├── .github └── FUNDING.yml ├── .gitignore ├── LICENSE ├── README.md ├── input ├── ER_edges.csv ├── bitcoin_alpha.csv └── bitcoin_otc.csv ├── logs └── bitcoin_otc_logs.json ├── output ├── embedding │ └── bitcoin_otc_sgcn.csv └── weights │ └── bitcoin_otc_sgcn.csv ├── sgcn.jpg ├── sgcn.pdf ├── sgcn_run_example.png └── src ├── main.py ├── param_parser.py ├── sgcn.py ├── signedsageconvolution.py └── utils.py /.github/FUNDING.yml: -------------------------------------------------------------------------------- 1 | github: [benedekrozemberczki] 2 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | */__pycache__/ 4 | *.py[cod] 5 | *$py.class 6 | 7 | # C extensions 8 | *.so 9 | 10 | # Distribution / packaging 11 | .Python 12 | build/ 13 | develop-eggs/ 14 | dist/ 15 | downloads/ 16 | eggs/ 17 | .eggs/ 18 | lib/ 19 | lib64/ 20 | parts/ 21 | sdist/ 22 | var/ 23 | wheels/ 24 | pip-wheel-metadata/ 25 | share/python-wheels/ 26 | *.egg-info/ 27 | .installed.cfg 28 | *.egg 29 | MANIFEST 30 | 31 | # PyInstaller 32 | # Usually these files are written by a python script from a template 33 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 34 | *.manifest 35 | *.spec 36 | 37 | # Installer logs 38 | pip-log.txt 39 | pip-delete-this-directory.txt 40 | 41 | # Unit test / coverage reports 42 | htmlcov/ 43 | .tox/ 44 | .nox/ 45 | .coverage 46 | .coverage.* 47 | .cache 48 | nosetests.xml 49 | coverage.xml 50 | *.cover 51 | *.py,cover 52 | .hypothesis/ 53 | .pytest_cache/ 54 | 55 | # Translations 56 | *.mo 57 | *.pot 58 | 59 | # Django stuff: 60 | *.log 61 | local_settings.py 62 | db.sqlite3 63 | db.sqlite3-journal 64 | 65 | # Flask stuff: 66 | instance/ 67 | .webassets-cache 68 | 69 | # Scrapy stuff: 70 | .scrapy 71 | 72 | # Sphinx documentation 73 | docs/_build/ 74 | 75 | # PyBuilder 76 | target/ 77 | 78 | # Jupyter Notebook 79 | .ipynb_checkpoints 80 | 81 | # IPython 82 | profile_default/ 83 | ipython_config.py 84 | 85 | # pyenv 86 | .python-version 87 | 88 | # pipenv 89 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 90 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 91 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 92 | # install all needed dependencies. 93 | #Pipfile.lock 94 | 95 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 96 | __pypackages__/ 97 | 98 | # Celery stuff 99 | celerybeat-schedule 100 | celerybeat.pid 101 | 102 | # SageMath parsed files 103 | *.sage.py 104 | 105 | # Environments 106 | .env 107 | .venv 108 | env/ 109 | venv/ 110 | ENV/ 111 | env.bak/ 112 | venv.bak/ 113 | 114 | # Spyder project settings 115 | .spyderproject 116 | .spyproject 117 | 118 | # Rope project settings 119 | .ropeproject 120 | 121 | # mkdocs documentation 122 | /site 123 | 124 | # mypy 125 | .mypy_cache/ 126 | .dmypy.json 127 | dmypy.json 128 | 129 | # Pyre type checker 130 | .pyre/ 131 | 132 | # Output files 133 | *Output/ 134 | 135 | # Data sets 136 | *dataset/ 137 | 138 | # saved models 139 | *.t7 140 | -------------------------------------------------------------------------------- /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 | SGCN 2 | ==================== 3 | [![Arxiv](https://img.shields.io/badge/ArXiv-1808.06354-orange.svg?color=blue)](https://arxiv.org/abs/1808.06354) [![repo size](https://img.shields.io/github/repo-size/benedekrozemberczki/SGCN.svg)](https://github.com/benedekrozemberczki/SGCN/archive/master.zip) 4 | [![codebeat badge](https://codebeat.co/badges/f967cf53-f43a-4159-bb24-50f2d7fc1776)](https://codebeat.co/projects/github-com-benedekrozemberczki-sgcn-master) [![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 **Signed Graph Convolutional Network (ICDM 2018)**. 7 | 8 |
9 | 10 | ### Abstract 11 |

12 | Due to the fact much of today's data can be represented as graphs, there has been a demand for generalizing neural network models for graph data. One recent direction that has shown fruitful results, and therefore growing interest, is the usage of graph convolutional neural networks (GCNs). They have been shown to provide a significant improvement on a wide range of tasks in network analysis, one of which being node representation learning. The task of learning low-dimensional node representations has shown to increase performance on a plethora of other tasks from link prediction and node classification, to community detection and visualization. Simultaneously, signed networks (or graphs having both positive and negative links) have become ubiquitous with the growing popularity of social media. However, since previous GCN models have primarily focused on unsigned networks (or graphs consisting of only positive links), it is unclear how they could be applied to signed networks due to the challenges presented by negative links. The primary challenges are based on negative links having not only a different semantic meaning as compared to positive links, but their principles are inherently different and they form complex relations with positive links. Therefore we propose a dedicated and principled effort that utilizes balance theory to correctly aggregate and propagate the information across layers of a signed GCN model. We perform empirical experiments comparing our proposed signed GCN against state-of-the-art baselines for learning node representations in signed networks. More specifically, our experiments are performed on four real-world datasets for the classical link sign prediction problem that is commonly used as the benchmark for signed network embeddings algorithms.

13 | 14 | This repository provides an implementation for SGCN as described in the paper: 15 | 16 | > Signed Graph Convolutional Network. 17 | > Tyler Derr, Yao Ma, and Jiliang Tang 18 | > ICDM, 2018. 19 | > [[Paper]](https://arxiv.org/abs/1808.06354) 20 | 21 | The original implementation is available [[here]](https://www.cse.msu.edu/~derrtyle/code/SGCN.zip) and SGCN is also available in [[PyTorch Geometric]](https://github.com/rusty1s/pytorch_geometric). 22 | 23 | ### Requirements 24 | 25 | The codebase is implemented in Python 3.5.2. package versions used for development are just below. 26 | ``` 27 | networkx 2.4 28 | tqdm 4.28.1 29 | numpy 1.15.4 30 | pandas 0.23.4 31 | texttable 1.5.0 32 | scipy 1.1.0 33 | argparse 1.1.0 34 | sklearn 0.20.0 35 | torch 1.1.0 36 | torch-scatter 1.4.0 37 | torch-sparse 0.4.3 38 | torch-cluster 1.4.5 39 | torch-geometric 1.3.2 40 | torchvision 0.3.0 41 | ``` 42 | -------------------------------------------- 43 | ### Datasets 44 |

45 | 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. Nodes should be indexed starting with 0. Sample graphs for the `Bitcoin Alpha` and `Bitcoin OTC` graphs are included in the `input/` directory. The structure of the edge dataset is the following: 46 |

47 | 48 | | **NODE ID 1**| **NODE ID 2** | **Sign** | 49 | | --- | --- | --- | 50 | | 0 | 3 |-1 | 51 | | 1 | 1 |1 | 52 | | 2 | 2 |1 | 53 | | 3 | 1 |-1 | 54 | | ... | ... |... | 55 | | n | 9 |-1 | 56 | 57 |

58 | An attributed dataset for an `Erdos-Renyi` graph is also included in the input folder. **The node feature dataset rows are sorted by ID increasing**. The structure of the features csv has to be the following: 59 |

60 | 61 | | **Feature 1** | **Feature 2** | **Feature 3** |...| **Feature d** | 62 | | --- | --- | --- | --- |--- | 63 | | 3 |0 |1.37 |... |1 | 64 | | 1 |1 |2.54 |... |-11 | 65 | | 2 |0 |1.08 |... |-12 | 66 | | 1 |1 |1.22 |... |-4 | 67 | | . ... |... |... |... |... | 68 | | 5 |0 |2.47 |... |21 | 69 | 70 | ### Options 71 |

72 | Learning an embedding is handled by the `src/main.py` script which provides the following command line arguments. 73 |

74 | 75 | #### Input and output options 76 | 77 | ``` 78 | --edge-path STR Input graph path. Default is `input/bitcoin_otc.csv`. 79 | --features-path STR Features path. Default is `input/bitcoin_otc.csv`. 80 | --embedding-path STR Embedding path. Default is `output/embedding/bitcoin_otc_sgcn.csv`. 81 | --regression-weights-path STR Regression weights path. Default is `output/weights/bitcoin_otc_sgcn.csv`. 82 | --log-path STR Log path. Default is `logs/bitcoin_otc_logs.json`. 83 | ``` 84 | 85 | #### Model options 86 | 87 | ``` 88 | --epochs INT Number of SGCN training epochs. Default is 100. 89 | --reduction-iterations INT Number of SVD epochs. Default is 128. 90 | --reduction-dimensions INT SVD dimensions. Default is 30. 91 | --seed INT Random seed value. Default is 42. 92 | --lamb FLOAT Embedding regularization parameter. Default is 1.0. 93 | --test-size FLOAT Test ratio. Default is 0.2. 94 | --learning-rate FLOAT Learning rate. Default is 0.01. 95 | --weight-decay FLOAT Weight decay. Default is 10^-5. 96 | --layers LST Layer sizes in model. Default is [32, 32]. 97 | --spectral-features BOOL Layer sizes in autoencoder model. Default is True 98 | --general-features BOOL Loss calculation for the model. Sets spectral features to False. 99 | ``` 100 | 101 | ### Examples 102 | 103 | The following commands learn a node embedding, regression parameters and write the embedding to disk. The node representations are ordered by the ID. The layer sizes can be set manually. 104 | 105 | Training an SGCN model on the default dataset. Saving the embedding, regression weights and logs at default paths. 106 | The regression weight file contains weights and bias (last column). 107 | ``` 108 | python src/main.py 109 | ``` 110 |

111 | 112 |

113 | 114 | ``pos_ratio" in the output table gives the ratio of test links predicted to be positive. 115 | 116 | Creating an SGCN model of the default dataset with a 96-64-32 architecture. 117 | ``` 118 | python src/main.py --layers 96 64 32 119 | ``` 120 | Creating a single layer SGCN model with 32 features. 121 | ``` 122 | python src/main.py --layers 32 123 | ``` 124 | Creating a model with some custom learning rate and epoch number. 125 | ``` 126 | python src/main.py --learning-rate 0.001 --epochs 200 127 | ``` 128 | Training a model on another dataset with features present - a signed `Erdos-Renyi` graph. Saving the weights, output and logs in a custom folder. 129 | ``` 130 | python src/main.py --general-features --edge-path input/erdos_renyi_edges.csv --features-path input/erdos_renyi_features.csv --embedding-path output/embedding/erdos_renyi.csv --regression-weights-path output/weights/erdos_renyi.csv --log-path logs/erdos_renyi.json 131 | ``` 132 | 133 | -------------------------------------------------------------------------------- 134 | 135 | **License** 136 | 137 | - [GNU](https://github.com/benedekrozemberczki/SGCN/blob/master/LICENSE) 138 | 139 | -------------------------------------------------------------------------------- 140 | -------------------------------------------------------------------------------- /input/ER_edges.csv: -------------------------------------------------------------------------------- 1 | from,to,sign 2 | 821,902,1 3 | 318,549,1 4 | 504,872,1 5 | 265,507,1 6 | 225,356,1 7 | 18,491,1 8 | 106,736,1 9 | 482,803,1 10 | 645,667,1 11 | 534,554,1 12 | 184,354,1 13 | 367,885,1 14 | 295,895,1 15 | 117,786,1 16 | 248,489,1 17 | 170,440,1 18 | 538,753,1 19 | 51,538,1 20 | 85,512,1 21 | 98,776,1 22 | 561,664,1 23 | 317,804,1 24 | 331,798,1 25 | 108,869,1 26 | 708,997,1 27 | 309,640,1 28 | 12,707,1 29 | 19,346,1 30 | 55,902,1 31 | 528,838,1 32 | 53,821,1 33 | 22,834,1 34 | 101,543,1 35 | 756,932,1 36 | 723,831,1 37 | 625,954,1 38 | 213,385,1 39 | 78,441,1 40 | 739,828,1 41 | 461,975,1 42 | 533,975,1 43 | 213,516,1 44 | 428,458,1 45 | 151,512,1 46 | 143,932,1 47 | 615,881,1 48 | 804,964,1 49 | 20,809,1 50 | 4,647,1 51 | 350,628,1 52 | 940,976,1 53 | 799,933,1 54 | 60,982,1 55 | 397,647,1 56 | 175,458,1 57 | 338,741,1 58 | 389,411,1 59 | 409,499,1 60 | 52,277,1 61 | 304,360,1 62 | 786,917,1 63 | 127,266,1 64 | 463,860,1 65 | 429,865,1 66 | 141,695,1 67 | 8,620,1 68 | 588,643,1 69 | 275,381,1 70 | 325,684,1 71 | 245,817,1 72 | 381,963,1 73 | 260,401,1 74 | 131,344,1 75 | 411,439,1 76 | 246,790,1 77 | 209,666,1 78 | 99,448,1 79 | 67,776,1 80 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462 | 594,697,1 463 | 705,849,1 464 | 519,536,1 465 | 749,955,1 466 | 61,714,1 467 | 522,555,1 468 | 90,480,1 469 | 310,753,1 470 | 342,595,1 471 | 622,771,1 472 | 188,650,1 473 | 223,261,1 474 | 122,749,1 475 | 172,890,1 476 | 36,666,1 477 | 61,478,1 478 | 626,921,1 479 | 300,987,1 480 | 291,911,1 481 | 11,760,1 482 | 818,987,1 483 | 117,860,1 484 | 789,793,1 485 | 23,51,1 486 | 368,419,1 487 | 7,145,1 488 | 6,243,1 489 | 48,593,1 490 | 755,790,1 491 | 249,594,1 492 | 741,886,1 493 | 241,434,1 494 | 323,857,1 495 | 210,726,1 496 | 573,635,1 497 | 347,900,1 498 | 527,549,1 499 | 372,982,1 500 | 365,645,1 501 | 117,290,1 502 | 122,848,1 503 | 63,132,1 504 | 34,760,1 505 | 448,565,1 506 | 235,667,1 507 | 267,671,1 508 | 118,861,1 509 | 121,817,1 510 | 352,459,1 511 | 831,967,1 512 | 175,892,1 513 | 413,870,1 514 | 180,707,1 515 | 270,942,1 516 | 68,934,1 517 | 408,533,1 518 | 376,884,1 519 | 682,886,1 520 | 246,408,1 521 | 621,631,1 522 | 63,897,1 523 | 695,881,1 524 | 639,944,1 525 | 135,415,1 526 | 779,841,1 527 | 147,933,1 528 | 190,500,1 529 | 76,698,1 530 | 535,772,1 531 | 490,494,1 532 | 690,806,1 533 | 121,559,1 534 | 289,403,1 535 | 59,192,1 536 | 73,979,1 537 | 112,303,1 538 | 184,581,1 539 | 461,618,1 540 | 24,154,1 541 | 62,268,1 542 | 154,468,1 543 | 384,706,1 544 | 686,778,1 545 | 187,214,1 546 | 678,686,1 547 | 73,261,1 548 | 93,837,1 549 | 222,871,1 550 | 2,439,1 551 | 469,894,1 552 | 17,138,1 553 | 157,838,1 554 | 723,878,1 555 | 210,633,1 556 | 111,160,1 557 | 540,575,1 558 | 634,698,1 559 | 430,739,1 560 | 70,956,1 561 | 120,928,1 562 | 743,776,1 563 | 1,512,1 564 | 31,198,1 565 | 638,813,1 566 | 39,996,1 567 | 707,797,1 568 | 319,443,1 569 | 41,819,1 570 | 824,910,1 571 | 25,971,1 572 | 155,786,1 573 | 420,921,1 574 | 597,687,1 575 | 817,877,1 576 | 494,549,1 577 | 335,992,1 578 | 695,824,1 579 | 561,775,1 580 | 7,922,1 581 | 776,798,1 582 | 147,418,1 583 | 232,471,1 584 | 372,892,1 585 | 664,915,1 586 | 256,690,1 587 | 273,857,1 588 | 559,878,1 589 | 631,889,1 590 | 159,356,1 591 | 908,960,1 592 | 769,872,1 593 | 319,468,1 594 | 422,578,1 595 | 303,959,1 596 | 139,330,1 597 | 256,870,1 598 | 72,863,1 599 | 85,915,1 600 | 713,902,1 601 | 193,960,1 602 | 204,582,1 603 | 69,318,1 604 | 487,766,1 605 | 87,377,1 606 | 319,394,1 607 | 98,559,1 608 | 48,145,1 609 | 41,551,1 610 | 388,817,1 611 | 98,703,1 612 | 573,971,1 613 | 456,571,1 614 | 525,771,1 615 | 169,675,1 616 | 580,768,1 617 | 433,895,1 618 | 98,939,1 619 | 145,722,1 620 | 392,559,1 621 | 571,730,1 622 | 639,982,1 623 | 143,738,1 624 | 436,818,1 625 | 455,948,1 626 | 478,549,1 627 | 477,801,1 628 | 412,718,1 629 | 47,963,1 630 | 76,733,1 631 | 571,969,1 632 | 746,975,1 633 | 796,803,1 634 | 370,777,1 635 | 311,498,1 636 | 690,876,1 637 | 172,780,1 638 | 458,677,1 639 | 219,987,1 640 | 99,975,1 641 | 471,718,1 642 | 357,829,1 643 | 121,132,1 644 | 535,626,1 645 | 159,383,1 646 | 12,690,1 647 | 830,835,1 648 | 613,934,1 649 | 575,870,1 650 | 746,899,1 651 | 130,171,1 652 | 41,468,1 653 | 485,902,1 654 | 47,278,1 655 | 44,667,1 656 | 40,635,1 657 | 134,189,1 658 | 106,171,1 659 | 386,762,1 660 | 675,760,1 661 | 9,905,1 662 | 726,799,1 663 | 343,356,1 664 | 575,854,1 665 | 652,794,1 666 | 33,295,1 667 | 382,579,1 668 | 504,938,1 669 | 587,623,1 670 | 324,518,1 671 | 489,824,1 672 | 549,969,1 673 | 567,967,1 674 | 404,678,1 675 | 620,995,1 676 | 585,717,1 677 | 249,726,1 678 | 265,917,1 679 | 549,808,1 680 | 514,553,1 681 | 375,907,1 682 | 319,871,1 683 | 348,366,1 684 | 463,726,1 685 | 718,785,1 686 | 393,614,1 687 | 286,559,1 688 | 178,808,1 689 | 68,703,1 690 | 708,824,1 691 | 359,833,1 692 | 70,944,1 693 | 770,844,1 694 | 61,156,1 695 | 410,887,1 696 | 676,789,1 697 | 395,524,1 698 | 128,512,1 699 | 542,599,1 700 | 594,829,1 701 | 10,630,1 702 | 243,477,1 703 | 43,660,1 704 | 373,804,1 705 | 115,198,1 706 | 374,390,1 707 | 15,86,1 708 | 120,210,1 709 | 212,993,1 710 | 328,594,1 711 | 622,668,1 712 | 215,216,1 713 | 138,472,1 714 | 367,589,1 715 | 912,993,1 716 | 466,829,1 717 | 278,691,1 718 | 414,558,1 719 | 300,324,1 720 | 185,337,1 721 | 189,470,1 722 | 673,981,1 723 | 596,983,1 724 | 205,548,1 725 | 422,789,1 726 | 483,535,1 727 | 724,818,1 728 | 72,911,1 729 | 844,853,1 730 | 597,934,1 731 | 704,809,1 732 | 320,970,1 733 | 193,862,1 734 | 516,828,1 735 | 211,722,1 736 | 4,95,1 737 | 9,546,1 738 | 202,611,1 739 | 181,644,1 740 | 54,726,1 741 | 926,957,1 742 | 200,674,1 743 | 48,343,1 744 | 34,624,1 745 | 196,769,1 746 | 770,783,1 747 | 566,625,1 748 | 217,344,1 749 | 157,603,1 750 | 297,923,1 751 | 484,926,1 752 | 555,620,1 753 | 539,697,1 754 | 413,606,1 755 | 597,985,1 756 | 812,972,1 757 | 478,697,1 758 | 541,986,1 759 | 16,651,1 760 | 19,675,1 761 | 421,454,1 762 | 34,569,1 763 | 232,544,1 764 | 454,819,1 765 | 202,710,1 766 | 240,441,1 767 | 108,481,1 768 | 3,468,1 769 | 321,464,1 770 | 285,725,1 771 | 536,694,1 772 | 106,929,1 773 | 603,872,1 774 | 94,541,1 775 | 304,949,1 776 | 470,670,1 777 | 303,427,1 778 | 217,596,1 779 | 190,583,1 780 | 126,655,1 781 | 97,149,1 782 | 235,756,1 783 | 773,995,1 784 | 705,797,1 785 | 170,454,1 786 | 345,619,1 787 | 314,482,1 788 | 166,617,1 789 | 207,809,1 790 | 162,736,1 791 | 557,707,1 792 | 675,989,1 793 | 77,711,1 794 | 148,323,1 795 | 220,535,1 796 | 13,717,1 797 | 3,16,1 798 | 612,663,1 799 | 128,932,1 800 | 218,851,1 801 | 363,520,1 802 | 63,909,1 803 | 354,566,1 804 | 17,669,1 805 | 616,771,1 806 | 483,533,1 807 | 98,480,1 808 | 85,652,1 809 | 730,913,1 810 | 29,697,1 811 | 305,681,1 812 | 220,722,1 813 | 150,545,1 814 | 199,737,1 815 | 10,13,1 816 | 247,250,1 817 | 667,902,1 818 | 391,886,1 819 | 659,788,1 820 | 36,195,1 821 | 17,98,1 822 | 723,763,1 823 | 67,536,1 824 | 534,891,1 825 | 146,248,1 826 | 448,644,1 827 | 116,183,1 828 | 307,833,1 829 | 19,321,1 830 | 550,793,1 831 | 287,722,1 832 | 29,549,1 833 | 14,89,1 834 | 548,850,1 835 | 77,103,1 836 | 33,346,1 837 | 233,627,1 838 | 147,961,1 839 | 641,936,1 840 | 270,521,1 841 | 352,826,1 842 | 758,831,1 843 | 866,981,1 844 | 26,323,1 845 | 183,884,1 846 | 544,555,1 847 | 437,759,1 848 | 41,105,1 849 | 133,199,1 850 | 97,425,1 851 | 122,901,1 852 | 257,944,1 853 | 89,383,1 854 | 546,588,1 855 | 457,488,1 856 | 313,754,1 857 | 133,513,1 858 | 176,761,1 859 | 54,791,1 860 | 225,838,1 861 | 484,493,1 862 | 147,945,1 863 | 665,906,1 864 | 273,528,1 865 | 320,807,1 866 | 763,803,1 867 | 329,353,1 868 | 905,923,1 869 | 241,306,1 870 | 100,734,1 871 | 827,927,1 872 | 579,864,1 873 | 74,916,1 874 | 28,772,1 875 | 206,606,1 876 | 467,665,1 877 | 33,638,1 878 | 41,700,1 879 | 422,947,1 880 | 288,325,1 881 | 200,686,1 882 | 216,552,1 883 | 432,901,1 884 | 25,848,1 885 | 336,689,1 886 | 5,641,1 887 | 23,55,1 888 | 309,868,1 889 | 39,394,1 890 | 320,474,1 891 | 440,780,1 892 | 832,839,1 893 | 533,595,1 894 | 277,657,1 895 | 441,904,1 896 | 41,110,1 897 | 458,999,1 898 | 307,785,1 899 | 319,635,1 900 | 8,84,1 901 | 58,463,1 902 | 21,167,1 903 | 87,505,1 904 | 264,465,1 905 | 345,893,1 906 | 692,936,1 907 | 572,834,1 908 | 161,169,1 909 | 337,910,1 910 | 379,987,1 911 | 71,519,1 912 | 453,913,1 913 | 46,184,1 914 | 460,948,1 915 | 23,845,1 916 | 735,998,1 917 | 455,514,1 918 | 53,259,1 919 | 167,679,1 920 | 36,879,1 921 | 151,877,1 922 | 311,465,1 923 | 152,753,1 924 | 469,575,1 925 | 412,922,1 926 | 378,929,1 927 | 907,946,1 928 | 167,901,1 929 | 676,864,1 930 | 286,468,1 931 | 469,842,1 932 | 325,665,1 933 | 556,735,1 934 | 237,737,1 935 | 292,790,1 936 | 192,261,1 937 | 45,603,1 938 | 274,358,1 939 | 128,931,1 940 | 31,124,1 941 | 309,441,1 942 | 205,979,1 943 | 183,821,1 944 | 176,558,1 945 | 546,889,1 946 | 173,976,1 947 | 341,412,1 948 | 659,840,1 949 | 279,464,1 950 | 14,433,1 951 | 146,432,1 952 | 231,333,1 953 | 732,884,1 954 | 288,469,1 955 | 9,175,1 956 | 529,594,1 957 | 861,873,1 958 | 19,514,1 959 | 376,461,1 960 | 894,965,1 961 | 385,438,1 962 | 58,780,1 963 | 43,841,1 964 | 429,771,1 965 | 286,907,1 966 | 487,701,1 967 | 93,609,1 968 | 249,423,1 969 | 36,727,1 970 | 384,783,1 971 | 442,703,1 972 | 18,26,1 973 | 4,884,1 974 | 224,544,1 975 | 389,463,1 976 | 120,872,1 977 | 466,600,1 978 | 610,918,1 979 | 333,508,1 980 | 605,780,1 981 | 608,858,1 982 | 786,843,1 983 | 820,919,1 984 | 253,872,1 985 | 159,988,1 986 | 118,761,1 987 | 9,624,1 988 | 99,979,1 989 | 379,531,1 990 | 145,885,1 991 | 189,893,1 992 | 399,712,1 993 | 26,360,1 994 | 573,835,1 995 | 458,801,1 996 | 633,734,1 997 | 267,490,1 998 | 296,490,1 999 | 643,982,1 1000 | 57,631,1 1001 | 139,501,1 1002 | 403,975,1 1003 | 715,787,1 1004 | 401,729,1 1005 | 597,715,1 1006 | 192,831,1 1007 | 151,306,1 1008 | 138,165,1 1009 | 428,722,1 1010 | 245,719,1 1011 | 357,391,1 1012 | 408,771,1 1013 | 48,873,1 1014 | 638,878,1 1015 | 411,454,1 1016 | 500,771,1 1017 | 83,377,1 1018 | 99,602,1 1019 | 299,411,1 1020 | 49,441,1 1021 | 477,494,1 1022 | 157,520,1 1023 | 392,513,1 1024 | 145,387,1 1025 | 341,856,1 1026 | 231,907,1 1027 | 234,642,1 1028 | 21,493,1 1029 | 664,799,1 1030 | 399,899,1 1031 | 423,986,1 1032 | 477,685,1 1033 | 236,534,1 1034 | 663,897,1 1035 | 666,902,1 1036 | 248,853,1 1037 | 93,785,1 1038 | 282,396,1 1039 | 18,329,1 1040 | 312,963,1 1041 | 70,337,1 1042 | 721,825,1 1043 | 269,545,1 1044 | 158,700,1 1045 | 153,649,1 1046 | 871,875,1 1047 | 518,644,1 1048 | 424,497,1 1049 | 547,862,1 1050 | 264,517,1 1051 | 306,484,1 1052 | 197,776,1 1053 | 449,658,1 1054 | 120,455,1 1055 | 216,338,1 1056 | 854,933,1 1057 | 442,948,1 1058 | 874,902,1 1059 | 550,740,1 1060 | 701,923,1 1061 | 115,435,1 1062 | 662,771,1 1063 | 176,513,1 1064 | 51,282,1 1065 | 84,829,1 1066 | 83,992,1 1067 | 229,641,1 1068 | 289,822,1 1069 | 371,932,1 1070 | 159,576,1 1071 | 640,868,1 1072 | 76,254,1 1073 | 121,966,1 1074 | 389,914,1 1075 | 522,709,1 1076 | 281,506,1 1077 | 846,994,1 1078 | 67,453,1 1079 | 34,810,1 1080 | 143,552,1 1081 | 158,414,1 1082 | 317,643,1 1083 | 204,743,1 1084 | 961,975,1 1085 | 393,755,1 1086 | 235,394,1 1087 | 312,915,1 1088 | 329,603,1 1089 | 362,834,1 1090 | 370,728,1 1091 | 273,824,1 1092 | 394,583,1 1093 | 3,641,1 1094 | 496,917,1 1095 | 342,632,1 1096 | 142,884,1 1097 | 220,332,1 1098 | 509,710,1 1099 | 14,976,1 1100 | 695,961,1 1101 | 303,458,1 1102 | 868,984,1 1103 | 761,973,1 1104 | 133,549,1 1105 | 52,67,1 1106 | 244,742,1 1107 | 566,620,1 1108 | 427,800,1 1109 | 269,963,1 1110 | 233,328,1 1111 | 576,872,1 1112 | 86,827,1 1113 | 449,971,1 1114 | 300,613,1 1115 | 511,919,1 1116 | 259,460,1 1117 | 98,507,1 1118 | 404,474,1 1119 | 30,995,1 1120 | 522,543,1 1121 | 93,765,1 1122 | 386,814,1 1123 | 182,548,1 1124 | 198,199,1 1125 | 102,899,1 1126 | 179,367,1 1127 | 461,528,1 1128 | 211,779,1 1129 | 111,268,1 1130 | 620,939,1 1131 | 503,792,1 1132 | 227,397,1 1133 | 48,749,1 1134 | 198,914,1 1135 | 123,984,1 1136 | 1,707,1 1137 | 195,363,1 1138 | 116,155,1 1139 | 137,590,1 1140 | 48,144,1 1141 | 441,843,1 1142 | 860,966,1 1143 | 488,756,1 1144 | 956,989,1 1145 | 166,217,1 1146 | 570,852,1 1147 | 322,879,1 1148 | 278,920,1 1149 | 43,719,1 1150 | 35,190,1 1151 | 313,372,1 1152 | 524,674,1 1153 | 157,551,1 1154 | 248,680,1 1155 | 606,668,1 1156 | 529,617,1 1157 | 67,812,1 1158 | 62,671,1 1159 | 144,931,1 1160 | 48,833,1 1161 | 296,997,1 1162 | 378,730,1 1163 | 96,970,1 1164 | 934,997,1 1165 | 104,627,1 1166 | 277,898,1 1167 | 290,532,1 1168 | 128,552,1 1169 | 464,644,1 1170 | 77,910,1 1171 | 60,352,1 1172 | 409,879,1 1173 | 874,901,1 1174 | 718,949,1 1175 | 6,714,1 1176 | 223,566,1 1177 | 183,598,1 1178 | 177,520,1 1179 | 835,861,1 1180 | 212,728,1 1181 | 614,760,1 1182 | 670,924,1 1183 | 535,788,1 1184 | 134,968,1 1185 | 375,499,1 1186 | 155,622,1 1187 | 5,871,1 1188 | 182,991,1 1189 | 296,866,1 1190 | 381,590,1 1191 | 23,339,1 1192 | 59,850,1 1193 | 453,767,1 1194 | 731,941,1 1195 | 744,972,1 1196 | 926,943,1 1197 | 79,595,1 1198 | 116,669,1 1199 | 708,808,1 1200 | 247,532,1 1201 | 72,130,1 1202 | 708,993,1 1203 | 156,286,1 1204 | 37,607,1 1205 | 623,789,1 1206 | 462,662,1 1207 | 472,776,1 1208 | 67,842,1 1209 | 670,745,1 1210 | 80,769,1 1211 | 734,953,1 1212 | 64,454,1 1213 | 620,837,1 1214 | 632,839,1 1215 | 93,780,1 1216 | 899,976,1 1217 | 487,888,1 1218 | 535,579,1 1219 | 181,936,1 1220 | 3,47,1 1221 | 390,494,1 1222 | 58,80,1 1223 | 341,951,1 1224 | 161,582,1 1225 | 137,615,1 1226 | 448,608,1 1227 | 248,427,1 1228 | 184,388,1 1229 | 466,702,1 1230 | 190,567,1 1231 | 449,753,1 1232 | 160,199,1 1233 | 639,978,1 1234 | 244,751,1 1235 | 50,363,1 1236 | 295,371,1 1237 | 181,721,1 1238 | 394,437,1 1239 | 136,918,1 1240 | 519,827,1 1241 | 650,893,1 1242 | 474,927,1 1243 | 402,596,1 1244 | 396,462,1 1245 | 603,925,1 1246 | 115,599,1 1247 | 201,845,1 1248 | 112,388,1 1249 | 8,838,1 1250 | 444,907,1 1251 | 195,708,1 1252 | 487,495,1 1253 | 69,95,1 1254 | 571,626,1 1255 | 478,727,1 1256 | 200,747,1 1257 | 329,574,1 1258 | 283,674,1 1259 | 76,430,1 1260 | 240,950,1 1261 | 315,857,1 1262 | 303,581,1 1263 | 178,978,1 1264 | 82,586,1 1265 | 46,718,1 1266 | 144,384,1 1267 | 656,713,1 1268 | 504,777,1 1269 | 427,457,1 1270 | 459,581,1 1271 | 181,533,1 1272 | 254,278,1 1273 | 246,605,1 1274 | 447,454,1 1275 | 77,925,1 1276 | 473,896,1 1277 | 356,379,1 1278 | 435,946,1 1279 | 755,895,1 1280 | 887,957,1 1281 | 120,676,1 1282 | 19,99,1 1283 | 455,712,1 1284 | 479,545,1 1285 | 611,722,1 1286 | 57,271,1 1287 | 761,976,1 1288 | 772,879,1 1289 | 46,123,1 1290 | 151,174,1 1291 | 545,799,1 1292 | 1,650,1 1293 | 174,350,1 1294 | 493,846,1 1295 | 648,899,1 1296 | 579,977,1 1297 | 389,687,1 1298 | 634,906,1 1299 | 732,815,1 1300 | 779,820,1 1301 | 176,452,1 1302 | 248,958,1 1303 | 200,901,1 1304 | 491,952,1 1305 | 171,551,1 1306 | 25,866,1 1307 | 407,630,1 1308 | 249,524,1 1309 | 608,706,1 1310 | 622,886,1 1311 | 263,289,1 1312 | 1,543,1 1313 | 94,583,1 1314 | 104,654,1 1315 | 478,671,1 1316 | 108,150,1 1317 | 415,860,1 1318 | 147,233,1 1319 | 242,933,1 1320 | 70,798,1 1321 | 461,995,1 1322 | 376,854,1 1323 | 341,514,1 1324 | 203,736,1 1325 | 11,276,1 1326 | 498,544,1 1327 | 434,587,1 1328 | 4,259,1 1329 | 12,340,1 1330 | 668,959,1 1331 | 505,888,1 1332 | 230,551,1 1333 | 213,625,1 1334 | 808,965,1 1335 | 229,768,1 1336 | 136,794,1 1337 | 253,883,1 1338 | 68,783,1 1339 | 34,922,1 1340 | 410,752,1 1341 | 3,186,1 1342 | 32,53,1 1343 | 12,370,1 1344 | 387,674,1 1345 | 469,521,1 1346 | 254,605,1 1347 | 117,192,1 1348 | 487,898,1 1349 | 241,604,1 1350 | 176,856,1 1351 | 378,686,1 1352 | 625,734,1 1353 | 861,967,1 1354 | 96,802,1 1355 | 607,895,1 1356 | 583,728,1 1357 | 487,725,1 1358 | 700,871,1 1359 | 837,927,1 1360 | 27,413,1 1361 | 132,789,1 1362 | 126,773,1 1363 | 684,992,1 1364 | 44,941,1 1365 | 628,677,1 1366 | 106,901,1 1367 | 122,676,1 1368 | 869,986,1 1369 | 880,920,1 1370 | 255,644,1 1371 | 202,825,1 1372 | 437,696,1 1373 | 370,857,1 1374 | 433,612,1 1375 | 164,189,1 1376 | 294,595,1 1377 | 754,821,1 1378 | 42,228,1 1379 | 64,833,1 1380 | 133,870,1 1381 | 412,668,1 1382 | 616,953,1 1383 | 289,435,1 1384 | 32,85,1 1385 | 374,661,1 1386 | 790,950,1 1387 | 849,907,1 1388 | 144,925,1 1389 | 706,731,1 1390 | 73,893,1 1391 | 230,843,1 1392 | 767,895,1 1393 | 794,966,1 1394 | 289,798,1 1395 | 231,809,1 1396 | 71,893,1 1397 | 127,973,1 1398 | 165,472,1 1399 | 181,203,1 1400 | 416,912,1 1401 | 537,728,1 1402 | 885,901,1 1403 | 27,220,1 1404 | 103,898,1 1405 | 470,673,1 1406 | 759,819,1 1407 | 102,809,1 1408 | 507,823,1 1409 | 545,678,1 1410 | 139,831,1 1411 | 783,837,1 1412 | 741,835,1 1413 | 368,483,1 1414 | 436,446,1 1415 | 132,648,1 1416 | 261,876,1 1417 | 473,650,1 1418 | 603,750,1 1419 | 238,544,1 1420 | 362,645,1 1421 | 244,977,1 1422 | 465,969,1 1423 | 505,899,1 1424 | 132,852,1 1425 | 472,705,1 1426 | 74,97,1 1427 | 905,934,1 1428 | 613,701,1 1429 | 237,475,1 1430 | 82,866,1 1431 | 870,911,1 1432 | 230,628,1 1433 | 513,754,1 1434 | 79,280,1 1435 | 397,494,1 1436 | 483,882,1 1437 | 325,617,1 1438 | 540,935,1 1439 | 644,991,1 1440 | 770,928,1 1441 | 253,987,1 1442 | 20,495,1 1443 | 227,949,1 1444 | 260,516,1 1445 | 129,626,1 1446 | 142,517,1 1447 | 5,591,1 1448 | 880,983,1 1449 | 510,859,1 1450 | 462,608,1 1451 | 299,981,1 1452 | 142,553,1 1453 | 160,230,1 1454 | 399,929,1 1455 | 9,972,1 1456 | 473,684,1 1457 | 168,533,1 1458 | 464,953,1 1459 | 473,731,1 1460 | 35,471,1 1461 | 314,546,1 1462 | 209,696,1 1463 | 744,829,1 1464 | 274,681,1 1465 | 562,931,1 1466 | 286,529,1 1467 | 589,792,1 1468 | 496,967,1 1469 | 153,535,1 1470 | 373,684,1 1471 | 75,814,1 1472 | 619,987,1 1473 | 340,567,1 1474 | 356,962,1 1475 | 172,732,1 1476 | 359,885,1 1477 | 104,656,1 1478 | 423,921,1 1479 | 230,792,1 1480 | 143,236,1 1481 | 409,899,1 1482 | 450,453,1 1483 | 584,770,1 1484 | 354,488,1 1485 | 376,724,1 1486 | 53,936,1 1487 | 365,787,1 1488 | 733,960,1 1489 | 165,823,1 1490 | 301,744,1 1491 | 423,448,1 1492 | 46,120,1 1493 | 402,937,1 1494 | 31,51,1 1495 | 260,600,1 1496 | 107,325,1 1497 | 418,720,1 1498 | 687,881,1 1499 | 268,476,1 1500 | 40,323,1 1501 | 247,959,1 1502 | 2,280,1 1503 | 62,479,1 1504 | 127,657,1 1505 | 117,139,1 1506 | 286,590,1 1507 | 955,956,1 1508 | 706,726,1 1509 | 550,724,1 1510 | 250,325,1 1511 | 374,596,1 1512 | 847,903,1 1513 | 619,741,1 1514 | 532,701,1 1515 | 543,723,1 1516 | 39,469,1 1517 | 437,663,1 1518 | 546,717,1 1519 | 263,281,1 1520 | 383,716,1 1521 | 42,388,1 1522 | 153,885,1 1523 | 226,360,1 1524 | 824,870,1 1525 | 437,877,1 1526 | 33,532,1 1527 | 124,212,1 1528 | 138,439,1 1529 | 424,449,1 1530 | 60,334,1 1531 | 413,637,1 1532 | 442,927,1 1533 | 303,887,1 1534 | 358,800,1 1535 | 400,654,1 1536 | 30,95,1 1537 | 224,266,1 1538 | 41,467,1 1539 | 626,903,1 1540 | 887,894,1 1541 | 103,870,1 1542 | 171,740,1 1543 | 549,906,1 1544 | 258,483,1 1545 | 388,937,1 1546 | 1,585,1 1547 | 405,773,1 1548 | 309,971,1 1549 | 369,922,1 1550 | 717,858,1 1551 | 1,126,1 1552 | 271,742,1 1553 | 360,774,1 1554 | 463,678,1 1555 | 414,685,1 1556 | 674,994,1 1557 | 190,709,1 1558 | 304,308,1 1559 | 158,520,1 1560 | 386,568,1 1561 | 99,339,1 1562 | 32,596,1 1563 | 863,909,1 1564 | 878,945,1 1565 | 408,573,1 1566 | 429,609,1 1567 | 385,776,1 1568 | 459,845,1 1569 | 30,820,1 1570 | 352,916,1 1571 | 23,273,1 1572 | 469,852,1 1573 | 85,553,1 1574 | 521,944,1 1575 | 118,940,1 1576 | 61,387,1 1577 | 334,518,1 1578 | 427,614,1 1579 | 245,452,1 1580 | 805,821,1 1581 | 229,688,1 1582 | 118,175,1 1583 | 118,521,1 1584 | 384,939,1 1585 | 661,693,1 1586 | 91,850,1 1587 | 74,443,1 1588 | 353,371,1 1589 | 525,755,1 1590 | 812,936,1 1591 | 416,745,1 1592 | 378,775,1 1593 | 67,83,1 1594 | 447,793,1 1595 | 537,791,1 1596 | 399,863,1 1597 | 696,719,1 1598 | 722,909,1 1599 | 278,930,1 1600 | 389,958,1 1601 | 323,396,1 1602 | 44,925,1 1603 | 624,933,1 1604 | 512,606,1 1605 | 473,596,1 1606 | 562,669,1 1607 | 164,599,1 1608 | 433,999,1 1609 | 56,945,1 1610 | 103,968,1 1611 | 409,468,1 1612 | 165,664,1 1613 | 384,842,1 1614 | 512,895,1 1615 | 781,829,1 1616 | 212,837,1 1617 | 93,895,1 1618 | 85,239,1 1619 | 46,131,1 1620 | 18,264,1 1621 | 360,781,1 1622 | 280,409,1 1623 | 16,330,1 1624 | 203,527,1 1625 | 235,860,1 1626 | 724,846,1 1627 | 510,668,1 1628 | 198,239,1 1629 | 281,284,1 1630 | 580,747,1 1631 | 189,737,1 1632 | 515,562,1 1633 | 707,934,1 1634 | 342,818,1 1635 | 657,678,1 1636 | 825,942,1 1637 | 412,956,1 1638 | 413,483,1 1639 | 104,278,1 1640 | 544,919,1 1641 | 482,688,1 1642 | 471,511,1 1643 | 201,620,1 1644 | 522,904,1 1645 | 204,704,1 1646 | 305,840,1 1647 | 323,454,1 1648 | 211,254,1 1649 | 6,523,1 1650 | 198,394,1 1651 | 196,966,1 1652 | 555,708,1 1653 | 96,377,1 1654 | 182,843,1 1655 | 99,752,1 1656 | 139,805,1 1657 | 381,577,1 1658 | 84,981,1 1659 | 97,793,1 1660 | 830,929,1 1661 | 48,127,1 1662 | 10,308,1 1663 | 459,871,1 1664 | 504,687,1 1665 | 9,463,1 1666 | 3,888,1 1667 | 448,597,1 1668 | 82,107,1 1669 | 543,804,1 1670 | 484,701,1 1671 | 115,745,1 1672 | 220,851,1 1673 | 904,998,1 1674 | 323,734,1 1675 | 149,369,1 1676 | 472,999,1 1677 | 468,805,1 1678 | 733,854,1 1679 | 243,536,1 1680 | 444,615,1 1681 | 400,741,1 1682 | 13,752,1 1683 | 72,808,1 1684 | 74,478,1 1685 | 502,820,1 1686 | 46,619,1 1687 | 3,509,1 1688 | 118,551,1 1689 | 127,158,1 1690 | 34,199,1 1691 | 503,724,1 1692 | 460,867,1 1693 | 936,971,1 1694 | 674,986,1 1695 | 488,495,1 1696 | 301,563,1 1697 | 307,908,1 1698 | 342,803,1 1699 | 518,631,1 1700 | 217,422,1 1701 | 70,594,1 1702 | 151,441,1 1703 | 797,897,1 1704 | 277,895,1 1705 | 705,868,1 1706 | 287,690,1 1707 | 730,921,1 1708 | 309,440,1 1709 | 278,761,1 1710 | 230,953,1 1711 | 416,438,1 1712 | 670,682,1 1713 | 82,383,1 1714 | 747,845,1 1715 | 456,725,1 1716 | 217,676,1 1717 | 402,575,1 1718 | 435,872,1 1719 | 184,229,1 1720 | 216,613,1 1721 | 126,466,1 1722 | 262,782,1 1723 | 521,569,1 1724 | 222,284,1 1725 | 672,730,1 1726 | 344,710,1 1727 | 309,858,1 1728 | 71,98,1 1729 | 14,659,1 1730 | 429,754,1 1731 | 700,967,1 1732 | 251,329,1 1733 | 618,743,1 1734 | 218,258,1 1735 | 425,846,1 1736 | 136,748,1 1737 | 481,908,1 1738 | 101,432,1 1739 | 37,485,1 1740 | 4,648,1 1741 | 875,917,1 1742 | 250,624,1 1743 | 82,806,1 1744 | 230,500,1 1745 | 199,665,1 1746 | 188,423,1 1747 | 666,713,1 1748 | 28,406,1 1749 | 362,977,1 1750 | 745,792,1 1751 | 982,994,1 1752 | 459,789,1 1753 | 60,716,1 1754 | 301,557,1 1755 | 249,765,1 1756 | 58,567,1 1757 | 419,536,1 1758 | 129,611,1 1759 | 281,728,1 1760 | 239,250,1 1761 | 404,815,1 1762 | 465,859,1 1763 | 683,969,1 1764 | 93,472,1 1765 | 122,268,1 1766 | 873,883,1 1767 | 688,776,1 1768 | 80,829,1 1769 | 393,952,1 1770 | 33,719,1 1771 | 76,705,1 1772 | 575,952,1 1773 | 387,817,1 1774 | 137,244,1 1775 | 219,948,1 1776 | 61,935,1 1777 | 33,389,1 1778 | 173,250,1 1779 | 283,783,1 1780 | 62,219,1 1781 | 55,910,1 1782 | 8,407,1 1783 | 334,438,1 1784 | 580,674,1 1785 | 82,891,1 1786 | 286,827,1 1787 | 365,713,1 1788 | 144,818,1 1789 | 112,664,1 1790 | 152,521,1 1791 | 75,397,1 1792 | 151,171,1 1793 | 506,713,1 1794 | 240,398,1 1795 | 942,985,1 1796 | 64,564,1 1797 | 763,879,1 1798 | 538,635,1 1799 | 174,876,1 1800 | 53,484,1 1801 | 522,526,1 1802 | 0,417,1 1803 | 79,291,1 1804 | 125,635,1 1805 | 214,236,1 1806 | 232,328,1 1807 | 505,590,1 1808 | 553,926,1 1809 | 75,841,1 1810 | 470,726,1 1811 | 214,576,1 1812 | 257,982,1 1813 | 605,627,1 1814 | 373,655,1 1815 | 769,891,1 1816 | 581,677,1 1817 | 138,840,1 1818 | 264,536,1 1819 | 217,787,1 1820 | 62,927,1 1821 | 108,635,1 1822 | 408,634,1 1823 | 290,838,1 1824 | 202,210,1 1825 | 147,298,1 1826 | 427,527,1 1827 | 74,981,1 1828 | 39,511,1 1829 | 6,171,1 1830 | 792,929,1 1831 | 453,758,1 1832 | 742,854,1 1833 | 687,755,1 1834 | 810,872,1 1835 | 67,648,1 1836 | 27,347,1 1837 | 515,855,1 1838 | 213,327,1 1839 | 687,782,1 1840 | 17,452,1 1841 | 387,510,1 1842 | 349,447,1 1843 | 50,949,1 1844 | 552,833,1 1845 | 131,265,1 1846 | 483,873,1 1847 | 75,373,1 1848 | 220,340,1 1849 | 22,82,1 1850 | 83,326,1 1851 | 137,337,1 1852 | 268,636,1 1853 | 323,356,1 1854 | 250,615,1 1855 | 219,327,1 1856 | 472,616,1 1857 | 165,559,1 1858 | 203,578,1 1859 | 293,447,1 1860 | 155,951,1 1861 | 167,451,1 1862 | 232,939,1 1863 | 328,711,1 1864 | 578,876,1 1865 | 81,455,1 1866 | 27,187,1 1867 | 131,616,1 1868 | 399,969,1 1869 | 78,333,1 1870 | 103,442,1 1871 | 438,964,1 1872 | 462,487,1 1873 | 586,827,1 1874 | 216,292,1 1875 | 77,365,1 1876 | 361,644,1 1877 | 105,699,1 1878 | 516,766,1 1879 | 365,992,1 1880 | 509,825,1 1881 | 679,995,1 1882 | 72,231,1 1883 | 845,972,1 1884 | 139,449,1 1885 | 235,333,1 1886 | 477,478,1 1887 | 576,998,1 1888 | 455,652,1 1889 | 541,723,1 1890 | 4,928,1 1891 | 407,943,1 1892 | 577,935,1 1893 | 131,610,1 1894 | 106,705,1 1895 | 429,777,1 1896 | 242,691,1 1897 | 575,966,1 1898 | 361,419,1 1899 | 330,798,1 1900 | 97,875,1 1901 | 194,940,1 1902 | 103,764,1 1903 | 183,541,1 1904 | 678,903,1 1905 | 307,552,1 1906 | 58,922,1 1907 | 264,514,1 1908 | 720,932,1 1909 | 133,858,1 1910 | 638,899,1 1911 | 786,906,1 1912 | 264,653,1 1913 | 31,168,1 1914 | 372,798,1 1915 | 150,189,1 1916 | 249,689,1 1917 | 565,972,1 1918 | 91,898,1 1919 | 884,999,1 1920 | 286,794,1 1921 | 151,940,1 1922 | 184,854,1 1923 | 640,989,1 1924 | 682,819,1 1925 | 487,999,1 1926 | 508,755,1 1927 | 100,285,1 1928 | 316,948,1 1929 | 159,999,1 1930 | 331,885,1 1931 | 109,502,1 1932 | 157,549,1 1933 | 103,795,1 1934 | 627,729,1 1935 | 24,316,1 1936 | 424,562,1 1937 | 289,820,1 1938 | 352,412,1 1939 | 462,959,1 1940 | 645,823,1 1941 | 340,577,1 1942 | 147,777,1 1943 | 159,279,1 1944 | 829,834,1 1945 | 201,458,1 1946 | 287,985,1 1947 | 448,728,1 1948 | 528,924,1 1949 | 856,863,1 1950 | 343,554,1 1951 | 507,905,1 1952 | 77,836,1 1953 | 97,521,1 1954 | 51,522,1 1955 | 61,835,1 1956 | 39,314,1 1957 | 73,795,1 1958 | 200,505,1 1959 | 122,978,1 1960 | 647,673,1 1961 | 14,927,1 1962 | 44,145,1 1963 | 66,218,1 1964 | 147,681,1 1965 | 55,976,1 1966 | 129,899,1 1967 | 396,656,1 1968 | 377,616,1 1969 | 280,794,1 1970 | 81,122,1 1971 | 547,764,1 1972 | 394,458,1 1973 | 695,854,1 1974 | 368,377,1 1975 | 418,888,1 1976 | 633,768,1 1977 | 315,764,1 1978 | 608,739,1 1979 | 256,837,1 1980 | 954,983,1 1981 | 11,205,1 1982 | 401,674,1 1983 | 681,999,1 1984 | 535,993,1 1985 | 753,963,1 1986 | 946,975,1 1987 | 266,807,1 1988 | 215,344,1 1989 | 151,446,1 1990 | 106,862,1 1991 | 420,550,1 1992 | 708,907,1 1993 | 511,602,1 1994 | 308,678,1 1995 | 747,945,1 1996 | 121,215,1 1997 | 67,656,1 1998 | 68,295,1 1999 | 117,745,1 2000 | 715,730,1 2001 | 277,807,1 2002 | 635,638,1 2003 | 468,981,1 2004 | 237,345,1 2005 | 520,527,1 2006 | 206,992,1 2007 | 168,351,1 2008 | 54,835,1 2009 | 174,374,1 2010 | 116,432,1 2011 | 66,796,1 2012 | 283,828,1 2013 | 506,842,1 2014 | 574,580,1 2015 | 169,237,1 2016 | 201,449,1 2017 | 267,336,1 2018 | 736,824,1 2019 | 142,409,1 2020 | 357,507,1 2021 | 103,293,1 2022 | 167,881,1 2023 | 146,461,1 2024 | 18,956,1 2025 | 860,871,1 2026 | 262,929,1 2027 | 200,999,1 2028 | 582,877,1 2029 | 381,853,1 2030 | 601,751,1 2031 | 38,933,1 2032 | 601,975,1 2033 | 452,956,1 2034 | 663,682,1 2035 | 178,714,1 2036 | 114,329,1 2037 | 126,891,1 2038 | 247,314,1 2039 | 141,736,1 2040 | 480,690,1 2041 | 771,966,1 2042 | 47,600,1 2043 | 409,497,1 2044 | 321,403,1 2045 | 87,656,1 2046 | 391,447,1 2047 | 836,868,1 2048 | 733,813,1 2049 | 566,711,1 2050 | 642,857,1 2051 | 807,981,1 2052 | 488,971,1 2053 | 174,548,1 2054 | 110,133,1 2055 | 794,809,1 2056 | 327,929,1 2057 | 224,615,1 2058 | 596,730,1 2059 | 12,196,1 2060 | 120,693,1 2061 | 517,926,1 2062 | 55,638,1 2063 | 319,407,1 2064 | 84,489,1 2065 | 305,785,1 2066 | 84,776,1 2067 | 406,643,1 2068 | 113,711,1 2069 | 73,774,1 2070 | 166,749,1 2071 | 454,473,1 2072 | 101,103,1 2073 | 382,871,1 2074 | 75,351,1 2075 | 240,375,1 2076 | 383,709,1 2077 | 135,311,1 2078 | 353,875,1 2079 | 132,490,1 2080 | 509,635,1 2081 | 122,410,1 2082 | 3,688,1 2083 | 386,749,1 2084 | 126,851,1 2085 | 632,866,1 2086 | 91,166,1 2087 | 392,947,1 2088 | 186,345,1 2089 | 395,890,1 2090 | 180,886,1 2091 | 105,364,1 2092 | 435,867,1 2093 | 158,451,1 2094 | 444,494,1 2095 | 440,609,1 2096 | 472,723,1 2097 | 349,507,1 2098 | 230,532,1 2099 | 472,543,1 2100 | 183,412,1 2101 | 185,918,1 2102 | 710,906,1 2103 | 9,608,1 2104 | 785,803,1 2105 | 381,839,1 2106 | 559,797,1 2107 | 206,488,1 2108 | 81,509,1 2109 | 169,521,1 2110 | 261,264,1 2111 | 246,288,1 2112 | 177,881,1 2113 | 415,789,1 2114 | 290,439,1 2115 | 278,327,1 2116 | 147,696,1 2117 | 174,824,1 2118 | 491,511,1 2119 | 95,545,1 2120 | 444,945,1 2121 | 306,529,1 2122 | 496,959,1 2123 | 38,160,1 2124 | 26,937,1 2125 | 426,774,1 2126 | 662,990,1 2127 | 24,854,1 2128 | 111,926,1 2129 | 220,893,1 2130 | 621,624,1 2131 | 200,741,1 2132 | 84,785,1 2133 | 78,245,1 2134 | 75,918,1 2135 | 128,408,1 2136 | 139,946,1 2137 | 309,457,1 2138 | 443,647,1 2139 | 371,763,1 2140 | 91,290,1 2141 | 89,551,1 2142 | 622,901,1 2143 | 757,980,1 2144 | 782,992,1 2145 | 628,995,1 2146 | 265,691,1 2147 | 142,605,1 2148 | 42,53,1 2149 | 352,933,1 2150 | 541,657,1 2151 | 537,594,1 2152 | 650,716,1 2153 | 178,557,1 2154 | 41,664,1 2155 | 238,985,1 2156 | 143,163,1 2157 | 633,936,1 2158 | 141,910,1 2159 | 62,986,1 2160 | 348,352,1 2161 | 412,484,1 2162 | 188,730,1 2163 | 306,605,1 2164 | 156,574,1 2165 | 1,540,1 2166 | 32,564,1 2167 | 769,967,1 2168 | 142,696,1 2169 | 286,388,1 2170 | 159,480,1 2171 | 727,945,1 2172 | 7,105,1 2173 | 160,557,1 2174 | 379,713,1 2175 | 212,501,1 2176 | 591,745,1 2177 | 13,106,1 2178 | 268,521,1 2179 | 660,821,1 2180 | 1,941,1 2181 | 374,785,1 2182 | 88,570,1 2183 | 90,543,1 2184 | 252,502,1 2185 | 257,774,1 2186 | 343,614,1 2187 | 561,628,1 2188 | 491,856,1 2189 | 294,357,1 2190 | 249,597,1 2191 | 402,971,1 2192 | 696,924,1 2193 | 372,421,1 2194 | 7,830,1 2195 | 587,973,1 2196 | 679,933,1 2197 | 621,940,1 2198 | 791,977,1 2199 | 779,897,1 2200 | 639,850,1 2201 | 56,974,1 2202 | 136,262,1 2203 | 59,461,1 2204 | 60,326,1 2205 | 57,150,1 2206 | 512,815,1 2207 | 496,909,1 2208 | 808,949,1 2209 | 142,182,1 2210 | 571,946,1 2211 | 256,792,1 2212 | 265,481,1 2213 | 793,811,1 2214 | 20,659,1 2215 | 340,832,1 2216 | 404,979,1 2217 | 529,923,1 2218 | 442,557,1 2219 | 81,220,1 2220 | 494,715,1 2221 | 272,397,1 2222 | 104,738,1 2223 | 592,913,1 2224 | 258,509,1 2225 | 227,375,1 2226 | 357,830,1 2227 | 203,795,1 2228 | 41,148,1 2229 | 0,667,1 2230 | 157,366,1 2231 | 62,490,1 2232 | 194,536,1 2233 | 180,745,1 2234 | 122,683,1 2235 | 269,765,1 2236 | 459,850,1 2237 | 374,451,1 2238 | 80,81,1 2239 | 638,678,1 2240 | 135,808,1 2241 | 858,955,1 2242 | 588,637,1 2243 | 80,894,1 2244 | 308,910,1 2245 | 759,961,1 2246 | 143,337,1 2247 | 60,463,1 2248 | 222,389,1 2249 | 75,992,1 2250 | 53,726,1 2251 | 43,113,1 2252 | 50,759,1 2253 | 244,951,1 2254 | 129,893,1 2255 | 488,908,1 2256 | 349,704,1 2257 | 860,930,1 2258 | 499,838,1 2259 | 319,714,1 2260 | 162,285,1 2261 | 14,201,1 2262 | 49,542,1 2263 | 723,727,1 2264 | 831,887,1 2265 | 541,806,1 2266 | 452,533,1 2267 | 541,566,1 2268 | 410,664,1 2269 | 483,711,1 2270 | 23,705,1 2271 | 589,939,1 2272 | 143,745,1 2273 | 730,837,1 2274 | 211,790,1 2275 | 174,833,1 2276 | 617,781,1 2277 | 794,804,1 2278 | 117,626,1 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5 | -------------------------------------------------------------------------------- /sgcn.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/benedekrozemberczki/SGCN/c01e930c02968ea5b0eb72f1fe93eaab99e46eea/sgcn.jpg -------------------------------------------------------------------------------- /sgcn.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/benedekrozemberczki/SGCN/c01e930c02968ea5b0eb72f1fe93eaab99e46eea/sgcn.pdf -------------------------------------------------------------------------------- /sgcn_run_example.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/benedekrozemberczki/SGCN/c01e930c02968ea5b0eb72f1fe93eaab99e46eea/sgcn_run_example.png -------------------------------------------------------------------------------- /src/main.py: -------------------------------------------------------------------------------- 1 | """SGCN runner.""" 2 | 3 | from sgcn import SignedGCNTrainer 4 | from param_parser import parameter_parser 5 | from utils import tab_printer, read_graph, score_printer, save_logs 6 | 7 | def main(): 8 | """ 9 | Parsing command line parameters. 10 | Creating target matrix. 11 | Fitting an SGCN. 12 | Predicting edge signs and saving the embedding. 13 | """ 14 | args = parameter_parser() 15 | tab_printer(args) 16 | edges = read_graph(args) 17 | trainer = SignedGCNTrainer(args, edges) 18 | trainer.setup_dataset() 19 | trainer.create_and_train_model() 20 | if args.test_size > 0: 21 | trainer.save_model() 22 | score_printer(trainer.logs) 23 | save_logs(args, trainer.logs) 24 | 25 | if __name__ == "__main__": 26 | main() 27 | -------------------------------------------------------------------------------- /src/param_parser.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | 3 | def parameter_parser(): 4 | """ 5 | A method to parse up command line parameters. 6 | By default it gives an embedding of the Bitcoin OTC dataset. 7 | The default hyperparameters give a good quality representation without grid search. 8 | Representations are sorted by node ID. 9 | """ 10 | parser = argparse.ArgumentParser(description="Run SGCN.") 11 | 12 | parser.add_argument("--edge-path", 13 | nargs="?", 14 | default="./input/bitcoin_otc.csv", 15 | help="Edge list csv.") 16 | 17 | parser.add_argument("--features-path", 18 | nargs="?", 19 | default="./input/bitcoin_otc.csv", 20 | help="Edge list csv.") 21 | 22 | parser.add_argument("--embedding-path", 23 | nargs="?", 24 | default="./output/embedding/bitcoin_otc_sgcn.csv", 25 | help="Target embedding csv.") 26 | 27 | parser.add_argument("--regression-weights-path", 28 | nargs="?", 29 | default="./output/weights/bitcoin_otc_sgcn.csv", 30 | help="Regression weights csv.") 31 | 32 | parser.add_argument("--log-path", 33 | nargs="?", 34 | default="./logs/bitcoin_otc_logs.json", 35 | help="Log json.") 36 | 37 | parser.add_argument("--epochs", 38 | type=int, 39 | default=100, 40 | help="Number of training epochs. Default is 100.") 41 | 42 | parser.add_argument("--reduction-iterations", 43 | type=int, 44 | default=30, 45 | help="Number of SVD iterations. Default is 30.") 46 | 47 | parser.add_argument("--reduction-dimensions", 48 | type=int, 49 | default=64, 50 | help="Number of SVD feature extraction dimensions. Default is 64.") 51 | 52 | parser.add_argument("--seed", 53 | type=int, 54 | default=42, 55 | help="Random seed for sklearn pre-training. Default is 42.") 56 | 57 | parser.add_argument("--lamb", 58 | type=float, 59 | default=1.0, 60 | help="Embedding regularization parameter. Default is 1.0.") 61 | 62 | parser.add_argument("--test-size", 63 | type=float, 64 | default=0.2, 65 | help="Test dataset size. Default is 0.2.") 66 | 67 | parser.add_argument("--learning-rate", 68 | type=float, 69 | default=0.01, 70 | help="Learning rate. Default is 0.01.") 71 | 72 | parser.add_argument("--weight-decay", 73 | type=float, 74 | default=10**-5, 75 | help="Learning rate. Default is 10^-5.") 76 | 77 | parser.add_argument("--layers", 78 | nargs="+", 79 | type=int, 80 | help="Layer dimensions separated by space. E.g. 32 32.") 81 | 82 | parser.add_argument("--spectral-features", 83 | dest="spectral_features", 84 | action="store_true") 85 | 86 | parser.add_argument("--general-features", 87 | dest="spectral_features", 88 | action="store_false") 89 | 90 | parser.set_defaults(spectral_features=True) 91 | 92 | parser.set_defaults(layers=[32, 32]) 93 | 94 | return parser.parse_args() 95 | -------------------------------------------------------------------------------- /src/sgcn.py: -------------------------------------------------------------------------------- 1 | """SGCN runner.""" 2 | 3 | import time 4 | import torch 5 | import random 6 | import numpy as np 7 | import pandas as pd 8 | from tqdm import trange 9 | import torch.nn.init as init 10 | from torch.nn import Parameter 11 | import torch.nn.functional as F 12 | from utils import calculate_auc, setup_features 13 | from sklearn.model_selection import train_test_split 14 | from signedsageconvolution import SignedSAGEConvolutionBase, SignedSAGEConvolutionDeep 15 | from signedsageconvolution import ListModule 16 | from utils import structured_negative_sampling 17 | 18 | class SignedGraphConvolutionalNetwork(torch.nn.Module): 19 | """ 20 | Signed Graph Convolutional Network Class. 21 | For details see: Signed Graph Convolutional Network. 22 | Tyler Derr, Yao Ma, and Jiliang Tang ICDM, 2018. 23 | https://arxiv.org/abs/1808.06354 24 | """ 25 | def __init__(self, device, args, X): 26 | super(SignedGraphConvolutionalNetwork, self).__init__() 27 | """ 28 | SGCN Initialization. 29 | :param device: Device for calculations. 30 | :param args: Arguments object. 31 | :param X: Node features. 32 | """ 33 | self.args = args 34 | torch.manual_seed(self.args.seed) 35 | self.device = device 36 | self.X = X 37 | self.setup_layers() 38 | 39 | def setup_layers(self): 40 | """ 41 | Adding Base Layers, Deep Signed GraphSAGE layers. 42 | Assing Regression Parameters if the model is not a single layer model. 43 | """ 44 | self.nodes = range(self.X.shape[0]) 45 | self.neurons = self.args.layers 46 | self.layers = len(self.neurons) 47 | self.positive_base_aggregator = SignedSAGEConvolutionBase(self.X.shape[1]*2, 48 | self.neurons[0]).to(self.device) 49 | 50 | self.negative_base_aggregator = SignedSAGEConvolutionBase(self.X.shape[1]*2, 51 | self.neurons[0]).to(self.device) 52 | self.positive_aggregators = [] 53 | self.negative_aggregators = [] 54 | for i in range(1, self.layers): 55 | self.positive_aggregators.append(SignedSAGEConvolutionDeep(3*self.neurons[i-1], 56 | self.neurons[i]).to(self.device)) 57 | 58 | self.negative_aggregators.append(SignedSAGEConvolutionDeep(3*self.neurons[i-1], 59 | self.neurons[i]).to(self.device)) 60 | 61 | self.positive_aggregators = ListModule(*self.positive_aggregators) 62 | self.negative_aggregators = ListModule(*self.negative_aggregators) 63 | self.regression_weights = Parameter(torch.Tensor(4*self.neurons[-1], 3)) 64 | self.regression_bias = Parameter(torch.FloatTensor(3)) 65 | init.xavier_normal_(self.regression_weights) 66 | self.regression_bias.data.fill_(0.0) 67 | 68 | def calculate_regression_loss(self, z, target): 69 | """ 70 | Calculating the regression loss for all pairs of nodes. 71 | :param z: Hidden vertex representations. 72 | :param target: Target vector. 73 | :return loss_term: Regression loss. 74 | :return predictions_soft: Predictions for each vertex pair. 75 | """ 76 | pos = torch.cat((self.positive_z_i, self.positive_z_j), 1) 77 | neg = torch.cat((self.negative_z_i, self.negative_z_j), 1) 78 | 79 | surr_neg_i = torch.cat((self.negative_z_i, self.negative_z_k), 1) 80 | surr_neg_j = torch.cat((self.negative_z_j, self.negative_z_k), 1) 81 | surr_pos_i = torch.cat((self.positive_z_i, self.positive_z_k), 1) 82 | surr_pos_j = torch.cat((self.positive_z_j, self.positive_z_k), 1) 83 | 84 | features = torch.cat((pos, neg, surr_neg_i, surr_neg_j, surr_pos_i, surr_pos_j)) 85 | predictions = torch.mm(features, self.regression_weights) + self.regression_bias 86 | predictions_soft = F.log_softmax(predictions, dim=1) 87 | loss_term = F.nll_loss(predictions_soft, target) 88 | return loss_term, predictions_soft 89 | 90 | def calculate_positive_embedding_loss(self, z, positive_edges): 91 | """ 92 | Calculating the loss on the positive edge embedding distances 93 | :param z: Hidden vertex representation. 94 | :param positive_edges: Positive training edges. 95 | :return : Loss value on positive edge embedding. 96 | """ 97 | i, j, k = structured_negative_sampling(positive_edges,z.shape[0]) 98 | self.positive_z_i = z[i] 99 | self.positive_z_j = z[j] 100 | self.positive_z_k = z[k] 101 | 102 | out = (z[i] - z[j]).pow(2).sum(dim=1) - (z[i] - z[k]).pow(2).sum(dim=1) 103 | return torch.clamp(out, min=0).mean() 104 | 105 | def calculate_negative_embedding_loss(self, z, negative_edges): 106 | """ 107 | Calculating the loss on the negative edge embedding distances 108 | :param z: Hidden vertex representation. 109 | :param negative_edges: Negative training edges. 110 | :return : Loss value on negative edge embedding. 111 | """ 112 | i, j, k = structured_negative_sampling(negative_edges,z.shape[0]) 113 | self.negative_z_i = z[i] 114 | self.negative_z_j = z[j] 115 | self.negative_z_k = z[k] 116 | 117 | out = (z[i] - z[k]).pow(2).sum(dim=1) - (z[i] - z[j]).pow(2).sum(dim=1) 118 | return torch.clamp(out, min=0).mean() 119 | 120 | def calculate_loss_function(self, z, positive_edges, negative_edges, target): 121 | """ 122 | Calculating the embedding losses, regression loss and weight regularization loss. 123 | :param z: Node embedding. 124 | :param positive_edges: Positive edge pairs. 125 | :param negative_edges: Negative edge pairs. 126 | :param target: Target vector. 127 | :return loss: Value of loss. 128 | """ 129 | loss_term_1 = self.calculate_positive_embedding_loss(z, positive_edges) 130 | loss_term_2 = self.calculate_negative_embedding_loss(z, negative_edges) 131 | regression_loss, self.predictions = self.calculate_regression_loss(z, target) 132 | loss_term = regression_loss+self.args.lamb*(loss_term_1+loss_term_2) 133 | return loss_term 134 | 135 | def forward(self, positive_edges, negative_edges, target): 136 | """ 137 | Model forward propagation pass. Can fit deep and single layer SGCN models. 138 | :param positive_edges: Positive edges. 139 | :param negative_edges: Negative edges. 140 | :param target: Target vectors. 141 | :return loss: Loss value. 142 | :return self.z: Hidden vertex representations. 143 | """ 144 | self.h_pos, self.h_neg = [], [] 145 | self.h_pos.append(torch.tanh(self.positive_base_aggregator(self.X, positive_edges))) 146 | self.h_neg.append(torch.tanh(self.negative_base_aggregator(self.X, negative_edges))) 147 | for i in range(1, self.layers): 148 | self.h_pos.append(torch.tanh(self.positive_aggregators[i-1](self.h_pos[i-1], self.h_neg[i-1], positive_edges, negative_edges))) 149 | self.h_neg.append(torch.tanh(self.negative_aggregators[i-1](self.h_neg[i-1], self.h_pos[i-1], positive_edges, negative_edges))) 150 | self.z = torch.cat((self.h_pos[-1], self.h_neg[-1]), 1) 151 | loss = self.calculate_loss_function(self.z, positive_edges, negative_edges, target) 152 | return loss, self.z 153 | 154 | class SignedGCNTrainer(object): 155 | """ 156 | Object to train and score the SGCN, log the model behaviour and save the output. 157 | """ 158 | def __init__(self, args, edges): 159 | """ 160 | Constructing the trainer instance and setting up logs. 161 | :param args: Arguments object. 162 | :param edges: Edge data structure with positive and negative edges separated. 163 | """ 164 | self.args = args 165 | self.edges = edges 166 | self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") 167 | self.setup_logs() 168 | 169 | def setup_logs(self): 170 | """ 171 | Creating a log dictionary. 172 | """ 173 | self.logs = {} 174 | self.logs["parameters"] = vars(self.args) 175 | self.logs["performance"] = [["Epoch", "AUC", "F1", "pos_ratio"]] 176 | self.logs["training_time"] = [["Epoch", "Seconds"]] 177 | 178 | def setup_dataset(self): 179 | """ 180 | Creating train and test split. 181 | """ 182 | self.positive_edges, self.test_positive_edges = train_test_split(self.edges["positive_edges"], 183 | test_size=self.args.test_size) 184 | 185 | self.negative_edges, self.test_negative_edges = train_test_split(self.edges["negative_edges"], 186 | test_size=self.args.test_size) 187 | self.ecount = len(self.positive_edges + self.negative_edges) 188 | 189 | self.X = setup_features(self.args, 190 | self.positive_edges, 191 | self.negative_edges, 192 | self.edges["ncount"]) 193 | 194 | self.positive_edges = torch.from_numpy(np.array(self.positive_edges, 195 | dtype=np.int64).T).type(torch.long).to(self.device) 196 | 197 | self.negative_edges = torch.from_numpy(np.array(self.negative_edges, 198 | dtype=np.int64).T).type(torch.long).to(self.device) 199 | 200 | self.y = np.array([0 if i < int(self.ecount/2) else 1 for i in range(self.ecount)]+[2]*(self.ecount*2)) 201 | self.y = torch.from_numpy(self.y).type(torch.LongTensor).to(self.device) 202 | self.X = torch.from_numpy(self.X).float().to(self.device) 203 | 204 | def score_model(self, epoch): 205 | """ 206 | Score the model on the test set edges in each epoch. 207 | :param epoch: Epoch number. 208 | """ 209 | loss, self.train_z = self.model(self.positive_edges, self.negative_edges, self.y) 210 | score_positive_edges = torch.from_numpy(np.array(self.test_positive_edges, dtype=np.int64).T).type(torch.long).to(self.device) 211 | score_negative_edges = torch.from_numpy(np.array(self.test_negative_edges, dtype=np.int64).T).type(torch.long).to(self.device) 212 | test_positive_z = torch.cat((self.train_z[score_positive_edges[0, :], :], self.train_z[score_positive_edges[1, :], :]), 1) 213 | test_negative_z = torch.cat((self.train_z[score_negative_edges[0, :], :], self.train_z[score_negative_edges[1, :], :]), 1) 214 | scores = torch.mm(torch.cat((test_positive_z, test_negative_z), 0), self.model.regression_weights.to(self.device)) + self.model.regression_bias.to(self.device) 215 | probability_scores = torch.exp(F.softmax(scores, dim=1)) 216 | predictions = probability_scores[:, 0]/probability_scores[:, 0:2].sum(1) 217 | predictions = predictions.cpu().detach().numpy() 218 | targets = [0]*len(self.test_positive_edges) + [1]*len(self.test_negative_edges) 219 | auc, f1, pos_ratio = calculate_auc(targets, predictions, self.edges) 220 | self.logs["performance"].append([epoch+1, auc, f1, pos_ratio]) 221 | 222 | def create_and_train_model(self): 223 | """ 224 | Model training and scoring. 225 | """ 226 | print("\nTraining started.\n") 227 | self.model = SignedGraphConvolutionalNetwork(self.device, self.args, self.X).to(self.device) 228 | self.optimizer = torch.optim.Adam(self.model.parameters(), 229 | lr=self.args.learning_rate, 230 | weight_decay=self.args.weight_decay) 231 | self.model.train() 232 | self.epochs = trange(self.args.epochs, desc="Loss") 233 | for epoch in self.epochs: 234 | start_time = time.time() 235 | self.optimizer.zero_grad() 236 | loss, _ = self.model(self.positive_edges, self.negative_edges, self.y) 237 | loss.backward() 238 | self.epochs.set_description("SGCN (Loss=%g)" % round(loss.item(), 4)) 239 | self.optimizer.step() 240 | self.logs["training_time"].append([epoch+1, time.time()-start_time]) 241 | if self.args.test_size > 0: 242 | self.score_model(epoch) 243 | 244 | def save_model(self): 245 | """ 246 | Saving the embedding and model weights. 247 | """ 248 | print("\nEmbedding is saved.\n") 249 | self.train_z = self.train_z.cpu().detach().numpy() 250 | embedding_header = ["id"] + ["x_"+str(x) for x in range(self.train_z.shape[1])] 251 | self.train_z = np.concatenate([np.array(range(self.train_z.shape[0])).reshape(-1, 1), self.train_z], axis=1) 252 | self.train_z = pd.DataFrame(self.train_z, columns=embedding_header) 253 | self.train_z.to_csv(self.args.embedding_path, index=None) 254 | print("\nRegression parameters are saved.\n") 255 | self.regression_weights = self.model.regression_weights.cpu().detach().numpy().T 256 | self.regression_bias = self.model.regression_bias.cpu().detach().numpy().reshape((3, 1)) 257 | regression_header = ["x_" + str(x) for x in range(self.regression_weights.shape[1])] + ["bias"] 258 | self.regression_params = pd.DataFrame(np.concatenate((self.regression_weights, self.regression_bias), axis=1), columns=regression_header) 259 | self.regression_params.to_csv(self.args.regression_weights_path, index=None) 260 | -------------------------------------------------------------------------------- /src/signedsageconvolution.py: -------------------------------------------------------------------------------- 1 | """Layer classes.""" 2 | 3 | import math 4 | import torch 5 | import torch.nn.functional as F 6 | from torch.nn import Parameter 7 | from torch_scatter import scatter_add, scatter_mean 8 | from torch_geometric.utils import remove_self_loops, add_self_loops 9 | 10 | def uniform(size, tensor): 11 | """ 12 | Uniform weight initialization. 13 | :param size: Size of the tensor. 14 | :param tensor: Tensor initialized. 15 | """ 16 | stdv = 1.0 / math.sqrt(size) 17 | if tensor is not None: 18 | tensor.data.uniform_(-stdv, stdv) 19 | 20 | class ListModule(torch.nn.Module): 21 | """ 22 | Abstract list layer class. 23 | """ 24 | def __init__(self, *args): 25 | """ 26 | Model initializing. 27 | """ 28 | super(ListModule, self).__init__() 29 | idx = 0 30 | for module in args: 31 | self.add_module(str(idx), module) 32 | idx += 1 33 | 34 | def __getitem__(self, idx): 35 | """ 36 | Getting the indexed layer. 37 | """ 38 | if idx < 0 or idx >= len(self._modules): 39 | raise IndexError('index {} is out of range'.format(idx)) 40 | it = iter(self._modules.values()) 41 | for i in range(idx): 42 | next(it) 43 | return next(it) 44 | 45 | def __iter__(self): 46 | """ 47 | Iterating on the layers. 48 | """ 49 | return iter(self._modules.values()) 50 | 51 | def __len__(self): 52 | """ 53 | Number of layers. 54 | """ 55 | return len(self._modules) 56 | 57 | class SignedSAGEConvolution(torch.nn.Module): 58 | """ 59 | Abstract Signed SAGE convolution class. 60 | :param in_channels: Number of features. 61 | :param out_channels: Number of filters. 62 | :param norm_embed: Normalize embedding -- boolean. 63 | :param bias: Add bias or no. 64 | """ 65 | def __init__(self, 66 | in_channels, 67 | out_channels, 68 | norm=True, 69 | norm_embed=True, 70 | bias=True): 71 | super(SignedSAGEConvolution, self).__init__() 72 | 73 | self.in_channels = in_channels 74 | self.out_channels = out_channels 75 | self.norm = norm 76 | self.norm_embed = norm_embed 77 | self.weight = Parameter(torch.Tensor(self.in_channels, out_channels)) 78 | 79 | if bias: 80 | self.bias = Parameter(torch.Tensor(out_channels)) 81 | else: 82 | self.register_parameter("bias", None) 83 | 84 | self.reset_parameters() 85 | 86 | def reset_parameters(self): 87 | """ 88 | Initialize parameters. 89 | """ 90 | size = self.weight.size(0) 91 | uniform(size, self.weight) 92 | uniform(size, self.bias) 93 | 94 | def __repr__(self): 95 | """ 96 | Create formal string representation. 97 | """ 98 | return "{}({}, {})".format(self.__class__.__name__, self.in_channels, self.out_channels) 99 | 100 | class SignedSAGEConvolutionBase(SignedSAGEConvolution): 101 | """ 102 | Base Signed SAGE class for the first layer of the model. 103 | """ 104 | def forward(self, x, edge_index): 105 | """ 106 | Forward propagation pass with features an indices. 107 | :param x: Feature matrix. 108 | :param edge_index: Indices. 109 | """ 110 | edge_index, _ = remove_self_loops(edge_index, None) 111 | row, col = edge_index 112 | 113 | if self.norm: 114 | out = scatter_mean(x[col], row, dim=0, dim_size=x.size(0)) 115 | else: 116 | out = scatter_add(x[col], row, dim=0, dim_size=x.size(0)) 117 | 118 | out = torch.cat((out, x), 1) 119 | out = torch.matmul(out, self.weight) 120 | 121 | if self.bias is not None: 122 | out = out + self.bias 123 | if self.norm_embed: 124 | out = F.normalize(out, p=2, dim=-1) 125 | return out 126 | 127 | class SignedSAGEConvolutionDeep(SignedSAGEConvolution): 128 | """ 129 | Deep Signed SAGE class for multi-layer models. 130 | """ 131 | def forward(self, x_1, x_2, edge_index_pos, edge_index_neg): 132 | """ 133 | Forward propagation pass with features an indices. 134 | :param x_1: Features for left hand side vertices. 135 | :param x_2: Features for right hand side vertices. 136 | :param edge_index_pos: Positive indices. 137 | :param edge_index_neg: Negative indices. 138 | :return out: Abstract convolved features. 139 | """ 140 | edge_index_pos, _ = remove_self_loops(edge_index_pos, None) 141 | edge_index_pos, _ = add_self_loops(edge_index_pos, num_nodes=x_1.size(0)) 142 | edge_index_neg, _ = remove_self_loops(edge_index_neg, None) 143 | edge_index_neg, _ = add_self_loops(edge_index_neg, num_nodes=x_2.size(0)) 144 | 145 | row_pos, col_pos = edge_index_pos 146 | row_neg, col_neg = edge_index_neg 147 | 148 | if self.norm: 149 | out_1 = scatter_mean(x_1[col_pos], row_pos, dim=0, dim_size=x_1.size(0)) 150 | out_2 = scatter_mean(x_2[col_neg], row_neg, dim=0, dim_size=x_2.size(0)) 151 | else: 152 | out_1 = scatter_add(x_1[col_pos], row_pos, dim=0, dim_size=x_1.size(0)) 153 | out_2 = scatter_add(x_2[col_neg], row_neg, dim=0, dim_size=x_2.size(0)) 154 | 155 | out = torch.cat((out_1, out_2, x_1), 1) 156 | out = torch.matmul(out, self.weight) 157 | if self.bias is not None: 158 | out = out + self.bias 159 | 160 | if self.norm_embed: 161 | out = F.normalize(out, p=2, dim=-1) 162 | return out 163 | -------------------------------------------------------------------------------- /src/utils.py: -------------------------------------------------------------------------------- 1 | """Data reading utils.""" 2 | 3 | import json 4 | import numpy as np 5 | import pandas as pd 6 | from scipy import sparse 7 | from texttable import Texttable 8 | from sklearn.decomposition import TruncatedSVD 9 | from sklearn.metrics import roc_auc_score, f1_score 10 | from torch import Tensor 11 | import torch 12 | 13 | def read_graph(args): 14 | """ 15 | Method to read graph and create a target matrix with pooled adjacency matrix powers. 16 | :param args: Arguments object. 17 | :return edges: Edges dictionary. 18 | """ 19 | dataset = pd.read_csv(args.edge_path).values.tolist() 20 | edges = {} 21 | edges["positive_edges"] = [edge[0:2] for edge in dataset if edge[2] == 1] 22 | edges["negative_edges"] = [edge[0:2] for edge in dataset if edge[2] == -1] 23 | edges["ecount"] = len(dataset) 24 | edges["ncount"] = len(set([edge[0] for edge in dataset]+[edge[1] for edge in dataset])) 25 | return edges 26 | 27 | def tab_printer(args): 28 | """ 29 | Function to print the logs in a nice tabular format. 30 | :param args: Parameters used for the model. 31 | """ 32 | args = vars(args) 33 | keys = sorted(args.keys()) 34 | t = Texttable() 35 | t.add_rows([["Parameter", "Value"]]) 36 | t.add_rows([[k.replace("_", " ").capitalize(), args[k]] for k in keys]) 37 | print(t.draw()) 38 | 39 | def calculate_auc(targets, predictions, edges): 40 | """ 41 | Calculate performance measures on test dataset. 42 | :param targets: Target vector to predict. 43 | :param predictions: Predictions vector. 44 | :param edges: Edges dictionary with number of edges etc. 45 | :return auc: AUC value. 46 | :return f1: F1-score. 47 | """ 48 | targets = [0 if target == 1 else 1 for target in targets] 49 | auc = roc_auc_score(targets, predictions) 50 | pred = [1 if p > 0.5 else 0 for p in predictions] 51 | f1 = f1_score(targets, pred) 52 | pos_ratio = sum(pred)/len(pred) 53 | return auc, f1, pos_ratio 54 | 55 | def score_printer(logs): 56 | """ 57 | Print the performance for every 10th epoch on the test dataset. 58 | :param logs: Log dictionary. 59 | """ 60 | t = Texttable() 61 | t.add_rows([per for i, per in enumerate(logs["performance"]) if i % 10 == 0]) 62 | print(t.draw()) 63 | 64 | def save_logs(args, logs): 65 | """ 66 | Save the logs at the path. 67 | :param args: Arguments objects. 68 | :param logs: Log dictionary. 69 | """ 70 | with open(args.log_path, "w") as f: 71 | json.dump(logs, f) 72 | 73 | def setup_features(args, positive_edges, negative_edges, node_count): 74 | """ 75 | Setting up the node features as a numpy array. 76 | :param args: Arguments object. 77 | :param positive_edges: Positive edges list. 78 | :param negative_edges: Negative edges list. 79 | :param node_count: Number of nodes. 80 | :return X: Node features. 81 | """ 82 | if args.spectral_features: 83 | X = create_spectral_features(args, positive_edges, negative_edges, node_count) 84 | else: 85 | X = create_general_features(args) 86 | return X 87 | 88 | def create_general_features(args): 89 | """ 90 | Reading features using the path. 91 | :param args: Arguments object. 92 | :return X: Node features. 93 | """ 94 | X = np.array(pd.read_csv(args.features_path)) 95 | return X 96 | 97 | def create_spectral_features(args, positive_edges, negative_edges, node_count): 98 | """ 99 | Creating spectral node features using the train dataset edges. 100 | :param args: Arguments object. 101 | :param positive_edges: Positive edges list. 102 | :param negative_edges: Negative edges list. 103 | :param node_count: Number of nodes. 104 | :return X: Node features. 105 | """ 106 | p_edges = positive_edges + [[edge[1], edge[0]] for edge in positive_edges] 107 | n_edges = negative_edges + [[edge[1], edge[0]] for edge in negative_edges] 108 | train_edges = p_edges + n_edges 109 | index_1 = [edge[0] for edge in train_edges] 110 | index_2 = [edge[1] for edge in train_edges] 111 | values = [1]*len(p_edges) + [-1]*len(n_edges) 112 | shaping = (node_count, node_count) 113 | signed_A = sparse.csr_matrix(sparse.coo_matrix((values, (index_1, index_2)), 114 | shape=shaping, 115 | dtype=np.float32)) 116 | 117 | svd = TruncatedSVD(n_components=args.reduction_dimensions, 118 | n_iter=args.reduction_iterations, 119 | random_state=args.seed) 120 | svd.fit(signed_A) 121 | X = svd.components_.T 122 | return X 123 | 124 | # below borrowed from torch_geometric 125 | @torch.jit._overload 126 | def maybe_num_nodes(edge_index, num_nodes=None): 127 | # type: (Tensor, Optional[int]) -> int 128 | pass 129 | 130 | 131 | @torch.jit._overload 132 | def maybe_num_nodes(edge_index, num_nodes=None): 133 | # type: (SparseTensor, Optional[int]) -> int 134 | pass 135 | 136 | 137 | def maybe_num_nodes(edge_index, num_nodes=None): 138 | if num_nodes is not None: 139 | return num_nodes 140 | elif isinstance(edge_index, Tensor): 141 | return int(edge_index.max()) + 1 142 | else: 143 | return max(edge_index.size(0), edge_index.size(1)) 144 | 145 | def structured_negative_sampling(edge_index, num_nodes=None): 146 | r"""Samples a negative edge :obj:`(i,k)` for every positive edge 147 | :obj:`(i,j)` in the graph given by :attr:`edge_index`, and returns it as a 148 | tuple of the form :obj:`(i,j,k)`. 149 | Args: 150 | edge_index (LongTensor): The edge indices. 151 | num_nodes (int, optional): The number of nodes, *i.e.* 152 | :obj:`max_val + 1` of :attr:`edge_index`. (default: :obj:`None`) 153 | :rtype: (LongTensor, LongTensor, LongTensor) 154 | """ 155 | num_nodes = maybe_num_nodes(edge_index, num_nodes) 156 | 157 | i, j = edge_index.to('cpu') 158 | idx_1 = i * num_nodes + j 159 | 160 | k = torch.randint(num_nodes, (i.size(0), ), dtype=torch.long) 161 | idx_2 = i * num_nodes + k 162 | 163 | mask = torch.from_numpy(np.isin(idx_2, idx_1)).to(torch.bool) 164 | rest = mask.nonzero(as_tuple=False).view(-1) 165 | while rest.numel() > 0: # pragma: no cover 166 | tmp = torch.randint(num_nodes, (rest.numel(), ), dtype=torch.long) 167 | idx_2 = i[rest] * num_nodes + tmp 168 | mask = torch.from_numpy(np.isin(idx_2, idx_1)).to(torch.bool) 169 | k[rest] = tmp 170 | rest = rest[mask.nonzero(as_tuple=False).view(-1)] 171 | 172 | return edge_index[0], edge_index[1], k.to(edge_index.device) --------------------------------------------------------------------------------