_parameters(self): 33 | """ 34 | Defining the weight matrices. 35 | """ 36 | self.weight_matrix = torch.nn.Parameter(torch.Tensor(self.in_channels, self.out_channels)) 37 | self.bias = torch.nn.Parameter(torch.Tensor(self.out_channels)) 38 | 39 | def init_parameters(self): 40 | """ 41 | Initializing weights. 42 | """ 43 | torch.nn.init.xavier_uniform_(self.weight_matrix) 44 | uniform(self.out_channels, self.bias) 45 | 46 | def forward(self, features): 47 | """ 48 | Doing a forward pass. 49 | :param features: Feature matrix. 50 | :return filtered_features: Convolved features. 51 | """ 52 | filtered_features = torch.mm(features, self.weight_matrix) 53 | filtered_features = filtered_features + self.bias 54 | return filtered_features 55 | 56 | class SparseFullyConnected(torch.nn.Module): 57 | """ 58 | Abstract class for PageRank and Approximate PageRank networks. 59 | :param in_channels: Number of input channels. 60 | :param out_channels: Number of output channels. 61 | :param density: Feature matrix structure. 62 | """ 63 | def __init__(self, in_channels, out_channels): 64 | super(SparseFullyConnected, self).__init__() 65 | self.in_channels = in_channels 66 | self.out_channels = out_channels 67 | self.define_parameters() 68 | self.init_parameters() 69 | 70 | def define_parameters(self): 71 | """ 72 | Defining the weight matrices. 73 | """ 74 | self.weight_matrix = torch.nn.Parameter(torch.Tensor(self.in_channels, self.out_channels)) 75 | self.bias = torch.nn.Parameter(torch.Tensor(self.out_channels)) 76 | 77 | def init_parameters(self): 78 | """ 79 | Initializing weights. 80 | """ 81 | torch.nn.init.xavier_uniform_(self.weight_matrix) 82 | uniform(self.out_channels, self.bias) 83 | 84 | def forward(self, feature_indices, feature_values): 85 | """ 86 | Making a forward pass. 87 | :param feature_indices: Non zero value indices. 88 | :param feature_values: Matrix values. 89 | :return filtered_features: Output features. 90 | """ 91 | number_of_nodes = torch.max(feature_indices[0]).item()+1 92 | number_of_features = torch.max(feature_indices[1]).item()+1 93 | filtered_features = spmm(index = feature_indices, 94 | value = feature_values, 95 | m = number_of_nodes, 96 | n = number_of_features, 97 | matrix = self.weight_matrix) 98 | filtered_features = filtered_features + self.bias 99 | return filtered_features 100 | 101 | class APPNPModel(torch.nn.Module): 102 | """ 103 | APPNP Model Class. 104 | :param args: Arguments object. 105 | :param number_of_labels: Number of target labels. 106 | :param number_of_features: Number of input features. 107 | :param graph: NetworkX graph. 108 | :param device: CPU or GPU. 109 | """ 110 | def __init__(self, args, number_of_labels, number_of_features, graph, device): 111 | super(APPNPModel, self).__init__() 112 | self.args = args 113 | self.number_of_labels = number_of_labels 114 | self.number_of_features = number_of_features 115 | self.graph = graph 116 | self.device = device 117 | self.setup_layers() 118 | self.setup_propagator() 119 | 120 | def setup_layers(self): 121 | """ 122 | Creating layers. 123 | """ 124 | self.layer_1 = SparseFullyConnected(self.number_of_features, self.args.layers[0]) 125 | self.layer_2 = DenseFullyConnected(self.args.layers[1], self.number_of_labels) 126 | 127 | def setup_propagator(self): 128 | """ 129 | Defining propagation matrix (Personalized Pagrerank or adjacency). 130 | """ 131 | self.propagator = create_propagator_matrix(self.graph, self.args.alpha, self.args.model) 132 | if self.args.model == "exact": 133 | self.propagator = self.propagator.to(self.device) 134 | else: 135 | self.edge_indices = self.propagator["indices"].to(self.device) 136 | self.edge_weights = self.propagator["values"].to(self.device) 137 | 138 | def forward(self, feature_indices, feature_values): 139 | """ 140 | Making a forward propagation pass. 141 | :param feature_indices: Feature indices for feature matrix. 142 | :param feature_values: Values in the feature matrix. 143 | :return self.predictions: Predicted class label log softmaxes. 144 | """ 145 | feature_values = torch.nn.functional.dropout(feature_values, 146 | p=self.args.dropout, 147 | training=self.training) 148 | 149 | latent_features_1 = self.layer_1(feature_indices, feature_values) 150 | 151 | latent_features_1 = torch.nn.functional.relu(latent_features_1) 152 | 153 | latent_features_1 = torch.nn.functional.dropout(latent_features_1, 154 | p=self.args.dropout, 155 | training=self.training) 156 | 157 | latent_features_2 = self.layer_2(latent_features_1) 158 | if self.args.model == "exact": 159 | self.predictions = torch.nn.functional.dropout(self.propagator, 160 | p=self.args.dropout, 161 | training=self.training) 162 | 163 | self.predictions = torch.mm(self.predictions, latent_features_2) 164 | else: 165 | localized_predictions = latent_features_2 166 | edge_weights = torch.nn.functional.dropout(self.edge_weights, 167 | p=self.args.dropout, 168 | training=self.training) 169 | 170 | for iteration in range(self.args.iterations): 171 | 172 | new_features = spmm(index=self.edge_indices, 173 | value=edge_weights, 174 | n=localized_predictions.shape[0], 175 | m=localized_predictions.shape[0], 176 | matrix=localized_predictions) 177 | 178 | localized_predictions = (1-self.args.alpha)*new_features 179 | localized_predictions = localized_predictions + self.args.alpha*latent_features_2 180 | self.predictions = localized_predictions 181 | self.predictions = torch.nn.functional.log_softmax(self.predictions, dim=1) 182 | return self.predictions 183 | -------------------------------------------------------------------------------- /src/main.py: -------------------------------------------------------------------------------- 1 | """ Running the APPNP model.""" 2 | 3 | import torch 4 | from appnp import APPNPTrainer 5 | from param_parser import parameter_parser 6 | from utils import tab_printer, graph_reader, feature_reader, target_reader 7 | 8 | def main(): 9 | """ 10 | Parsing command line parameters, reading data, fitting an APPNP/PPNP and scoring the model. 11 | """ 12 | args = parameter_parser() 13 | torch.manual_seed(args.seed) 14 | tab_printer(args) 15 | graph = graph_reader(args.edge_path) 16 | features = feature_reader(args.features_path) 17 | target = target_reader(args.target_path) 18 | trainer = APPNPTrainer(args, graph, features, target) 19 | trainer.fit() 20 | 21 | if __name__ == "__main__": 22 | main() 23 | -------------------------------------------------------------------------------- /src/param_parser.py: -------------------------------------------------------------------------------- 1 | """Getting the parameters from the commandline.""" 2 | 3 | import argparse 4 | 5 | def parameter_parser(): 6 | """ 7 | A method to parse up command line parameters. By default it trains on the Cora dataset. 8 | The default hyperparameters give a good quality representation without grid search. 9 | """ 10 | parser = argparse.ArgumentParser(description="Run PPNP/APPNP.") 11 | 12 | parser.add_argument("--edge-path", 13 | nargs="?", 14 | default="./input/cora_edges.csv", 15 | help="Edge list csv.") 16 | 17 | parser.add_argument("--features-path", 18 | nargs="?", 19 | default="./input/cora_features.json", 20 | help="Features json.") 21 | 22 | parser.add_argument("--target-path", 23 | nargs="?", 24 | default="./input/cora_target.csv", 25 | help="Target classes csv.") 26 | 27 | parser.add_argument("--model", 28 | nargs="?", 29 | default="exact", 30 | help="Model type.") 31 | 32 | parser.add_argument("--epochs", 33 | type=int, 34 | default=2000, 35 | help="Number of training epochs. Default is 2000.") 36 | 37 | parser.add_argument("--seed", 38 | type=int, 39 | default=42, 40 | help="Random seed for train-test split. Default is 42.") 41 | 42 | parser.add_argument("--iterations", 43 | type=int, 44 | default=10, 45 | help="Number of Approximate Personalized PageRank iterations. Default is 10.") 46 | 47 | parser.add_argument("--early-stopping-rounds", 48 | type=int, 49 | default=500, 50 | help="Number of training rounds before early stopping. Default is 10.") 51 | 52 | parser.add_argument("--train-size", 53 | type=int, 54 | default=1500, 55 | help="Training set size. Default is 1500.") 56 | 57 | parser.add_argument("--test-size", 58 | type=int, 59 | default=500, 60 | help="Test set size. Default is 500.") 61 | 62 | parser.add_argument("--dropout", 63 | type=float, 64 | default=0.5, 65 | help="Dropout parameter. Default is 0.5.") 66 | 67 | parser.add_argument("--alpha", 68 | type=float, 69 | default=0.1, 70 | help="Page rank teleport parameter. Default is 0.1.") 71 | 72 | parser.add_argument("--learning-rate", 73 | type=float, 74 | default=0.01, 75 | help="Learning rate. Default is 0.01.") 76 | 77 | parser.add_argument("--lambd", 78 | type=float, 79 | default=0.005, 80 | help="Weight matrix regularization. Default is 0.005.") 81 | 82 | parser.add_argument("--layers", 83 | nargs="+", 84 | type=int, 85 | help="Layer dimensions separated by space. E.g. 64 64.") 86 | 87 | parser.set_defaults(layers=[64, 64]) 88 | 89 | return parser.parse_args() 90 | -------------------------------------------------------------------------------- /src/utils.py: -------------------------------------------------------------------------------- 1 | """Utilities for data manipulation.""" 2 | 3 | import json 4 | import torch 5 | import numpy as np 6 | import pandas as pd 7 | import networkx as nx 8 | from scipy import sparse 9 | from texttable import Texttable 10 | 11 | def tab_printer(args): 12 | """ 13 | Function to print the logs in a nice tabular format. 14 | :param args: Parameters used for the model. 15 | """ 16 | args = vars(args) 17 | keys = sorted(args.keys()) 18 | t = Texttable() 19 | t.add_rows([["Parameter", "Value"]] + [[k.replace("_", " ").capitalize(), args[k]] for k in keys]) 20 | print(t.draw()) 21 | 22 | def graph_reader(path): 23 | """ 24 | Function to read the graph from the path. 25 | :param path: Path to the edge list. 26 | :return graph: NetworkX object returned. 27 | """ 28 | graph = nx.from_edgelist(pd.read_csv(path).values.tolist()) 29 | graph.remove_edges_from(nx.selfloop_edges(graph)) 30 | return graph 31 | 32 | def feature_reader(path): 33 | """ 34 | Reading the feature matrix stored as JSON from the disk. 35 | :param path: Path to the JSON file. 36 | :return out_features: Dict with index and value tensor. 37 | """ 38 | features = json.load(open(path)) 39 | features = {int(k): [int(val) for val in v] for k, v in features.items()} 40 | return features 41 | 42 | def target_reader(path): 43 | """ 44 | Reading the target vector from disk. 45 | :param path: Path to the target. 46 | :return target: Target vector. 47 | """ 48 | target = np.array(pd.read_csv(path)["target"]) 49 | return target 50 | 51 | def create_adjacency_matrix(graph): 52 | """ 53 | Creating a sparse adjacency matrix. 54 | :param graph: NetworkX object. 55 | :return A: Adjacency matrix. 56 | """ 57 | index_1 = [edge[0] for edge in graph.edges()] + [edge[1] for edge in graph.edges()] 58 | index_2 = [edge[1] for edge in graph.edges()] + [edge[0] for edge in graph.edges()] 59 | values = [1 for edge in index_1] 60 | node_count = max(max(index_1)+1, max(index_2)+1) 61 | A = sparse.coo_matrix((values, (index_1, index_2)), shape=(node_count, node_count), dtype=np.float32) 62 | return A 63 | 64 | def normalize_adjacency_matrix(A, I): 65 | """ 66 | Creating a normalized adjacency matrix with self loops. 67 | :param A: Sparse adjacency matrix. 68 | :param I: Identity matrix. 69 | :return A_tile_hat: Normalized adjacency matrix. 70 | """ 71 | A_tilde = A + I 72 | degrees = A_tilde.sum(axis=0)[0].tolist() 73 | D = sparse.diags(degrees, [0]) 74 | D = D.power(-0.5) 75 | A_tilde_hat = D.dot(A_tilde).dot(D) 76 | return A_tilde_hat 77 | 78 | def create_propagator_matrix(graph, alpha, model): 79 | """ 80 | Creating apropagation matrix. 81 | :param graph: NetworkX graph. 82 | :param alpha: Teleport parameter. 83 | :param model: Type of model exact or approximate. 84 | :return propagator: Propagator matrix Dense torch matrix / 85 | dict with indices and values for sparse multiplication. 86 | """ 87 | A = create_adjacency_matrix(graph) 88 | I = sparse.eye(A.shape[0]) 89 | A_tilde_hat = normalize_adjacency_matrix(A, I) 90 | if model == "exact": 91 | propagator = (I-(1-alpha)*A_tilde_hat).todense() 92 | propagator = alpha*torch.inverse(torch.FloatTensor(propagator)) 93 | else: 94 | propagator = dict() 95 | A_tilde_hat = sparse.coo_matrix(A_tilde_hat) 96 | indices = np.concatenate([A_tilde_hat.row.reshape(-1, 1), A_tilde_hat.col.reshape(-1, 1)], axis=1).T 97 | propagator["indices"] = torch.LongTensor(indices) 98 | propagator["values"] = torch.FloatTensor(A_tilde_hat.data) 99 | return propagator 100 | --------------------------------------------------------------------------------