├── .gitignore ├── LICENCE ├── README.md ├── data ├── ind.citeseer.allx ├── ind.citeseer.ally ├── ind.citeseer.graph ├── ind.citeseer.test.index ├── ind.citeseer.tx ├── ind.citeseer.ty ├── ind.citeseer.x ├── ind.citeseer.y ├── ind.cora.allx ├── ind.cora.ally ├── ind.cora.graph ├── ind.cora.test.index ├── ind.cora.tx ├── ind.cora.ty ├── ind.cora.x ├── ind.cora.y ├── ind.pubmed.allx ├── ind.pubmed.ally ├── ind.pubmed.graph ├── ind.pubmed.test.index ├── ind.pubmed.tx ├── ind.pubmed.ty ├── ind.pubmed.x └── ind.pubmed.y ├── layers.py ├── models.py ├── sampler.py ├── train.py └── utils.py /.gitignore: -------------------------------------------------------------------------------- 1 | __pycache__ 2 | .vscode 3 | *.out 4 | parse_train.py -------------------------------------------------------------------------------- /LICENCE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2020 Gkunnan97 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in 13 | all copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN 21 | THE SOFTWARE. -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Pytorch Implementation of FastGCN and AS-GCN 2 | PyTorch implementation of [FastGCN](https://arxiv.org/abs/1801.10247) and [AS-GCN](http://papers.nips.cc/paper/7707-adaptive-sampling-towards-fast-graph-representation-learning). The supported datasets are: cora, citeseer and pubmed. 3 | Mind that this implementation may differ from the original in some parts. Especially for the AS-GCN, the different methods of calculating variance did not bring better performance. So if you want to use it into the research, please cheak these details carefully. 4 | ## Requirements 5 | * PyTorch 1.14 6 | * Python 3.7 7 | 8 | ## Usage 9 | python train.py --dataset dataset_name --model model_name 10 | 11 | ## Reference 12 | FASTGCN: FAST LEARNING WITH GRAPH CONVOLUTIONAL NETWORKS VIA IMPORTANCE SAMPLING 13 | Adaptive Sampling Towards Fast Graph Representation Learning -------------------------------------------------------------------------------- /data/ind.citeseer.allx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Gkunnan97/FastGCN_pytorch/ef1eaaab4d18cefc9b76cfe1e9e4b0a3db9d6f86/data/ind.citeseer.allx -------------------------------------------------------------------------------- /data/ind.citeseer.ally: -------------------------------------------------------------------------------- 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| 1957 292 | 1709 293 | 1955 294 | 2570 295 | 2387 296 | 1811 297 | 2025 298 | 2447 299 | 2696 300 | 2052 301 | 2366 302 | 1857 303 | 2273 304 | 2245 305 | 2672 306 | 2133 307 | 2421 308 | 1929 309 | 2125 310 | 2319 311 | 2641 312 | 2167 313 | 2418 314 | 1765 315 | 1761 316 | 1828 317 | 2188 318 | 1972 319 | 1997 320 | 2419 321 | 2289 322 | 2296 323 | 2587 324 | 2051 325 | 2440 326 | 2053 327 | 2191 328 | 1923 329 | 2164 330 | 1861 331 | 2339 332 | 2333 333 | 2523 334 | 2670 335 | 2121 336 | 1921 337 | 1724 338 | 2253 339 | 2374 340 | 1940 341 | 2545 342 | 2301 343 | 2244 344 | 2156 345 | 1849 346 | 2551 347 | 2011 348 | 2279 349 | 2572 350 | 1757 351 | 2400 352 | 2569 353 | 2072 354 | 2526 355 | 2173 356 | 2069 357 | 2036 358 | 1819 359 | 1734 360 | 1880 361 | 2137 362 | 2408 363 | 2226 364 | 2604 365 | 1771 366 | 2698 367 | 2187 368 | 2060 369 | 1756 370 | 2201 371 | 2066 372 | 2439 373 | 1844 374 | 1772 375 | 2383 376 | 2398 377 | 1708 378 | 1992 379 | 1959 380 | 1794 381 | 2426 382 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| 2707 656 | 2465 657 | 1785 658 | 2149 659 | 2045 660 | 2505 661 | 2611 662 | 2217 663 | 2180 664 | 1904 665 | 2453 666 | 2484 667 | 1871 668 | 2309 669 | 2349 670 | 2482 671 | 2004 672 | 1965 673 | 2406 674 | 2162 675 | 1805 676 | 2654 677 | 2007 678 | 1947 679 | 1981 680 | 2112 681 | 2141 682 | 1720 683 | 1758 684 | 2080 685 | 2330 686 | 2030 687 | 2432 688 | 2089 689 | 2547 690 | 1820 691 | 1815 692 | 2675 693 | 1840 694 | 2658 695 | 2370 696 | 2251 697 | 1908 698 | 2029 699 | 2068 700 | 2513 701 | 2549 702 | 2267 703 | 2580 704 | 2327 705 | 2351 706 | 2111 707 | 2022 708 | 2321 709 | 2614 710 | 2252 711 | 2104 712 | 1822 713 | 2552 714 | 2243 715 | 1798 716 | 2396 717 | 2663 718 | 2564 719 | 2148 720 | 2562 721 | 2684 722 | 2001 723 | 2151 724 | 2706 725 | 2240 726 | 2474 727 | 2303 728 | 2634 729 | 2680 730 | 2055 731 | 2090 732 | 2503 733 | 2347 734 | 2402 735 | 2238 736 | 1950 737 | 2054 738 | 2016 739 | 1872 740 | 2233 741 | 1710 742 | 2032 743 | 2540 744 | 2628 745 | 1795 746 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| 19632 65 | 19548 66 | 19546 67 | 18825 68 | 19498 69 | 19266 70 | 19117 71 | 19595 72 | 19252 73 | 18730 74 | 18913 75 | 18809 76 | 19452 77 | 19520 78 | 19274 79 | 19555 80 | 19388 81 | 18919 82 | 19099 83 | 19637 84 | 19403 85 | 18720 86 | 19526 87 | 18905 88 | 19451 89 | 19408 90 | 18923 91 | 18794 92 | 19322 93 | 19431 94 | 18912 95 | 18841 96 | 19239 97 | 19125 98 | 19258 99 | 19565 100 | 18898 101 | 19482 102 | 19029 103 | 18778 104 | 19096 105 | 19684 106 | 19552 107 | 18765 108 | 19361 109 | 19171 110 | 19367 111 | 19623 112 | 19402 113 | 19327 114 | 19118 115 | 18888 116 | 18726 117 | 19510 118 | 18831 119 | 19490 120 | 19576 121 | 19050 122 | 18729 123 | 18896 124 | 19246 125 | 19012 126 | 18862 127 | 18873 128 | 19193 129 | 19693 130 | 19474 131 | 18953 132 | 19115 133 | 19182 134 | 19269 135 | 19116 136 | 18837 137 | 18872 138 | 19007 139 | 19212 140 | 18798 141 | 19102 142 | 18772 143 | 19660 144 | 19511 145 | 18914 146 | 18886 147 | 19672 148 | 19360 149 | 19213 150 | 18810 151 | 19420 152 | 19512 153 | 18719 154 | 19432 155 | 19350 156 | 19127 157 | 18782 158 | 19587 159 | 18924 160 | 19488 161 | 18781 162 | 19340 163 | 19190 164 | 19383 165 | 19094 166 | 18835 167 | 19487 168 | 19230 169 | 18791 170 | 18882 171 | 18937 172 | 18928 173 | 18755 174 | 18802 175 | 19516 176 | 18795 177 | 18786 178 | 19273 179 | 19349 180 | 19398 181 | 19626 182 | 19130 183 | 19351 184 | 19489 185 | 19446 186 | 18959 187 | 19025 188 | 18792 189 | 18878 190 | 19304 191 | 19629 192 | 19061 193 | 18785 194 | 19194 195 | 19179 196 | 19210 197 | 19417 198 | 19583 199 | 19415 200 | 19443 201 | 18739 202 | 19662 203 | 18904 204 | 18910 205 | 18901 206 | 18960 207 | 18722 208 | 18827 209 | 19290 210 | 18842 211 | 19389 212 | 19344 213 | 18961 214 | 19098 215 | 19147 216 | 19334 217 | 19358 218 | 18829 219 | 18984 220 | 18931 221 | 18742 222 | 19320 223 | 19111 224 | 19196 225 | 18887 226 | 18991 227 | 19469 228 | 18990 229 | 18876 230 | 19261 231 | 19270 232 | 19522 233 | 19088 234 | 19284 235 | 19646 236 | 19493 237 | 19225 238 | 19615 239 | 19449 240 | 19043 241 | 19674 242 | 19391 243 | 18918 244 | 19155 245 | 19110 246 | 18815 247 | 19131 248 | 18834 249 | 19715 250 | 19603 251 | 19688 252 | 19133 253 | 19053 254 | 19166 255 | 19066 256 | 18893 257 | 18757 258 | 19582 259 | 19282 260 | 19257 261 | 18869 262 | 19467 263 | 18954 264 | 19371 265 | 19151 266 | 19462 267 | 19598 268 | 19653 269 | 19187 270 | 19624 271 | 19564 272 | 19534 273 | 19581 274 | 19478 275 | 18985 276 | 18746 277 | 19342 278 | 18777 279 | 19696 280 | 18824 281 | 19138 282 | 18728 283 | 19643 284 | 19199 285 | 18731 286 | 19168 287 | 18948 288 | 19216 289 | 19697 290 | 19347 291 | 18808 292 | 18725 293 | 19134 294 | 18847 295 | 18828 296 | 18996 297 | 19106 298 | 19485 299 | 18917 300 | 18911 301 | 18776 302 | 19203 303 | 19158 304 | 18895 305 | 19165 306 | 19382 307 | 18780 308 | 18836 309 | 19373 310 | 19659 311 | 18947 312 | 19375 313 | 19299 314 | 18761 315 | 19366 316 | 18754 317 | 19248 318 | 19416 319 | 19658 320 | 19638 321 | 19034 322 | 19281 323 | 18844 324 | 18922 325 | 19491 326 | 19272 327 | 19341 328 | 19068 329 | 19332 330 | 19559 331 | 19293 332 | 18804 333 | 18933 334 | 18935 335 | 19405 336 | 18936 337 | 18945 338 | 18943 339 | 18818 340 | 18797 341 | 19570 342 | 19464 343 | 19428 344 | 19093 345 | 19433 346 | 18986 347 | 19161 348 | 19255 349 | 19157 350 | 19046 351 | 19292 352 | 19434 353 | 19298 354 | 18724 355 | 19410 356 | 19694 357 | 19214 358 | 19640 359 | 19189 360 | 18963 361 | 19218 362 | 19585 363 | 19041 364 | 19550 365 | 19123 366 | 19620 367 | 19376 368 | 19561 369 | 18944 370 | 19706 371 | 19056 372 | 19283 373 | 18741 374 | 19319 375 | 19144 376 | 19542 377 | 18821 378 | 19404 379 | 19080 380 | 19303 381 | 18793 382 | 19306 383 | 19678 384 | 19435 385 | 19519 386 | 19566 387 | 19278 388 | 18946 389 | 19536 390 | 19020 391 | 19057 392 | 19198 393 | 19333 394 | 19649 395 | 19699 396 | 19399 397 | 19654 398 | 19136 399 | 19465 400 | 19321 401 | 19577 402 | 18907 403 | 19665 404 | 19386 405 | 19596 406 | 19247 407 | 19473 408 | 19568 409 | 19355 410 | 18925 411 | 19586 412 | 18982 413 | 19616 414 | 19495 415 | 19612 416 | 19023 417 | 19438 418 | 18817 419 | 19692 420 | 19295 421 | 19414 422 | 19676 423 | 19472 424 | 19107 425 | 19062 426 | 19035 427 | 18883 428 | 19409 429 | 19052 430 | 19606 431 | 19091 432 | 19651 433 | 19475 434 | 19413 435 | 18796 436 | 19369 437 | 19639 438 | 19701 439 | 19461 440 | 19645 441 | 19251 442 | 19063 443 | 19679 444 | 19545 445 | 19081 446 | 19363 447 | 18995 448 | 19549 449 | 18790 450 | 18855 451 | 18833 452 | 18899 453 | 19395 454 | 18717 455 | 19647 456 | 18768 457 | 19103 458 | 19245 459 | 18819 460 | 18779 461 | 19656 462 | 19076 463 | 18745 464 | 18971 465 | 19197 466 | 19711 467 | 19074 468 | 19128 469 | 19466 470 | 19139 471 | 19309 472 | 19324 473 | 18814 474 | 19092 475 | 19627 476 | 19060 477 | 18806 478 | 18929 479 | 18737 480 | 18942 481 | 18906 482 | 18858 483 | 19456 484 | 19253 485 | 19716 486 | 19104 487 | 19667 488 | 19574 489 | 18903 490 | 19237 491 | 18864 492 | 19556 493 | 19364 494 | 18952 495 | 19008 496 | 19323 497 | 19700 498 | 19170 499 | 19267 500 | 19345 501 | 19238 502 | 18909 503 | 18892 504 | 19109 505 | 19704 506 | 18902 507 | 19275 508 | 19680 509 | 18723 510 | 19242 511 | 19112 512 | 19169 513 | 18956 514 | 19343 515 | 19650 516 | 19541 517 | 19698 518 | 19521 519 | 19087 520 | 18976 521 | 19038 522 | 18775 523 | 18968 524 | 19671 525 | 19412 526 | 19407 527 | 19573 528 | 19027 529 | 18813 530 | 19357 531 | 19460 532 | 19673 533 | 19481 534 | 19036 535 | 19614 536 | 18787 537 | 19195 538 | 18732 539 | 18884 540 | 19613 541 | 19657 542 | 19575 543 | 19226 544 | 19589 545 | 19234 546 | 19617 547 | 19707 548 | 19484 549 | 18740 550 | 19424 551 | 18784 552 | 19419 553 | 19159 554 | 18865 555 | 19105 556 | 19315 557 | 19480 558 | 19664 559 | 19378 560 | 18803 561 | 19605 562 | 18870 563 | 19042 564 | 19426 565 | 18848 566 | 19223 567 | 19509 568 | 19532 569 | 18752 570 | 19691 571 | 18718 572 | 19209 573 | 19362 574 | 19090 575 | 19492 576 | 19567 577 | 19687 578 | 19018 579 | 18830 580 | 19530 581 | 19554 582 | 19119 583 | 19442 584 | 19558 585 | 19527 586 | 19427 587 | 19291 588 | 19543 589 | 19422 590 | 19142 591 | 18897 592 | 18950 593 | 19425 594 | 19002 595 | 19588 596 | 18978 597 | 19551 598 | 18930 599 | 18736 600 | 19101 601 | 19215 602 | 19150 603 | 19263 604 | 18949 605 | 18974 606 | 18759 607 | 19335 608 | 19200 609 | 19129 610 | 19328 611 | 19437 612 | 18988 613 | 19429 614 | 19368 615 | 19406 616 | 19049 617 | 18811 618 | 19296 619 | 19256 620 | 19385 621 | 19602 622 | 18770 623 | 19337 624 | 19580 625 | 19476 626 | 19045 627 | 19132 628 | 19089 629 | 19120 630 | 19265 631 | 19483 632 | 18767 633 | 19227 634 | 18934 635 | 19069 636 | 18820 637 | 19006 638 | 19459 639 | 18927 640 | 19037 641 | 19280 642 | 19441 643 | 18823 644 | 19015 645 | 19114 646 | 19618 647 | 18957 648 | 19176 649 | 18853 650 | 19648 651 | 19201 652 | 19444 653 | 19279 654 | 18751 655 | 19302 656 | 19505 657 | 18733 658 | 19601 659 | 19533 660 | 18863 661 | 19708 662 | 19387 663 | 19346 664 | 19152 665 | 19206 666 | 18851 667 | 19338 668 | 19681 669 | 19380 670 | 19055 671 | 18766 672 | 19085 673 | 19591 674 | 19547 675 | 18958 676 | 19146 677 | 18840 678 | 19051 679 | 19021 680 | 19207 681 | 19235 682 | 19086 683 | 18979 684 | 19300 685 | 18939 686 | 19100 687 | 19619 688 | 19287 689 | 18980 690 | 19277 691 | 19326 692 | 19108 693 | 18920 694 | 19625 695 | 19374 696 | 19078 697 | 18734 698 | 19634 699 | 19339 700 | 18877 701 | 19423 702 | 19652 703 | 19683 704 | 19044 705 | 18983 706 | 19330 707 | 19529 708 | 19714 709 | 19468 710 | 19075 711 | 19540 712 | 18839 713 | 19022 714 | 19286 715 | 19537 716 | 19175 717 | 19463 718 | 19167 719 | 19705 720 | 19562 721 | 19244 722 | 19486 723 | 19611 724 | 18801 725 | 19178 726 | 19590 727 | 18846 728 | 19450 729 | 19205 730 | 19381 731 | 18941 732 | 19670 733 | 19185 734 | 19504 735 | 19633 736 | 18997 737 | 19113 738 | 19397 739 | 19636 740 | 19709 741 | 19289 742 | 19264 743 | 19353 744 | 19584 745 | 19126 746 | 18938 747 | 19669 748 | 18964 749 | 19276 750 | 18774 751 | 19173 752 | 19231 753 | 18973 754 | 18769 755 | 19064 756 | 19040 757 | 19668 758 | 18738 759 | 19082 760 | 19655 761 | 19236 762 | 19352 763 | 19609 764 | 19628 765 | 18951 766 | 19384 767 | 19122 768 | 18875 769 | 18992 770 | 18753 771 | 19379 772 | 19254 773 | 19301 774 | 19506 775 | 19135 776 | 19010 777 | 19682 778 | 19400 779 | 19579 780 | 19316 781 | 19553 782 | 19208 783 | 19635 784 | 19644 785 | 18891 786 | 19024 787 | 18989 788 | 19250 789 | 18850 790 | 19317 791 | 18915 792 | 19607 793 | 18799 794 | 18881 795 | 19479 796 | 19031 797 | 19365 798 | 19164 799 | 18744 800 | 18760 801 | 19502 802 | 19058 803 | 19517 804 | 18735 805 | 19448 806 | 19243 807 | 19453 808 | 19285 809 | 18857 810 | 19439 811 | 19016 812 | 18975 813 | 19503 814 | 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-------------------------------------------------------------------------------- https://raw.githubusercontent.com/Gkunnan97/FastGCN_pytorch/ef1eaaab4d18cefc9b76cfe1e9e4b0a3db9d6f86/data/ind.pubmed.x -------------------------------------------------------------------------------- /data/ind.pubmed.y: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Gkunnan97/FastGCN_pytorch/ef1eaaab4d18cefc9b76cfe1e9e4b0a3db9d6f86/data/ind.pubmed.y -------------------------------------------------------------------------------- /layers.py: -------------------------------------------------------------------------------- 1 | import math 2 | import pdb 3 | 4 | import torch 5 | 6 | from torch.nn.parameter import Parameter 7 | from torch.nn.modules.module import Module 8 | 9 | 10 | class GraphConvolution(Module): 11 | """ 12 | Simple GCN layer, similar to https://arxiv.org/abs/1609.02907 13 | """ 14 | 15 | def __init__(self, in_features, out_features, bias=False): 16 | super(GraphConvolution, self).__init__() 17 | self.in_features = in_features 18 | self.out_features = out_features 19 | self.weight = Parameter(torch.FloatTensor(in_features, out_features)) 20 | if bias: 21 | self.bias = Parameter(torch.FloatTensor(out_features)) 22 | else: 23 | self.register_parameter('bias', None) 24 | self.reset_parameters() 25 | 26 | def reset_parameters(self): 27 | # stdv = math.sqrt(6.0 / (self.weight.shape[0] + self.weight.shape[1])) 28 | stdv = 1.0 / math.sqrt(self.weight.size(1)) 29 | self.weight.data.uniform_(-stdv, stdv) 30 | if self.bias is not None: 31 | self.bias.data.uniform_(-stdv, stdv) 32 | 33 | def forward(self, input, adj): 34 | # pdb.set_trace() 35 | support = torch.mm(input, self.weight) 36 | output = torch.spmm(adj, support) 37 | if self.bias is not None: 38 | return output + self.bias 39 | else: 40 | return output 41 | 42 | def __repr__(self): 43 | return self.__class__.__name__ + ' (' \ 44 | + str(self.in_features) + ' -> ' \ 45 | + str(self.out_features) + ')' 46 | -------------------------------------------------------------------------------- /models.py: -------------------------------------------------------------------------------- 1 | import torch.nn as nn 2 | import torch.nn.functional as F 3 | from layers import GraphConvolution 4 | 5 | 6 | class GCN(nn.Module): 7 | def __init__(self, nfeat, nhid, nclass, dropout, sampler): 8 | super().__init__() 9 | 10 | self.gc1 = GraphConvolution(nfeat, nhid) 11 | self.gc2 = GraphConvolution(nhid, nclass) 12 | self.dropout = dropout 13 | self.sampler = sampler 14 | self.out_softmax = nn.Softmax(dim=1) 15 | 16 | def forward(self, x, adj): 17 | outputs1 = F.relu(self.gc1(x, adj[0])) 18 | outputs1 = F.dropout(outputs1, self.dropout, training=self.training) 19 | outputs2 = self.gc2(outputs1, adj[1]) 20 | return F.log_softmax(outputs2, dim=1) 21 | # return self.out_softmax(outputs2) 22 | 23 | def sampling(self, *args, **kwargs): 24 | return self.sampler.sampling(*args, **kwargs) 25 | -------------------------------------------------------------------------------- /sampler.py: -------------------------------------------------------------------------------- 1 | import pdb 2 | import math 3 | import torch 4 | import numpy as np 5 | import scipy.sparse as sp 6 | 7 | from scipy.sparse.linalg import norm as sparse_norm 8 | from torch.nn.parameter import Parameter 9 | 10 | from utils import sparse_mx_to_torch_sparse_tensor, load_data 11 | 12 | 13 | class Sampler: 14 | def __init__(self, features, adj, **kwargs): 15 | allowed_kwargs = {'input_dim', 'layer_sizes', 'device'} 16 | for kwarg in kwargs.keys(): 17 | assert kwarg in allowed_kwargs, \ 18 | 'Invalid keyword argument: ' + kwarg 19 | 20 | self.input_dim = kwargs.get('input_dim', 1) 21 | self.layer_sizes = kwargs.get('layer_sizes', [1]) 22 | self.scope = kwargs.get('scope', 'test_graph') 23 | self.device = kwargs.get('device', torch.device("cpu")) 24 | 25 | self.num_layers = len(self.layer_sizes) 26 | 27 | self.adj = adj 28 | self.features = features 29 | 30 | self.train_nodes_number = self.adj.shape[0] 31 | 32 | def sampling(self, v_indices): 33 | raise NotImplementedError("sampling is not implimented") 34 | 35 | def _change_sparse_to_tensor(self, adjs): 36 | new_adjs = [] 37 | for adj in adjs: 38 | new_adjs.append( 39 | sparse_mx_to_torch_sparse_tensor(adj).to(self.device)) 40 | return new_adjs 41 | 42 | 43 | class Sampler_FastGCN(Sampler): 44 | def __init__(self, pre_probs, features, adj, **kwargs): 45 | super().__init__(features, adj, **kwargs) 46 | # NOTE: uniform sampling can also has the same performance!!!! 47 | # try, with the change: col_norm = np.ones(features.shape[0]) 48 | col_norm = sparse_norm(adj, axis=0) 49 | self.probs = col_norm / np.sum(col_norm) 50 | 51 | def sampling(self, v): 52 | """ 53 | Inputs: 54 | v: batch nodes list 55 | """ 56 | all_support = [[]] * self.num_layers 57 | 58 | cur_out_nodes = v 59 | for layer_index in range(self.num_layers-1, -1, -1): 60 | cur_sampled, cur_support = self._one_layer_sampling( 61 | cur_out_nodes, self.layer_sizes[layer_index]) 62 | all_support[layer_index] = cur_support 63 | cur_out_nodes = cur_sampled 64 | 65 | all_support = self._change_sparse_to_tensor(all_support) 66 | sampled_X0 = self.features[cur_out_nodes] 67 | return sampled_X0, all_support, 0 68 | 69 | def _one_layer_sampling(self, v_indices, output_size): 70 | # NOTE: FastGCN described in paper samples neighboors without reference 71 | # to the v_indices. But in its tensorflow implementation, it has used 72 | # the v_indice to filter out the disconnected nodes. So the same thing 73 | # has been done here. 74 | support = self.adj[v_indices, :] 75 | neis = np.nonzero(np.sum(support, axis=0))[1] 76 | p1 = self.probs[neis] 77 | p1 = p1 / np.sum(p1) 78 | sampled = np.random.choice(np.array(np.arange(np.size(neis))), 79 | output_size, True, p1) 80 | 81 | u_sampled = neis[sampled] 82 | support = support[:, u_sampled] 83 | sampled_p1 = p1[sampled] 84 | 85 | support = support.dot(sp.diags(1.0 / (sampled_p1 * output_size))) 86 | return u_sampled, support 87 | 88 | 89 | class Sampler_ASGCN(Sampler, torch.nn.Module): 90 | def __init__(self, pre_probs, features, adj, **kwargs): 91 | super().__init__(features, adj, **kwargs) 92 | torch.nn.Module.__init__(self) 93 | self.feats_dim = features.shape[1] 94 | 95 | # attention weights w1 is also wg 96 | self.w1 = Parameter(torch.FloatTensor(self.feats_dim, 1)) 97 | self.w2 = Parameter(torch.FloatTensor(self.feats_dim, 1)) 98 | self._reset_parameters() 99 | 100 | def _reset_parameters(self): 101 | stdv = 1.0 / math.sqrt(self.w1.size(0)) 102 | self.w1.data.uniform_(-stdv, stdv) 103 | self.w2.data.uniform_(-stdv, stdv) 104 | 105 | def sampling(self, v): 106 | """ 107 | Inputs: 108 | v: batch nodes list 109 | """ 110 | v = torch.LongTensor(v) 111 | all_support = [[]] * self.num_layers 112 | all_p_u = [[]] * self.num_layers 113 | 114 | # sample top-1 layer 115 | # all_x_u[self.num_layers - 1] = self.features[v] 116 | cur_out_nodes = v 117 | for i in range(self.num_layers-1, -1, -1): 118 | cur_u_sampled, cur_support, cur_var_need = \ 119 | self._one_layer_sampling(cur_out_nodes, 120 | output_size=self.layer_sizes[i]) 121 | 122 | all_support[i] = cur_support 123 | all_p_u[i] = cur_var_need 124 | 125 | cur_out_nodes = cur_u_sampled 126 | 127 | loss = self._calc_variance(all_p_u) 128 | sampled_X0 = self.features[cur_out_nodes] 129 | return sampled_X0, all_support, loss 130 | 131 | def _calc_variance(self, var_need): 132 | # NOTE: it's useless in this implementation for the three datasets 133 | # only calc the variane of the last layer 134 | u_nodes, p_u = var_need[-1][0], var_need[-1][1] 135 | p_u = p_u.reshape(-1, 1) 136 | feature = self.features[u_nodes] 137 | means = torch.sum(feature, 0) 138 | feature = feature - means 139 | var = torch.mean(torch.sum(torch.mul(feature, feature) * p_u, 0)) 140 | return var 141 | 142 | def _one_layer_sampling(self, v_indices, output_size): 143 | support = self.adj[v_indices, :] 144 | neis = np.nonzero(np.sum(support, axis=0))[1] 145 | support = support[:, neis] 146 | # NOTE: change the sparse support to dense, mind the matrix size 147 | support = support.todense() 148 | support = torch.FloatTensor(support).to(self.device) 149 | h_v = self.features[v_indices] 150 | h_u = self.features[neis] 151 | 152 | attention = torch.mm(h_v, self.w1) + \ 153 | torch.mm(h_u, self.w2).reshape(1, -1) + 1 154 | attention = (1.0 / np.size(neis)) * torch.relu(attention) 155 | 156 | p1 = torch.sum(support * attention, 0) 157 | # sampling only done in CPU 158 | numpy_p1 = p1.to('cpu').data.numpy() 159 | numpy_p1 = numpy_p1 / np.sum(numpy_p1) 160 | sampled = np.random.choice(np.array(np.arange(np.size(neis))), 161 | size=output_size, 162 | replace=True, 163 | p=numpy_p1) 164 | 165 | u_sampled = neis[sampled] 166 | support = support[:, sampled] 167 | sampled_p1 = p1[sampled] 168 | 169 | t_diag = torch.diag(1.0 / (sampled_p1 * output_size)) 170 | support = torch.mm(support, t_diag) 171 | 172 | return u_sampled, support, (neis, p1 / torch.sum(p1)) 173 | 174 | 175 | if __name__ == '__main__': 176 | adj, features, adj_train, train_features, y_train, y_test, test_index = \ 177 | load_data("cora") 178 | batchsize = 256 179 | layer_sizes = [128, 128, batchsize] 180 | input_dim = features.shape[1] 181 | 182 | sampler = Sampler_ASGCN(None, train_features, adj_train, 183 | input_dim=input_dim, 184 | layer_sizes=layer_sizes, scope="None") 185 | 186 | batch_inds = list(range(batchsize)) 187 | sampled_feats, sampled_adjs, var_loss = sampler.sampling(batch_inds) 188 | pdb.set_trace() 189 | -------------------------------------------------------------------------------- /train.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import time 3 | 4 | import torch 5 | import torch.nn.functional as F 6 | import torch.optim as optim 7 | import numpy as np 8 | 9 | from models import GCN 10 | from sampler import Sampler_FastGCN, Sampler_ASGCN 11 | from utils import load_data, get_batches, accuracy 12 | from utils import sparse_mx_to_torch_sparse_tensor 13 | 14 | 15 | def get_args(): 16 | # Training settings 17 | parser = argparse.ArgumentParser() 18 | parser.add_argument('--dataset', type=str, default='cora', 19 | help='dataset name.') 20 | # model can be "Fast" or "AS" 21 | parser.add_argument('--model', type=str, default='Fast', 22 | help='model name.') 23 | parser.add_argument('--test_gap', type=int, default=10, 24 | help='the train epochs between two test') 25 | parser.add_argument('--no-cuda', action='store_true', default=True, 26 | help='Disables CUDA training.') 27 | parser.add_argument('--fastmode', action='store_true', default=False, 28 | help='Validate during training pass.') 29 | parser.add_argument('--seed', type=int, default=123, help='Random seed.') 30 | parser.add_argument('--epochs', type=int, default=100, 31 | help='Number of epochs to train.') 32 | parser.add_argument('--lr', type=float, default=0.01, 33 | help='Initial learning rate.') 34 | parser.add_argument('--weight_decay', type=float, default=5e-4, 35 | help='Weight decay (L2 loss on parameters).') 36 | parser.add_argument('--hidden', type=int, default=16, 37 | help='Number of hidden units.') 38 | parser.add_argument('--dropout', type=float, default=0.0, 39 | help='Dropout rate (1 - keep probability).') 40 | parser.add_argument('--batchsize', type=int, default=256, 41 | help='batchsize for train') 42 | args = parser.parse_args() 43 | args.cuda = not args.no_cuda and torch.cuda.is_available() 44 | return args 45 | 46 | 47 | def train(train_ind, train_labels, batch_size, train_times): 48 | t = time.time() 49 | model.train() 50 | for epoch in range(train_times): 51 | for batch_inds, batch_labels in get_batches(train_ind, 52 | train_labels, 53 | batch_size): 54 | sampled_feats, sampled_adjs, var_loss = model.sampling( 55 | batch_inds) 56 | optimizer.zero_grad() 57 | output = model(sampled_feats, sampled_adjs) 58 | loss_train = loss_fn(output, batch_labels) + 0.5 * var_loss 59 | acc_train = accuracy(output, batch_labels) 60 | loss_train.backward() 61 | optimizer.step() 62 | # just return the train loss of the last train epoch 63 | return loss_train.item(), acc_train.item(), time.time() - t 64 | 65 | 66 | def test(test_adj, test_feats, test_labels, epoch): 67 | t = time.time() 68 | model.eval() 69 | outputs = model(test_feats, test_adj) 70 | loss_test = loss_fn(outputs, test_labels) 71 | acc_test = accuracy(outputs, test_labels) 72 | 73 | return loss_test.item(), acc_test.item(), time.time() - t 74 | 75 | 76 | if __name__ == '__main__': 77 | # load data, set superpara and constant 78 | args = get_args() 79 | adj, features, adj_train, train_features, y_train, y_test, test_index = \ 80 | load_data(args.dataset) 81 | 82 | layer_sizes = [128, 128] 83 | input_dim = features.shape[1] 84 | train_nums = adj_train.shape[0] 85 | test_gap = args.test_gap 86 | nclass = y_train.shape[1] 87 | 88 | np.random.seed(args.seed) 89 | torch.manual_seed(args.seed) 90 | if args.cuda: 91 | torch.cuda.manual_seed(args.seed) 92 | # set device 93 | if args.cuda: 94 | device = torch.device("cuda") 95 | print("use cuda") 96 | else: 97 | device = torch.device("cpu") 98 | 99 | # data for train and test 100 | features = torch.FloatTensor(features).to(device) 101 | train_features = torch.FloatTensor(train_features).to(device) 102 | y_train = torch.LongTensor(y_train).to(device).max(1)[1] 103 | 104 | test_adj = [adj, adj[test_index, :]] 105 | test_feats = features 106 | test_labels = y_test 107 | test_adj = [sparse_mx_to_torch_sparse_tensor(cur_adj).to(device) 108 | for cur_adj in test_adj] 109 | test_labels = torch.LongTensor(test_labels).to(device).max(1)[1] 110 | 111 | # init the sampler 112 | if args.model == 'Fast': 113 | sampler = Sampler_FastGCN(None, train_features, adj_train, 114 | input_dim=input_dim, 115 | layer_sizes=layer_sizes, 116 | device=device) 117 | elif args.model == 'AS': 118 | sampler = Sampler_ASGCN(None, train_features, adj_train, 119 | input_dim=input_dim, 120 | layer_sizes=layer_sizes, 121 | device=device) 122 | else: 123 | print(f"model name error, no model named {args.model}") 124 | exit() 125 | 126 | # init model, optimizer and loss function 127 | model = GCN(nfeat=features.shape[1], 128 | nhid=args.hidden, 129 | nclass=nclass, 130 | dropout=args.dropout, 131 | sampler=sampler).to(device) 132 | optimizer = optim.Adam(model.parameters(), 133 | lr=args.lr, weight_decay=args.weight_decay) 134 | loss_fn = F.nll_loss 135 | # loss_fn = torch.nn.CrossEntropyLoss() 136 | 137 | # train and test 138 | for epochs in range(0, args.epochs // test_gap): 139 | train_loss, train_acc, train_time = train(np.arange(train_nums), 140 | y_train, 141 | args.batchsize, 142 | test_gap) 143 | test_loss, test_acc, test_time = test(test_adj, 144 | test_feats, 145 | test_labels, 146 | args.epochs) 147 | print(f"epchs:{epochs * test_gap}~{(epochs + 1) * test_gap - 1} " 148 | f"train_loss: {train_loss:.3f}, " 149 | f"train_acc: {train_acc:.3f}, " 150 | f"train_times: {train_time:.3f}s " 151 | f"test_loss: {test_loss:.3f}, " 152 | f"test_acc: {test_acc:.3f}, " 153 | f"test_times: {test_time:.3f}s") 154 | -------------------------------------------------------------------------------- /utils.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import pdb 3 | import pickle as pkl 4 | 5 | import torch 6 | import numpy as np 7 | import networkx as nx 8 | import scipy.sparse as sp 9 | 10 | 11 | def _load_data(dataset_str): 12 | """Load data.""" 13 | 14 | def parse_index_file(filename): 15 | """Parse index file.""" 16 | index = [] 17 | for line in open(filename): 18 | index.append(int(line.strip())) 19 | return index 20 | 21 | def sample_mask(idx, l): 22 | """Create mask.""" 23 | mask = np.zeros(l) 24 | mask[idx] = 1 25 | return np.array(mask, dtype=np.bool) 26 | 27 | names = ['x', 'y', 'tx', 'ty', 'allx', 'ally', 'graph'] 28 | objects = [] 29 | for i in range(len(names)): 30 | with open("data/ind.{}.{}".format(dataset_str, names[i]), 'rb') as f: 31 | if sys.version_info > (3, 0): 32 | objects.append(pkl.load(f, encoding='latin1')) 33 | else: 34 | objects.append(pkl.load(f)) 35 | 36 | x, y, tx, ty, allx, ally, graph = tuple(objects) 37 | test_idx_reorder = parse_index_file( 38 | "data/ind.{}.test.index".format(dataset_str)) 39 | test_idx_range = np.sort(test_idx_reorder) 40 | 41 | if dataset_str == 'citeseer': 42 | # Fix citeseer dataset (there are some isolated nodes in the graph) 43 | # Find isolated nodes, add them as zero-vecs into the right position 44 | test_idx_range_full = range( 45 | min(test_idx_reorder), max(test_idx_reorder)+1) 46 | tx_extended = sp.lil_matrix((len(test_idx_range_full), x.shape[1])) 47 | tx_extended[test_idx_range-min(test_idx_range), :] = tx 48 | tx = tx_extended 49 | ty_extended = np.zeros((len(test_idx_range_full), y.shape[1])) 50 | ty_extended[test_idx_range-min(test_idx_range), :] = ty 51 | ty = ty_extended 52 | 53 | features = sp.vstack((allx, tx)).tolil() 54 | features[test_idx_reorder, :] = features[test_idx_range, :] 55 | adj = nx.adjacency_matrix(nx.from_dict_of_lists(graph)) 56 | 57 | labels = np.vstack((ally, ty)) 58 | labels[test_idx_reorder, :] = labels[test_idx_range, :] 59 | 60 | idx_test = test_idx_range.tolist() 61 | idx_train = range(len(ally)-500) 62 | idx_val = range(len(ally)-500, len(ally)) 63 | # idx_train = range(len(y)) 64 | # idx_val = range(len(y), len(y)+500) 65 | 66 | train_mask = sample_mask(idx_train, labels.shape[0]) 67 | val_mask = sample_mask(idx_val, labels.shape[0]) 68 | test_mask = sample_mask(idx_test, labels.shape[0]) 69 | 70 | y_train = np.zeros(labels.shape) 71 | y_val = np.zeros(labels.shape) 72 | y_test = np.zeros(labels.shape) 73 | y_train[train_mask, :] = labels[train_mask, :] 74 | y_val[val_mask, :] = labels[val_mask, :] 75 | y_test[test_mask, :] = labels[test_mask, :] 76 | 77 | return (adj, features, y_train, y_val, y_test, 78 | train_mask, val_mask, test_mask) 79 | 80 | 81 | def nontuple_preprocess_features(features): 82 | """Row-normalize feature matrix and convert to tuple representation""" 83 | rowsum = np.array(features.sum(1)) 84 | ep = 1e-10 85 | r_inv = np.power(rowsum + ep, -1).flatten() 86 | r_inv[np.isinf(r_inv)] = 0. 87 | r_mat_inv = sp.diags(r_inv) 88 | features = r_mat_inv.dot(features) 89 | return features 90 | 91 | 92 | def normalize_adj(adj): 93 | """Symmetrically normalize adjacency matrix.""" 94 | adj = sp.coo_matrix(adj) 95 | rowsum = np.array(adj.sum(1)) 96 | d_inv_sqrt = np.power(rowsum, -0.5).flatten() 97 | d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0. 98 | d_mat_inv_sqrt = sp.diags(d_inv_sqrt) 99 | return adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).tocoo() 100 | 101 | 102 | def nontuple_preprocess_adj(adj): 103 | adj_normalized = normalize_adj(sp.eye(adj.shape[0]) + adj) 104 | # adj_normalized = sp.eye(adj.shape[0]) + normalize_adj(adj) 105 | return adj_normalized.tocsr() 106 | 107 | 108 | def load_data(dataset): 109 | # train_mask, val_mask, test_mask: np.ndarray, [True/False] * node_number 110 | adj, features, y_train, y_val, y_test, train_mask, val_mask, test_mask = \ 111 | _load_data(dataset) 112 | # pdb.set_trace() 113 | train_index = np.where(train_mask)[0] 114 | adj_train = adj[train_index, :][:, train_index] 115 | y_train = y_train[train_index] 116 | val_index = np.where(val_mask)[0] 117 | y_val = y_val[val_index] 118 | test_index = np.where(test_mask)[0] 119 | y_test = y_test[test_index] 120 | 121 | num_train = adj_train.shape[0] 122 | 123 | features = nontuple_preprocess_features(features).todense() 124 | train_features = features[train_index] 125 | 126 | norm_adj_train = nontuple_preprocess_adj(adj_train) 127 | norm_adj = nontuple_preprocess_adj(adj) 128 | 129 | if dataset == 'pubmed': 130 | norm_adj = 1*sp.diags(np.ones(norm_adj.shape[0])) + norm_adj 131 | norm_adj_train = 1*sp.diags(np.ones(num_train)) + norm_adj_train 132 | 133 | # change type to tensor 134 | # norm_adj = sparse_mx_to_torch_sparse_tensor(norm_adj) 135 | # features = torch.FloatTensor(features) 136 | # norm_adj_train = sparse_mx_to_torch_sparse_tensor(norm_adj_train) 137 | # train_features = torch.FloatTensor(train_features) 138 | # y_train = torch.LongTensor(y_train) 139 | # y_test = torch.LongTensor(y_test) 140 | # test_index = torch.LongTensor(test_index) 141 | 142 | return (norm_adj, features, norm_adj_train, train_features, 143 | y_train, y_test, test_index) 144 | 145 | 146 | def get_batches(train_ind, train_labels, batch_size=64, shuffle=True): 147 | """ 148 | Inputs: 149 | train_ind: np.array 150 | """ 151 | nums = train_ind.shape[0] 152 | if shuffle: 153 | np.random.shuffle(train_ind) 154 | i = 0 155 | while i < nums: 156 | cur_ind = train_ind[i:i + batch_size] 157 | cur_labels = train_labels[cur_ind] 158 | yield cur_ind, cur_labels 159 | i += batch_size 160 | 161 | 162 | def accuracy(output, labels): 163 | preds = output.max(1)[1].type_as(labels) 164 | correct = preds.eq(labels).double() 165 | correct = correct.sum() 166 | return correct / len(labels) 167 | 168 | 169 | def sparse_mx_to_torch_sparse_tensor(sparse_mx): 170 | """Convert a scipy sparse matrix to a torch sparse tensor.""" 171 | sparse_mx = sparse_mx.tocoo().astype(np.float32) 172 | indices = torch.from_numpy( 173 | np.vstack((sparse_mx.row, sparse_mx.col)).astype(np.int64)) 174 | values = torch.from_numpy(sparse_mx.data) 175 | shape = torch.Size(sparse_mx.shape) 176 | return torch.sparse.FloatTensor(indices, values, shape) 177 | 178 | 179 | if __name__ == '__main__': 180 | pdb.set_trace() 181 | adj, features, adj_train, train_features, y_train, y_test, test_index = \ 182 | load_data('cora') 183 | pdb.set_trace() 184 | --------------------------------------------------------------------------------