├── BYVENUE.rst
├── LICENSE
└── README.rst


/BYVENUE.rst:
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   1 | Literature of Deep Learning for Graphs
   2 | **************************************
   3 | 
   4 | This is a paper list about deep learning for graphs.
   5 | 
   6 | .. raw:: html
   7 | 
   8 |     <div><a href="README.rst">Sort by topic</a></div>
   9 |     <div><a href="BYVENUE.rst">Sort by venue</a></div>
  10 | 
  11 | .. contents::
  12 |     :local:
  13 |     :depth: 4
  14 | 
  15 | .. sectnum::
  16 |     :depth: 4
  17 | 
  18 | .. role:: authors(emphasis)
  19 | 
  20 | .. role:: venue(strong)
  21 | 
  22 | .. role:: keywords(emphasis)
  23 | 
  24 | 2008
  25 | ====
  26 | 
  27 | JMLR
  28 | ----
  29 | 
  30 | `Visualizing Data Using T-sne
  31 | <http://www.jmlr.org/papers/volume9/vandermaaten08a/vandermaaten08a.pdf>`_
  32 |     | :authors:`Laurens, van, der, Maaten, Geoffrey, Hinton`
  33 |     | :venue:`JMLR 2008`
  34 | 
  35 | 2011
  36 | ====
  37 | 
  38 | ICML
  39 | ----
  40 | 
  41 | `A Three-way Model for Collective Learning on Multi-relational Data.
  42 | <http://www.icml-2011.org/papers/438_icmlpaper.pdf>`_
  43 |     | :authors:`Maximilian, Nickel, Volker, Tresp, Hans-Peter, Kriegel`
  44 |     | :venue:`ICML 2011`
  45 | 
  46 | 2012
  47 | ====
  48 | 
  49 | ML
  50 | --
  51 | 
  52 | `Visualizing Non-metric Similarities in Multiple Maps
  53 | <https://link.springer.com/content/pdf/10.1007/s10994-011-5273-4.pdf>`_
  54 |     | :authors:`Laurens, van, der, Maaten, Geoffrey, Hinton`
  55 |     | :venue:`ML 2012`
  56 | 
  57 | 2013
  58 | ====
  59 | 
  60 | NIPS
  61 | ----
  62 | 
  63 | `Translating Embeddings for Modeling Multi-relational Data
  64 | <https://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data.pdf>`_
  65 |     | :authors:`Antoine, Bordes, Nicolas, Usunier, Alberto, Garcia-Duran, Jason, Weston, Oksana, Yakhnenko`
  66 |     | :venue:`NIPS 2013`
  67 | 
  68 | 2014
  69 | ====
  70 | 
  71 | WSDM
  72 | ----
  73 | 
  74 | `Learning Latent Representations of Nodes for Classifying in Heterogeneous Social Networks
  75 | <https://dl.acm.org/citation.cfm?id=2556225>`_
  76 |     | :authors:`Yann, Jacob, Ludovic, Denoyer, Patrick, Gallinari`
  77 |     | :venue:`WSDM 2014`
  78 | 
  79 | AAAI
  80 | ----
  81 | 
  82 | `Knowledge Graph Embedding by Translating on Hyperplanes
  83 | <https://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/viewFile/8531/8546>`_
  84 |     | :authors:`Zhen, Wang, Jianwen, Zhang, Jianlin, Feng, Zheng, Chen`
  85 |     | :venue:`AAAI 2014`
  86 | 
  87 | KDD
  88 | ---
  89 | 
  90 | `Deepwalk: Online Learning of Social Representations
  91 | <https://arxiv.org/pdf/1403.6652>`_
  92 |     | :authors:`Bryan, Perozzi, Rami, Al-Rfou, Steven, Skiena`
  93 |     | :venue:`KDD 2014`
  94 |     | :keywords:`Node, classification, Random, walk, Skip-gram`
  95 | 
  96 | NIPS
  97 | ----
  98 | 
  99 | `Reducing the Rank of Relational Factorization Models by Including Observable Patterns
 100 | <http://papers.nips.cc/paper/5448-reducing-the-rank-in-relational-factorization-models-by-including-observable-patterns.pdf>`_
 101 |     | :authors:`Maximilian, Nickel, Xueyan, Jiang, Volker, Tresp`
 102 |     | :venue:`NIPS 2014`
 103 | 
 104 | 2015
 105 | ====
 106 | 
 107 | AAAI
 108 | ----
 109 | 
 110 | `Network Representation Learning with Rich Text Information
 111 | <https://www.aaai.org/ocs/index.php/IJCAI/IJCAI15/paper/view/11098>`_
 112 |     | :authors:`Cheng, Yang, Zhiyuan, Liu, Deli, Zhao, Maosong, Sun, Edward, Chang`
 113 |     | :venue:`AAAI 2015`
 114 | 
 115 | `Learning Entity and Relation Embeddings for Knowledge Graph Completion
 116 | <https://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/viewFile/9571/9523>`_
 117 |     | :authors:`Yankai, Lin, Zhiyuan, Liu, Maosong, Sun, Yang, Liu, Xuan, Zhu`
 118 |     | :venue:`AAAI 2015`
 119 | 
 120 | ACL
 121 | ---
 122 | 
 123 | `Knowledge Graph Embedding via Dynamic Mapping Matrix
 124 | <https://www.aclweb.org/anthology/P15-1067>`_
 125 |     | :authors:`Guoliang, Ji, Shizhu, He, Liheng, Xu, Kang, Liu, Jun, Zha`
 126 |     | :venue:`ACL 2015`
 127 | 
 128 | CIKM
 129 | ----
 130 | 
 131 | `Grarep: Learning Graph Representations with Global Structural Information
 132 | <https://dl.acm.org/citation.cfm?id=2806512>`_
 133 |     | :authors:`Shaosheng, Cao, Wei, Lu, Qiongkai, Xu`
 134 |     | :venue:`CIKM 2015`
 135 |     | :keywords:`High-order, SVD`
 136 | 
 137 | EMNLP
 138 | -----
 139 | 
 140 | `Modeling Relation Paths for Representation Learning of Knowledge Bases
 141 | <https://arxiv.org/pdf/1506.00379>`_
 142 |     | :authors:`Yankai, Lin, Zhiyuan, Liu, Huanbo, Luan, Maosong, Sun, Siwei, Rao, Song, Liu`
 143 |     | :venue:`EMNLP 2015`
 144 | 
 145 | ICLR
 146 | ----
 147 | 
 148 | `Embedding Entities and Relations for Learning and Inference in Knowledge Bases
 149 | <https://arxiv.org/pdf/1412.6575>`_
 150 |     | :authors:`Bishan, Yang, Wen-tau, Yih, Xiaodong, He, Jianfeng, Gao, Li, Deng`
 151 |     | :venue:`ICLR 2015`
 152 | 
 153 | IEEE
 154 | ----
 155 | 
 156 | `A Review of Relational Machine Learning for Knowledge Graph
 157 | <https://arxiv.org/pdf/1503.00759.pdf>`_
 158 |     | :authors:`Maximilian, Nickel, Kevin, Murphy, Volker, Tresp, Evgeniy, Gabrilovich`
 159 |     | :venue:`IEEE 2015`
 160 | 
 161 | KDD
 162 | ---
 163 | 
 164 | `Pte: Predictive Text Embedding through Large-scale Heterogeneous Text Networks
 165 | <https://arxiv.org/pdf/1508.00200>`_
 166 |     | :authors:`Jian, Tang, Meng, Qu, Qiaozhu, Mei`
 167 |     | :venue:`KDD 2015`
 168 |     | :keywords:`Text, Embedding, Heterogeneous, Text, Graphs`
 169 | 
 170 | `Heterogeneous Network Embedding via Deep Architectures
 171 | <https://dl.acm.org/citation.cfm?id=2783296>`_
 172 |     | :authors:`Shiyu, Chang, Wei, Han, Jiliang, Tang, Guo-Jun, Qi, Charu, C., Aggarwal, Thomas, S., Huang`
 173 |     | :venue:`KDD 2015`
 174 | 
 175 | WWW
 176 | ---
 177 | 
 178 | `Line: Large-scale Information Network Embedding
 179 | <https://arxiv.org/pdf/1503.03578>`_
 180 |     | :authors:`Jian, Tang, Meng, Qu, Mingzhe, Wang, Ming, Zhang, Jun, Yan, Qiaozhu, Mei`
 181 |     | :venue:`WWW 2015`
 182 |     | :keywords:`First-order, Second-order, Node, classification`
 183 | 
 184 | 2016
 185 | ====
 186 | 
 187 | arXiv
 188 | -----
 189 | 
 190 | `Variational Graph Auto-encoders
 191 | <https://arxiv.org/abs/1611.07308>`_
 192 |     | :authors:`Thomas, N., Kipf, Max, Welling`
 193 |     | :venue:`arXiv 2016`
 194 | 
 195 | `Meta-path Guided Embedding for Similarity Search in Large-scale Heterogeneous Information Networks
 196 | <https://arxiv.org/pdf/1610.09769>`_
 197 |     | :authors:`Jingbo, Shang, Meng, Qu, Jialu, Liu, Lance, M., Kaplan, Jiawei, Han, Jian, Peng`
 198 |     | :venue:`arXiv 2016`
 199 | 
 200 | IJCAI
 201 | -----
 202 | 
 203 | `Max-margin Deepwalk: Discriminative Learning of Network Representation
 204 | <https://www.ijcai.org/Proceedings/16/Papers/547.pdf>`_
 205 |     | :authors:`Cunchao, Tu, Weicheng, Zhang, Zhiyuan, Liu, Maosong, Sun`
 206 |     | :venue:`IJCAI 2016`
 207 | 
 208 | AAAI
 209 | ----
 210 | 
 211 | `Holographic Embeddings of Knowledge Graphs
 212 | <https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/viewPDFInterstitial/12484/11828>`_
 213 |     | :authors:`Maximilian, Nickel, Lorenzo, Rosasco, Tomaso, Poggio`
 214 |     | :venue:`AAAI 2016`
 215 | 
 216 | ICML
 217 | ----
 218 | 
 219 | `Complex Embeddings for Simple Link Prediction
 220 | <http://www.jmlr.org/proceedings/papers/v48/trouillon16.pdf>`_
 221 |     | :authors:`Théo, Trouillon, Johannes, Welbl, Sebastian, Riedel, Éric, Gaussier, Guillaume, Bouchard`
 222 |     | :venue:`ICML 2016`
 223 | 
 224 | `Revisiting Semi-supervised Learning with Graph Embeddings
 225 | <https://arxiv.org/pdf/1603.08861>`_
 226 |     | :authors:`Zhilin, Yang, William, W., Cohen, Ruslan, Salakhutdinov`
 227 |     | :venue:`ICML 2016`
 228 | 
 229 | WWW
 230 | ---
 231 | 
 232 | `Visualizing Large-scale and High-dimensional Data
 233 | <https://arxiv.org/pdf/1602.00370>`_
 234 |     | :authors:`Jian, Tang, Jingzhou, Liu, Ming, Zhang, Qiaozhu, Mei`
 235 |     | :venue:`WWW 2016`
 236 | 
 237 | KDD
 238 | ---
 239 | 
 240 | `Node2vec: Scalable Feature Learning for Networks
 241 | <https://arxiv.org/pdf/1607.00653>`_
 242 |     | :authors:`Aditya, Grover, Jure, Leskovec`
 243 |     | :venue:`KDD 2016`
 244 |     | :keywords:`Breadth-first, Search, Depth-first, Search, Node, Classification, Link, Prediction`
 245 | 
 246 | 2017
 247 | ====
 248 | 
 249 | AAAI
 250 | ----
 251 | 
 252 | `Scalable Graph Embedding for Asymmetric Proximity
 253 | <https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14696>`_
 254 |     | :authors:`Chang, Zhou, Yuqiong, Liu, Xiaofei, Liu, Zhongyi, Liu, Jun, Gao`
 255 |     | :venue:`AAAI 2017`
 256 | 
 257 | ACL
 258 | ---
 259 | 
 260 | `Cane: Context-aware Network Embedding for Relation Modeling
 261 | <https://aclweb.org/anthology/papers/P/P17/P17-1158/>`_
 262 |     | :authors:`Cunchao, Tu, Han, Liu, Zhiyuan, Liu, Maosong, Sun`
 263 |     | :venue:`ACL 2017`
 264 | 
 265 | CIKM
 266 | ----
 267 | 
 268 | `Hin2vec: Explore Meta-paths in Heterogeneous Information Networks for Representation Learning
 269 | <https://dl.acm.org/citation.cfm?id=3132953>`_
 270 |     | :authors:`Tao-yang, Fu, Wang-Chien, Lee, Zhen, Lei`
 271 |     | :venue:`CIKM 2017`
 272 | 
 273 | `An Attention-based Collaboration Framework for Multi-view Network Representation Learning
 274 | <https://arxiv.org/pdf/1709.06636>`_
 275 |     | :authors:`Meng, Qu, Jian, Tang, Jingbo, Shang, Xiang, Ren, Ming, Zhang, Jiawei, Han`
 276 |     | :venue:`CIKM 2017`
 277 | 
 278 | `Multi-view Clustering with Graph Embedding for Connectome Analysis
 279 | <https://dl.acm.org/citation.cfm?id=3132909>`_
 280 |     | :authors:`Guixiang, Ma, Lifang, He, Chun-Ta, Lu, Weixiang, Shao, Philip, S., Yu, Alex, D., Leow, Ann, B., Ragin`
 281 |     | :venue:`CIKM 2017`
 282 | 
 283 | `Attributed Signed Network Embedding
 284 | <https://dl.acm.org/citation.cfm?id=3132847.3132905>`_
 285 |     | :authors:`Suhang, Wang, Charu, Aggarwal, Jiliang, Tang, Huan, Liu`
 286 |     | :venue:`CIKM 2017`
 287 | 
 288 | `Attributed Network Embedding for Learning in a Dynamic Environment
 289 | <https://arxiv.org/pdf/1706.01860.pdf>`_
 290 |     | :authors:`Jundong, Li, Harsh, Dani, Xia, Hu, Jiliang, Tang, Yi, Chang, Huan, Liu`
 291 |     | :venue:`CIKM 2017`
 292 | 
 293 | CoNLL
 294 | -----
 295 | 
 296 | `Graph-based Neural Multi-document Summarization
 297 | <https://www.aclweb.org/anthology/K17-1045>`_
 298 |     | :authors:`Michihiro, Yasunaga, Rui, Zhang, Kshitijh, Meelu, Ayush, Pareek, Krishnan, Srinivasan, Dragomir, Radev`
 299 |     | :venue:`CoNLL 2017`
 300 | 
 301 | EMNLP
 302 | -----
 303 | 
 304 | `Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling
 305 | <https://www.aclweb.org/anthology/D17-1159>`_
 306 |     | :authors:`Diego, Marcheggiani, Ivan, Titov`
 307 |     | :venue:`EMNLP 2017`
 308 | 
 309 | `Graph Convolutional Encoders for Syntax-aware Neural Machine Translation
 310 | <https://www.aclweb.org/anthology/D17-1209>`_
 311 |     | :authors:`Joost, Bastings, Ivan, Titov, Wilker, Aziz, Diego, Marcheggiani, Khalil, Sima’an`
 312 |     | :venue:`EMNLP 2017`
 313 | 
 314 | ICCV
 315 | ----
 316 | 
 317 | `3d Graph Neural Networks for Rgbd Semantic Segmentation
 318 | <http://www.cs.toronto.edu/~rjliao/papers/iccv_2017_3DGNN.pdf>`_
 319 |     | :authors:`Xiaojuan, Qi, Renjie, Liao, Jiaya, Jia, Sanja, Fidler, Raquel, Urtasun`
 320 |     | :venue:`ICCV 2017`
 321 | 
 322 | `Situation Recognition With Graph Neural Networks
 323 | <https://arxiv.org/abs/1708.04320>`_
 324 |     | :authors:`Ruiyu, Li, Makarand, Tapaswi, Renjie, Liao, Jiaya, Jia, Raquel, Urtasun, Sanja, Fidler`
 325 |     | :venue:`ICCV 2017`
 326 | 
 327 | `Graph-based Classification of Omnidirectional Images
 328 | <https://arxiv.org/abs/1707.08301>`_
 329 |     | :authors:`Renata, Khasanova, Pascal, Frossard`
 330 |     | :venue:`ICCV 2017`
 331 | 
 332 | ICLR
 333 | ----
 334 | 
 335 | `Dyngem: Deep Embedding Method for Dynamic Graphs
 336 | <https://arxiv.org/pdf/1805.11273.pdf>`_
 337 |     | :authors:`Palash, Goyal, Nitin, Kamra, Xinran, He, Yan, Liu`
 338 |     | :venue:`ICLR 2017 Workshop`
 339 | 
 340 | `Semi-supervised Classification with Graph Convolutional Networks
 341 | <https://arxiv.org/pdf/1609.02907>`_
 342 |     | :authors:`Thomas, N., Kipf, Max, Welling`
 343 |     | :venue:`ICLR 2017`
 344 | 
 345 | ICML
 346 | ----
 347 | 
 348 | `Know-evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs
 349 | <https://arxiv.org/pdf/1705.05742.pdf>`_
 350 |     | :authors:`Rakshit, Trivedi, Hanjun, Dai, Yichen, Wang, Le, Song`
 351 |     | :venue:`ICML 2017`
 352 | 
 353 | `Neural Message Passing for Quantum Chemistry
 354 | <https://arxiv.org/pdf/1704.01212>`_
 355 |     | :authors:`Justin, Gilmer, Samuel, S., Schoenholz, Patrick, F., Riley, Oriol, Vinyals, George, E., Dahl`
 356 |     | :venue:`ICML 2017`
 357 | 
 358 | IJCAI
 359 | -----
 360 | 
 361 | `Fast Network Embedding Enhancement via High Order Proximity Approximation
 362 | <https://www.ijcai.org/proceedings/2017/544>`_
 363 |     | :authors:`Cheng, Yang, Maosong, Sun, Zhiyuan, Liu, Cunchao, Tu`
 364 |     | :venue:`IJCAI 2017`
 365 | 
 366 | `Motif-aware Graph Embeddings
 367 | <http://gearons.org/assets/docs/motif-aware-graph-final.pdf>`_
 368 |     | :authors:`Hoang, Nguyen, Tsuyoshi, Murata`
 369 |     | :venue:`IJCAI 2017`
 370 | 
 371 | KDD
 372 | ---
 373 | 
 374 | `Struc2vec: Learning Node Representations from Structural Identity
 375 | <https://arxiv.org/pdf/1704.03165>`_
 376 |     | :authors:`Leonardo, F., R., Ribeiro, Pedro, H., P., Savarese, Daniel, R., Figueiredo`
 377 |     | :venue:`KDD 2017`
 378 |     | :keywords:`Structural, Identity`
 379 | 
 380 | `Metapath2vec: Scalable Representation Learning for Heterogeneous Networks
 381 | <https://dl.acm.org/citation.cfm?id=3098036>`_
 382 |     | :authors:`Yuxiao, Dong, Nitesh, V., Chawla, Ananthram, Swami`
 383 |     | :venue:`KDD 2017`
 384 | 
 385 | NIPS
 386 | ----
 387 | 
 388 | `Poincaré Embeddings for Learning Hierarchical Representations
 389 | <https://arxiv.org/pdf/1705.08039>`_
 390 |     | :authors:`Maximilian, Nickel, Douwe, Kiela`
 391 |     | :venue:`NIPS 2017`
 392 | 
 393 | `Learning Graph Representations with Embedding Propagation
 394 | <https://arxiv.org/pdf/1710.03059>`_
 395 |     | :authors:`Alberto, Garcia-Duran, Mathias, Niepert`
 396 |     | :venue:`NIPS 2017`
 397 | 
 398 | `Inductive Representation Learning on Large Graphs
 399 | <https://arxiv.org/pdf/1706.02216>`_
 400 |     | :authors:`William, L., Hamilton, Rex, Ying, Jure, Leskovec`
 401 |     | :venue:`NIPS 2017`
 402 | 
 403 | NeurIPS
 404 | -------
 405 | 
 406 | `Learning Combinatorial Optimization Algorithms over Graphs
 407 | <https://arxiv.org/abs/1704.01665>`_
 408 |     | :authors:`Hanjun, Dai, Elias, B., Khalil, Yuyu, Zhang, Bistra, Dilkina, Le, Song`
 409 |     | :venue:`NeurIPS 2017`
 410 | 
 411 | `Protein Interface Prediction Using Graph Convolutional Networks
 412 | <https://papers.nips.cc/paper/7231-protein-interface-prediction-using-graph-convolutional-networks.pdf>`_
 413 |     | :authors:`Alex, Fout, Jonathon, Byrd, Basir, Shariat, Asa, Ben-Hur`
 414 |     | :venue:`NeurIPS 2017`
 415 | 
 416 | `Premise Selection for Theorem Proving by Deep Graph Embedding
 417 | <https://arxiv.org/abs/1709.09994>`_
 418 |     | :authors:`Mingzhe, Wang, Yihe, Tang, Jian, Wang, Jia, Deng`
 419 |     | :venue:`NeurIPS 2017`
 420 | 
 421 | arXiv
 422 | -----
 423 | 
 424 | `Modeling Relational Data with Graph Convolutional Networks
 425 | <https://arxiv.org/pdf/1703.06103>`_
 426 |     | :authors:`Michael, Schlichtkrull, Thomas, N., Kipf, Peter, Bloem, Rianne, Van, Den, Berg, Ivan, Titov, Max, Welling`
 427 |     | :venue:`arXiv 2017`
 428 | 
 429 | `Fast Linear Model for Knowledge Graph Embeddings
 430 | <https://arxiv.org/pdf/1710.10881>`_
 431 |     | :authors:`Armand, Joulin, Edouard, Grave, Piotr, Bojanowski, Maximilian, Nickel, Tomas, Mikolov`
 432 |     | :venue:`arXiv 2017`
 433 | 
 434 | 2018
 435 | ====
 436 | 
 437 | AAAI
 438 | ----
 439 | 
 440 | `Adversarial Network Embedding
 441 | <https://arxiv.org/pdf/1711.07838>`_
 442 |     | :authors:`Quanyu, Dai, Qiang, Li, Jian, Tang, Dan, Wang`
 443 |     | :venue:`AAAI 2018`
 444 | 
 445 | `Graphgan: Graph Representation Learning with Generative Adversarial Nets
 446 | <https://arxiv.org/pdf/1711.08267>`_
 447 |     | :authors:`Hongwei, Wang, Jia, Wang, Jialin, Wang, Miao, Zhao, Weinan, Zhang, Fuzheng, Zhang, Xing, Xie, Minyi, Guo`
 448 |     | :venue:`AAAI 2018`
 449 | 
 450 | `Starspace: Embed All The Things
 451 | <https://arxiv.org/pdf/1709.03856>`_
 452 |     | :authors:`Ledell, Wu, Adam, Fisch, Sumit, Chopra, Keith, Adams, Antoine, Bordes, Jason, Weston`
 453 |     | :venue:`AAAI 2018`
 454 | 
 455 | `Generative Adversarial Network Based Heterogeneous Bibliographic Network Representation for Personalized Citation Recommendation
 456 | <https://www.semanticscholar.org/paper/Generative-Adversarial-Network-Based-Heterogeneous-Cai-Han/1596d6487012696ba400fb69904a2c372a08a2be>`_
 457 |     | :authors:`Xiaoyan, Cai, Junwei, Han, Libin, Yang`
 458 |     | :venue:`AAAI 2018`
 459 | 
 460 | `Dynamic Network Embedding by Modeling Triadic Closure Process
 461 | <http://yangy.org/works/dynamictriad/dynamic_triad.pdf>`_
 462 |     | :authors:`Lekui, Zhou, Yang, Yang, Xiang, Ren, Fei, Wu, Yueting, Zhuang`
 463 |     | :venue:`AAAI 2018`
 464 | 
 465 | `Depthlgp: Learning Embeddings of Out-of-sample Nodes in Dynamic Networks
 466 | <https://pdfs.semanticscholar.org/9499/b38866b1eb87ae43fa5be02f9d08cd3c20a8.pdf?_ga=2.6780794.935636364.1561139530-1831876308.1523264869>`_
 467 |     | :authors:`Jianxin, Ma, Peng, Cui, Wenwu, Zhu`
 468 |     | :venue:`AAAI 2018`
 469 | 
 470 | `Timers: Error-bounded Svd Restart on Dynamic Networks
 471 | <https://arxiv.org/pdf/1711.09541.pdf>`_
 472 |     | :authors:`Ziwei, Zhang, Peng, Cui, Jian, Pei, Xiao, Wang, Wenwu, Zhu`
 473 |     | :venue:`AAAI 2018`
 474 | 
 475 | `Convolutional 2d Knowledge Graph Embeddings
 476 | <https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/download/17366/15884>`_
 477 |     | :authors:`Tim, Dettmers, Pasquale, Minervini, Pontus, Stenetorp, Sebastian, Riedel`
 478 |     | :venue:`AAAI 2018`
 479 | 
 480 | `Knowledge Graph Embedding With Iterative Guidance From Soft Rules
 481 | <https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/download/16369/16011>`_
 482 |     | :authors:`Shu, Guo, Quan, Wang, Lihong, Wang, Bin, Wang, Li, Guo`
 483 |     | :venue:`AAAI 2018`
 484 | 
 485 | `Spatial Temporal Graph Convolutional Networks for Skeleton-based Action Recognition
 486 | <https://arxiv.org/abs/1801.07455>`_
 487 |     | :authors:`Sijie, Yan, Yuanjun, Xiong, Dahua, Lin`
 488 |     | :venue:`AAAI 2018`
 489 | 
 490 | `Socialgcn: An Efficient Graph Convolutional Network Based Model for Social Recommendation
 491 | <https://arxiv.org/pdf/1811.02815.pdf>`_
 492 |     | :authors:`Le, Wu, Peijie, Sun, Richang, Hong, Yanjie, Fu, Xiting, Wang, Meng, Wang`
 493 |     | :venue:`AAAI 2018`
 494 |     | :keywords:`GCN, Social, recommendation`
 495 | 
 496 | `Link Prediction via Subgraph Embedding-based Convex Matrix Completion
 497 | <http://iiis.tsinghua.edu.cn/~weblt/papers/link-prediction-subgraphembeddings.pdf>`_
 498 |     | :authors:`Zhu, Cao, Linlin, Wang, Gerard, de, Melo`
 499 |     | :venue:`AAAI 2018`
 500 | 
 501 | `Action Schema Networks: Generalised Policies with Deep Learning
 502 | <https://arxiv.org/pdf/1709.04271.pdf>`_
 503 |     | :authors:`Sam, Toyer, Felipe, Trevizan, Sylvie, Thiebaux, Lexing, Xie`
 504 |     | :venue:`AAAI 2018`
 505 | 
 506 | ACL
 507 | ---
 508 | 
 509 | `Improving Knowledge Graph Embedding Using Simple Constraints
 510 | <https://arxiv.org/abs/1805.02408>`_
 511 |     | :authors:`Boyang, Ding, Quan, Wang, Bin, Wang, Li, Guo`
 512 |     | :venue:`ACL 2018`
 513 | 
 514 | `A Graph-to-sequence Model for Amr-to-text Generation
 515 | <https://www.aclweb.org/anthology/P18-1150>`_
 516 |     | :authors:`Linfeng, Song, Yue, Zhang, Zhiguo, Wang, Daniel, Gildea`
 517 |     | :venue:`ACL 2018`
 518 | 
 519 | `Graph-to-sequence Learning Using Gated Graph Neural Networks
 520 | <https://www.aclweb.org/anthology/P18-1026>`_
 521 |     | :authors:`Daniel, Beck, Gholamreza, Haffari, Trevor, Cohn`
 522 |     | :venue:`ACL 2018`
 523 | 
 524 | Bioinformatics
 525 | --------------
 526 | 
 527 | `Modeling Polypharmacy Side Effects with Graph Convolutional Networks
 528 | <https://arxiv.org/abs/1802.00543>`_
 529 |     | :authors:`Marinka, Zitnik, Monica, Agrawal, Jure, Leskovec`
 530 |     | :venue:`Bioinformatics 2018`
 531 | 
 532 | `Neodti: Neural Integration of Neighbor Information from a Heterogeneous Network for Discovering New
 533 | <https://academic.oup.com/bioinformatics/article-abstract/35/1/104/5047760?redirectedFrom=fulltext>`_
 534 |     | :authors:`Fangping, Wan, Lixiang, Hong, An, Xiao, Tao, Jiang, Jianyang, Zeng`
 535 |     | :venue:`Bioinformatics 2018`
 536 | 
 537 | CIKM
 538 | ----
 539 | 
 540 | `Regal: Representation Learning-based Graph Alignment
 541 | <https://arxiv.org/pdf/1802.06257.pdf>`_
 542 |     | :authors:`Mark, Heimann, Haoming, Shen, Tara, Safavi, Danai, Koutra`
 543 |     | :venue:`CIKM 2018`
 544 | 
 545 | COLING
 546 | ------
 547 | 
 548 | `Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering
 549 | <https://aclweb.org/anthology/C18-1280>`_
 550 |     | :authors:`Daniil, Sorokin, Iryna, Gurevych`
 551 |     | :venue:`COLING 2018`
 552 | 
 553 | CVPR
 554 | ----
 555 | 
 556 | `Image Generation from Scene Graphs
 557 | <https://arxiv.org/abs/1804.01622>`_
 558 |     | :authors:`Justin, Johnson, Agrim, Gupta, Li, Fei-Fei`
 559 |     | :venue:`CVPR 2018`
 560 | 
 561 | `Foldingnet: Point Cloud Auto-encoder via Deep Grid Deformation
 562 | <https://arxiv.org/abs/1712.07262>`_
 563 |     | :authors:`Yaoqing, Yang, Chen, Feng, Yiru, Shen, Dong, Tian`
 564 |     | :venue:`CVPR 2018`
 565 | 
 566 | `Ppfnet: Global Context Aware Local Features for Robust 3d Point Matching
 567 | <https://arxiv.org/abs/1802.02669>`_
 568 |     | :authors:`Haowen, Deng, Tolga, Birdal, Slobodan, Ilic`
 569 |     | :venue:`CVPR 2018`
 570 | 
 571 | `Iterative Visual Reasoning Beyond Convolutions
 572 | <https://arxiv.org/abs/1803.11189>`_
 573 |     | :authors:`Xinlei, Chen, Li-Jia, Li, Li, Fei-Fei, Abhinav, Gupta`
 574 |     | :venue:`CVPR 2018`
 575 | 
 576 | `Surface Networks
 577 | <https://arxiv.org/abs/1705.10819>`_
 578 |     | :authors:`Ilya, Kostrikov, Zhongshi, Jiang, Daniele, Panozzo, Denis, Zorin, Joan, Bruna`
 579 |     | :venue:`CVPR 2018`
 580 | 
 581 | `Feastnet: Feature-steered Graph Convolutions for 3d Shape Analysis
 582 | <https://arxiv.org/abs/1706.05206>`_
 583 |     | :authors:`Nitika, Verma, Edmond, Boyer, Jakob, Verbeek`
 584 |     | :venue:`CVPR 2018`
 585 | 
 586 | `Learning to Act Properly: Predicting and Explaining Affordances From Images
 587 | <https://arxiv.org/abs/1712.07576>`_
 588 |     | :authors:`Ching-Yao, Chuang, Jiaman, Li, Antonio, Torralba, Sanja, Fidler`
 589 |     | :venue:`CVPR 2018`
 590 | 
 591 | `Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling
 592 | <https://arxiv.org/abs/1712.06760>`_
 593 |     | :authors:`Yiru, Shen, Chen, Feng, Yaoqing, Yang, Dong, Tian`
 594 |     | :venue:`CVPR 2018`
 595 | 
 596 | `Deformable Shape Completion With Graph Convolutional Autoencoders
 597 | <https://arxiv.org/abs/1712.00268>`_
 598 |     | :authors:`Or, Litany, Alex, Bronstein, Michael, Bronstein, Ameesh, Makadia`
 599 |     | :venue:`CVPR 2018`
 600 | 
 601 | ECCV
 602 | ----
 603 | 
 604 | `Pixel2mesh: Generating 3d Mesh Models from Single Rgb Images
 605 | <https://arxiv.org/abs/1804.01654>`_
 606 |     | :authors:`Nanyang, Wang, Yinda, Zhang, Zhuwen, Li, Yanwei, Fu, Wei, Liu, Yu-Gang, Jiang`
 607 |     | :venue:`ECCV 2018`
 608 | 
 609 | `Learning Human-object Interactions by Graph Parsing Neural Networks
 610 | <https://arxiv.org/abs/1808.07962>`_
 611 |     | :authors:`Siyuan, Qi, Wenguan, Wang, Baoxiong, Jia, Jianbing, Shen, Song-Chun, Zhu`
 612 |     | :venue:`ECCV 2018`
 613 | 
 614 | `Generating 3d Faces Using Convolutional Mesh Autoencoders
 615 | <https://arxiv.org/abs/1807.10267>`_
 616 |     | :authors:`Anurag, Ranjan, Timo, Bolkart, Soubhik, Sanyal, Michael, J., Black`
 617 |     | :venue:`ECCV 2018`
 618 | 
 619 | `Learning So(3) Equivariant Representations with Spherical Cnns
 620 | <https://arxiv.org/abs/1711.06721>`_
 621 |     | :authors:`Carlos, Esteves, Christine, Allen-Blanchette, Ameesh, Makadia, Kostas, Daniilidis`
 622 |     | :venue:`ECCV 2018`
 623 | 
 624 | `Neural Graph Matching Networks for Fewshot 3d Action Recognition
 625 | <http://openaccess.thecvf.com/content_ECCV_2018/papers/Michelle_Guo_Neural_Graph_Matching_ECCV_2018_paper.pdf>`_
 626 |     | :authors:`Michelle, Guo, Edward, Chou, De-An, Huang, Shuran, Song, Serena, Yeung, Li, Fei-Fei`
 627 |     | :venue:`ECCV 2018`
 628 | 
 629 | `Multi-kernel Diffusion Cnns for Graph-based Learning on Point Clouds
 630 | <https://arxiv.org/abs/1809.05370>`_
 631 |     | :authors:`Lasse, Hansen, Jasper, Diesel, Mattias, P., Heinrich`
 632 |     | :venue:`ECCV 2018`
 633 | 
 634 | `Hierarchical Video Frame Sequence Representation with Deep Convolutional Graph Network
 635 | <https://arxiv.org/abs/1906.00377>`_
 636 |     | :authors:`Feng, Mao, Xiang, Wu, Hui, Xue, Rong, Zhang`
 637 |     | :venue:`ECCV 2018`
 638 | 
 639 | `Graph R-cnn for Scene Graph Generation
 640 | <https://arxiv.org/abs/1808.00191>`_
 641 |     | :authors:`Jianwei, Yang, Jiasen, Lu, Stefan, Lee, Dhruv, Batra, Devi, Parikh`
 642 |     | :venue:`ECCV 2018`
 643 | 
 644 | `Exploring Visual Relationship for Image Captioning
 645 | <https://arxiv.org/abs/1809.07041>`_
 646 |     | :authors:`Ting, Yao, Yingwei, Pan, Yehao, Li, Tao, Mei`
 647 |     | :venue:`ECCV 2018`
 648 | 
 649 | EMNLP
 650 | -----
 651 | 
 652 | `Linguistically-informed Self-attention for Semantic Role Labeling
 653 | <https://www.aclweb.org/anthology/D18-1548>`_
 654 |     | :authors:`Emma, Strubell, Patrick, Verga, Daniel, Andor, David, Weiss, Andrew, McCallum`
 655 |     | :venue:`EMNLP 2018`
 656 | 
 657 | `Graph Convolution over Pruned Dependency Trees Improves Relation Extraction
 658 | <https://aclweb.org/anthology/D18-1244>`_
 659 |     | :authors:`Yuhao, Zhang, Peng, Qi, Christopher, D., Manning`
 660 |     | :venue:`EMNLP 2018`
 661 | 
 662 | `Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks
 663 | <https://www.aclweb.org/anthology/D18-1032.pdf>`_
 664 |     | :authors:`Zhichun, Wang, Qingsong, Lv, Xiaohan, Lan, Yu, Zhang`
 665 |     | :venue:`EMNLP 2018`
 666 | 
 667 | ICDM
 668 | ----
 669 | 
 670 | `Meta-graph Based Hin Spectral Embedding: Methods, Analyses, and Insights
 671 | <https://www.semanticscholar.org/paper/Meta-Graph-Based-HIN-Spectral-Embedding%3A-Methods%2C-Yang-Feng/4d5f4d6785d550383e3f3afb04c3015bf0d28405>`_
 672 |     | :authors:`Carl, Yang, Yichen, Feng, Pan, Li, Yu, Shi, Jiawei, Han`
 673 |     | :venue:`ICDM 2018`
 674 | 
 675 | ICLR
 676 | ----
 677 | 
 678 | `Graph Attention Networks
 679 | <https://arxiv.org/pdf/1710.10903>`_
 680 |     | :authors:`Petar, Veličković, Guillem, Cucurull, Arantxa, Casanova, Adriana, Romero, Pietro, Liò, Yoshua, Bengio`
 681 |     | :venue:`ICLR 2018`
 682 | 
 683 | `Fastgcn: Fast Learning with Graph Convolutional Networks via Importance Sampling
 684 | <https://arxiv.org/pdf/1801.10247>`_
 685 |     | :authors:`Jie, Chen, Tengfei, Ma, Cao, Xiao`
 686 |     | :venue:`ICLR 2018`
 687 | 
 688 | `Qanet: Combining Local Convolution with Global Self-attention for Reading Comprehension
 689 | <https://arxiv.org/pdf/1804.09541.pdf>`_
 690 |     | :authors:`Adams, Wei, Yu, David, Dohan, Minh-Thang, Luong, Rui, Zhao, Kai, Chen, Mohammad, Norouzi, Quoc, V., Le`
 691 |     | :venue:`ICLR 2018`
 692 | 
 693 | `A Structured Self-attentive Sentence Embedding
 694 | <https://arxiv.org/pdf/1703.03130.pdf>`_
 695 |     | :authors:`Zhouhan, Lin, Minwei, Feng, Cicero, Nogueira, dos, Santos, Mo, Yu, Bing, Xiang, Bowen, Zhou, Yoshua, Bengio`
 696 |     | :venue:`ICLR 2018`
 697 | 
 698 | `Nervenet: Learning Structured Policy with Graph Neural Networks
 699 | <https://openreview.net/pdf?id=S1sqHMZCb>`_
 700 |     | :authors:`Tingwu, Wang, Renjie, Liao, Jimmy, Ba, Sanja, Fidler`
 701 |     | :venue:`ICLR 2018`
 702 | 
 703 | `Few-shot Learning with Graph Neural Networks
 704 | <https://arxiv.org/abs/1711.04043>`_
 705 |     | :authors:`Victor, Garcia, Joan, Bruna`
 706 |     | :venue:`ICLR 2018`
 707 | 
 708 | ICML
 709 | ----
 710 | 
 711 | `Representation Learning on Graphs with Jumping Knowledge Networks
 712 | <https://arxiv.org/pdf/1806.03536>`_
 713 |     | :authors:`Keyulu, Xu, Chengtao, Li, Yonglong, Tian, Tomohiro, Sonobe, Ken-ichi, Kawarabayashi, Stefanie, Jegelka`
 714 |     | :venue:`ICML 2018`
 715 | 
 716 | `Stochastic Training of Graph Convolutional Networks with Variance Reduction
 717 | <https://arxiv.org/pdf/1710.10568>`_
 718 |     | :authors:`Jianfei, Chen, Jun, Zhu, Le, Song`
 719 |     | :venue:`ICML 2018`
 720 | 
 721 | `Graph Networks As Learnable Physics Engines for Inference and Control
 722 | <https://arxiv.org/pdf/1806.01242.pdf>`_
 723 |     | :authors:`Alvaro, Sanchez-Gonzalez, Nicolas, Heess, Jost, Tobias, Springenberg, Josh, Merel, Martin, Riedmiller`
 724 |     | :venue:`ICML 2018`
 725 | 
 726 | `Learning Policy Representations in Multiagent Systems
 727 | <https://arxiv.org/pdf/1806.06464.pdf>`_
 728 |     | :authors:`Aditya, Grover, Maruan, Al-Shedivat, Jayesh, K., Gupta, Yura, Burda, Harrison, Edwards`
 729 |     | :venue:`ICML 2018`
 730 | 
 731 | `Adversarial Attack on Graph Structured Data
 732 | <https://arxiv.org/abs/1806.02371>`_
 733 |     | :authors:`Hanjun, Dai, Hui, Li, Tian, Tian, Xin, Huang, Lin, Wang, Jun, Zhu, Le, Song`
 734 |     | :venue:`ICML 2018`
 735 | 
 736 | `Learning Steady-states of Iterative Algorithms over Graphs
 737 | <http://proceedings.mlr.press/v80/dai18a.html>`_
 738 |     | :authors:`Hanjun, Dai, Zornitsa, Kozareva, Bo, Dai, Alex, Smola, Le, Song`
 739 |     | :venue:`ICML 2018`
 740 | 
 741 | `Neural Relational Inference for Interacting Systems
 742 | <https://arxiv.org/abs/1802.04687>`_
 743 |     | :authors:`Thomas, Kipf, Ethan, Fetaya, Kuan-Chieh, Wang, Max, Welling, Richard, Zemel`
 744 |     | :venue:`ICML 2018`
 745 | 
 746 | `Graphrnn: Generating Realistic Graphs with Deep Auto-regressive Models
 747 | <https://arxiv.org/abs/1802.08773>`_
 748 |     | :authors:`Jiaxuan, You, Rex, Ying, Xiang, Ren, William, L., Hamilton, Jure, Leskovec`
 749 |     | :venue:`ICML 2018`
 750 | 
 751 | `Netgan: Generating Graphs via Random Walks
 752 | <https://arxiv.org/abs/1803.00816>`_
 753 |     | :authors:`Aleksandar, Bojchevski, Oleksandr, Shchur, Daniel, Zügner, Stephan, Günnemann`
 754 |     | :venue:`ICML 2018`
 755 | 
 756 | `Learning Deep Generative Models of Graphs
 757 | <https://arxiv.org/abs/1803.03324>`_
 758 |     | :authors:`Yujia, Li, Oriol, Vinyals, Chris, Dyer, Razvan, Pascanu, Peter, Battaglia`
 759 |     | :venue:`ICML 2018`
 760 | 
 761 | `Junction Tree Variational Autoencoder for Molecular Graph Generation
 762 | <https://arxiv.org/abs/1802.04364>`_
 763 |     | :authors:`Wengong, Jin, Regina, Barzilay, Tommi, Jaakkola`
 764 |     | :venue:`ICML 2018`
 765 | 
 766 | IJCAI
 767 | -----
 768 | 
 769 | `Anrl: Attributed Network Representation Learning via Deep Neural Networks
 770 | <https://www.ijcai.org/proceedings/2018/438>`_
 771 |     | :authors:`Zhen, Zhang, Hongxia, Yang, Jiajun, Bu, Sheng, Zhou, Pinggang, Yu, Jianwei, Zhang, Martin, Ester, Can, Wang`
 772 |     | :venue:`IJCAI 2018`
 773 | 
 774 | `Efficient Attributed Network Embedding via Recursive Randomized Hashing
 775 | <https://www.ijcai.org/proceedings/2018/397>`_
 776 |     | :authors:`Wei, Wu, Bin, Li, Ling, Chen, Chengqi, Zhang`
 777 |     | :venue:`IJCAI 2018`
 778 | 
 779 | `Deep Attributed Network Embedding
 780 | <https://www.ijcai.org/proceedings/2018/467>`_
 781 |     | :authors:`Hongchang, Gao, Heng, Huang`
 782 |     | :venue:`IJCAI 2018`
 783 | 
 784 | `Dynamic Network Embedding : An Extended Approach for Skip-gram Based Network Embedding
 785 | <https://www.ijcai.org/proceedings/2018/0288.pdf>`_
 786 |     | :authors:`Lun, Du, Yun, Wang, Guojie, Song, Zhicong, Lu, Junshan, Wang`
 787 |     | :venue:`IJCAI 2018`
 788 | 
 789 | KDD
 790 | ---
 791 | 
 792 | `Learning Structural Node Embeddings via Diffusion Wavelets
 793 | <https://arxiv.org/pdf/1710.10321>`_
 794 |     | :authors:`Claire, Donnat, Marinka, Zitnik, David, Hallac, Jure, Leskovec`
 795 |     | :venue:`KDD 2018`
 796 | 
 797 | `Pme: Projected Metric Embedding on Heterogeneous Networks for Link Prediction
 798 | <https://dl.acm.org/citation.cfm?id=3219986>`_
 799 |     | :authors:`Hongxu, Chen, Hongzhi, Yin, Weiqing, Wang, Hao, Wang, Quoc, Viet, Hung, Nguyen, Xue, Li`
 800 |     | :venue:`KDD 2018`
 801 | 
 802 | `Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks
 803 | <https://arxiv.org/pdf/1807.03490>`_
 804 |     | :authors:`Yu, Shi, Qi, Zhu, Fang, Guo, Chao, Zhang, Jiawei, Han`
 805 |     | :venue:`KDD 2018`
 806 | 
 807 | `Dynamic Embeddings for User Profiling in Twitter
 808 | <https://dl.acm.org/citation.cfm?id=3219819.3220043>`_
 809 |     | :authors:`Shangsong, Liang, Xiangliang, Zhang, Zhaochun, Ren, Evangelos, Kanoulas`
 810 |     | :venue:`KDD 2018`
 811 | 
 812 | `Large-scale Learnable Graph Convolutional Networks
 813 | <https://arxiv.org/pdf/1808.03965>`_
 814 |     | :authors:`Hongyang, Gao, Zhengyang, Wang, Shuiwang, Ji`
 815 |     | :venue:`KDD 2018`
 816 | 
 817 | `Graph Convolutional Neural Networks for Web-scale Recommender Systems
 818 | <https://arxiv.org/pdf/1806.01973.pdf>`_
 819 |     | :authors:`Rex, Ying, Ruining, He, Kaifeng, Chen, Pong, Eksombatchai, William, L., Hamilton, Jure, Leskovec`
 820 |     | :venue:`KDD 2018`
 821 |     | :keywords:`P, i, n, S, a, g, e`
 822 | 
 823 | `Graph Convolutional Matrix Completion
 824 | <https://www.kdd.org/kdd2018/files/deep-learning-day/DLDay18_paper_32.pdf>`_
 825 |     | :authors:`Rianne, van, den, Berg, Thomas, N., Kipf, Max, Welling`
 826 |     | :venue:`KDD 2018 Workshop`
 827 | 
 828 | `Deepinf: Social Influence Prediction with Deep Learning
 829 | <https://arxiv.org/pdf/1807.05560.pdf>`_
 830 |     | :authors:`Jiezhong, Qiu, Jian, Tang, Hao, Ma, Yuxiao, Dong, Kuansan, Wang, Jie, Tang`
 831 |     | :venue:`KDD 2018`
 832 | 
 833 | `Adversarial Attacks on Neural Networks for Graph Data
 834 | <https://arxiv.org/abs/1805.07984>`_
 835 |     | :authors:`Daniel, Zügner, Amir, Akbarnejad, Stephan, Günnemann`
 836 |     | :venue:`KDD 2018`
 837 | 
 838 | NAACL
 839 | -----
 840 | 
 841 | `Kbgan: Adversarial Learning for Knowledge Graph Embeddings
 842 | <https://arxiv.org/abs/1711.04071>`_
 843 |     | :authors:`Liwei, Cai, William, Yang, Wang`
 844 |     | :venue:`NAACL 2018`
 845 | 
 846 | `A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network
 847 | <https://aclweb.org/anthology/papers/N/N18/N18-2053/>`_
 848 |     | :authors:`Dai, Quoc, Nguyen, Tu, Dinh, Nguyen, Dat, Quoc, Nguyen, Dinh, Phung`
 849 |     | :venue:`NAACL 2018`
 850 | 
 851 | `Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks
 852 | <https://www.aclweb.org/anthology/N18-2078>`_
 853 |     | :authors:`Diego, Marcheggiani, Joost, Bastings, Ivan, Titov`
 854 |     | :venue:`NAACL 2018`
 855 | 
 856 | NeurIPS
 857 | -------
 858 | 
 859 | `Simple Embedding for Link Prediction in Knowledge Graphs
 860 | <https://arxiv.org/abs/1802.04868>`_
 861 |     | :authors:`Seyed, Mehran, Kazemi, David, Poole`
 862 |     | :venue:`NeurIPS 2018`
 863 | 
 864 | `Adaptive Sampling Towards Fast Graph Representation Learning
 865 | <https://papers.nips.cc/paper/7707-adaptive-sampling-towards-fast-graph-representation-learning.pdf>`_
 866 |     | :authors:`Wenbing, Huang, Tong, Zhang, Yu, Rong, Junzhou, Huang`
 867 |     | :venue:`NeurIPS 2018`
 868 | 
 869 | `Hierarchical Graph Representation Learning with Differentiable Pooling
 870 | <https://arxiv.org/pdf/1806.08804>`_
 871 |     | :authors:`Rex, Ying, Jiaxuan, You, Christopher, Morris, Xiang, Ren, William, L., Hamilton, Jure, Leskovec`
 872 |     | :venue:`NeurIPS 2018`
 873 | 
 874 | `Bayesian Semi-supervised Learning with Graph Gaussian Processes
 875 | <https://papers.nips.cc/paper/7440-bayesian-semi-supervised-learning-with-graph-gaussian-processes.pdf>`_
 876 |     | :authors:`Yin, Cheng, Ng, Nicolò, Colombo, Ricardo, Silva`
 877 |     | :venue:`NeurIPS 2018`
 878 | 
 879 | `Beyond Grids: Learning Graph Representations for Visual Recognition
 880 | <https://papers.nips.cc/paper/8135-beyond-grids-learning-graph-representations-for-visual-recognition>`_
 881 |     | :authors:`Yin, Li, Abhinav, Gupta`
 882 |     | :venue:`NeurIPS 2018`
 883 | 
 884 | `Learning Conditioned Graph Structures for Interpretable Visual Question Answering
 885 | <https://arxiv.org/abs/1806.07243>`_
 886 |     | :authors:`Will, Norcliffe-Brown, Efstathios, Vafeias, Sarah, Parisot`
 887 |     | :venue:`NeurIPS 2018`
 888 | 
 889 | `Linknet: Relational Embedding for Scene Graph
 890 | <https://arxiv.org/abs/1811.06410>`_
 891 |     | :authors:`Sanghyun, Woo, Dahun, Kim, Donghyeon, Cho, In, So, Kweon`
 892 |     | :venue:`NeurIPS 2018`
 893 | 
 894 | `Flexible Neural Representation for Physics Prediction
 895 | <https://arxiv.org/abs/1806.08047>`_
 896 |     | :authors:`Damian, Mrowca, Chengxu, Zhuang, Elias, Wang, Nick, Haber, Li, Fei-Fei, Joshua, B., Tenenbaum, Daniel, L., K., Yamins`
 897 |     | :venue:`NeurIPS 2018`
 898 | 
 899 | `Link Prediction Based on Graph Neural Networks
 900 | <https://papers.nips.cc/paper/7763-link-prediction-based-on-graph-neural-networks.pdf>`_
 901 |     | :authors:`Muhan, Zhang, Yixin, Chen`
 902 |     | :venue:`NeurIPS 2018`
 903 | 
 904 | `Relational Recurrent Neural Networks
 905 | <https://papers.nips.cc/paper/7960-relational-recurrent-neural-networks.pdf>`_
 906 |     | :authors:`Adam, Santoro, Ryan, Faulkner, David, Raposo, Jack, Rae, Mike, Chrzanowski, Théophane, Weber, Daan, Wierstra, Oriol, Vinyals, Razvan, Pascanu, Timothy, Lillicrap`
 907 |     | :venue:`NeurIPS 2018`
 908 | 
 909 | `Transfer of Deep Reactive Policies for Mdp Planning
 910 | <http://www.cse.iitd.ac.in/~mausam/papers/nips18.pdf>`_
 911 |     | :authors:`Aniket, Bajpai, Sankalp, Garg, Mausam`
 912 |     | :venue:`NeurIPS 2018`
 913 | 
 914 | `Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search
 915 | <https://arxiv.org/abs/1810.10659>`_
 916 |     | :authors:`Zhuwen, Li, Qifeng, Chen, Vladlen, Koltun`
 917 |     | :venue:`NeurIPS 2018`
 918 | 
 919 | `Reinforcement Learning for Solving the Vehicle Routing Problem
 920 | <https://arxiv.org/abs/1802.04240>`_
 921 |     | :authors:`Mohammadreza, Nazari, Afshin, Oroojlooy, Lawrence, V., Snyder, Martin, Takáč`
 922 |     | :venue:`NeurIPS 2018`
 923 | 
 924 | `Generative Modeling for Protein Structures
 925 | <https://papers.nips.cc/paper/7978-generative-modeling-for-protein-structures.pdf>`_
 926 |     | :authors:`Namrata, Anand, Po-Ssu, Huang`
 927 |     | :venue:`NeurIPS 2018`
 928 | 
 929 | `Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders
 930 | <https://arxiv.org/abs/1809.02630>`_
 931 |     | :authors:`Tengfei, Ma, Jie, Chen, Cao, Xiao`
 932 |     | :venue:`NeurIPS 2018`
 933 | 
 934 | `Graph Convolutional Policy Network for Goal-directed Molecular Graph Generation
 935 | <https://arxiv.org/abs/1806.02473>`_
 936 |     | :authors:`Jiaxuan, You, Bowen, Liu, Rex, Ying, Vijay, Pande, Jure, Leskovec`
 937 |     | :venue:`NeurIPS 2018`
 938 | 
 939 | `Constrained Graph Variational Autoencoders for Molecule Design
 940 | <https://arxiv.org/abs/1805.09076>`_
 941 |     | :authors:`Qi, Liu, Miltiadis, Allamanis, Marc, Brockschmidt, Alexander, L., Gaunt`
 942 |     | :venue:`NeurIPS 2018`
 943 | 
 944 | SIGIR
 945 | -----
 946 | 
 947 | `Bine: Bipartite Network Embedding
 948 | <https://dl.acm.org/citation.cfm?id=3209978.3209987>`_
 949 |     | :authors:`Ming, Gao, Leihui, Chen, Xiangnan, He, Aoying, Zhou`
 950 |     | :venue:`SIGIR 2018`
 951 | 
 952 | WSDM
 953 | ----
 954 | 
 955 | `Network Embedding As Matrix Factorization: Unifying Deepwalk, Line, Pte, and Node2vec
 956 | <https://arxiv.org/pdf/1710.02971>`_
 957 |     | :authors:`Jiezhong, Qiu, Yuxiao, Dong, Hao, Ma, Jian, Li, Kuansan, Wang, Jie, Tang`
 958 |     | :venue:`WSDM 2018`
 959 | 
 960 | `Exploring Expert Cognition for Attributed Network Embedding
 961 | <https://dl.acm.org/citation.cfm?id=3159655>`_
 962 |     | :authors:`Xiao, Huang, Qingquan, Song, Jundong, Li, Xia, Hu`
 963 |     | :venue:`WSDM 2018`
 964 | 
 965 | `Shine: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction
 966 | <https://arxiv.org/pdf/1712.00732>`_
 967 |     | :authors:`Hongwei, Wang, Fuzheng, Zhang, Min, Hou, Xing, Xie, Minyi, Guo, Qi, Liu`
 968 |     | :venue:`WSDM 2018`
 969 | 
 970 | `Multidimensional Network Embedding with Hierarchical Structures
 971 | <https://dl.acm.org/citation.cfm?id=3159680>`_
 972 |     | :authors:`Yao, Ma, Zhaochun, Ren, Ziheng, Jiang, Jiliang, Tang, Dawei, Yin`
 973 |     | :venue:`WSDM 2018`
 974 | 
 975 | `Curriculum Learning for Heterogeneous Star Network Embedding via Deep Reinforcement Learning
 976 | <https://dl.acm.org/citation.cfm?id=3159711>`_
 977 |     | :authors:`Meng, Qu, Jian, Tang, Jiawei, Han`
 978 |     | :venue:`WSDM 2018`
 979 | 
 980 | WWW
 981 | ---
 982 | 
 983 | `Verse: Versatile Graph Embeddings from Similarity Measures
 984 | <https://arxiv.org/pdf/1803.04742>`_
 985 |     | :authors:`Anton, Tsitsulin, Davide, Mottin, Panagiotis, Karras, Emmanuel, Müller`
 986 |     | :venue:`WWW 2018`
 987 | 
 988 | `Co-regularized Deep Multi-network Embedding
 989 | <https://dl.acm.org/citation.cfm?id=3186113>`_
 990 |     | :authors:`Jingchao, Ni, Shiyu, Chang, Xiao, Liu, Wei, Cheng, Haifeng, Chen, Dongkuan, Xu, Xiang, Zhang`
 991 |     | :venue:`WWW 2018`
 992 | 
 993 | `Side: Representation Learning in Signed Directed Networks
 994 | <https://dl.acm.org/citation.cfm?id=3186117>`_
 995 |     | :authors:`Junghwan, Kim, Haekyu, Park, Ji-Eun, Lee, U, Kang`
 996 |     | :venue:`WWW 2018`
 997 | 
 998 | arXiv
 999 | -----
1000 | 
1001 | `Pitfalls of Graph Neural Network Evaluation
1002 | <https://arxiv.org/pdf/1811.05868>`_
1003 |     | :authors:`Oleksandr, Shchur, Maximilian, Mumme, Aleksandar, Bojchevski, Stephan, Günnemann`
1004 |     | :venue:`arXiv 2018`
1005 | 
1006 | `Molgan: An Implicit Generative Model for Small Molecular Graphs
1007 | <https://arxiv.org/abs/1805.11973>`_
1008 |     | :authors:`Nicola, De, Cao, Thomas, Kipf`
1009 |     | :venue:`arXiv 2018`
1010 | 
1011 | 2019
1012 | ====
1013 | 
1014 | WSDM
1015 | ----
1016 | 
1017 | `A General View for Network Embedding As Matrix Factorization
1018 | <https://dl.acm.org/citation.cfm?id=3291029>`_
1019 |     | :authors:`Xin, Liu, Tsuyoshi, Murata, Kyoung-Sook, Kim, Chatchawan, Kotarasu, Chenyi, Zhuang`
1020 |     | :venue:`WSDM 2019`
1021 | 
1022 | `Session-based Social Recommendation via Dynamic Graph Attention Networks
1023 | <https://arxiv.org/pdf/1902.09362.pdf>`_
1024 |     | :authors:`Weiping, Song, Zhiping, Xiao, Yifan, Wang, Laurent, Charlin, Ming, Zhang, Jian, Tang`
1025 |     | :venue:`WSDM 2019`
1026 |     | :keywords:`Social, recommendation, session-based, GAT`
1027 | 
1028 | ICLR
1029 | ----
1030 | 
1031 | `Deep Graph Infomax
1032 | <https://arxiv.org/pdf/1809.10341>`_
1033 |     | :authors:`Petar, Veličković, William, Fedus, William, L., Hamilton, Pietro, Liò, Yoshua, Bengio, R, Devon, Hjelm`
1034 |     | :venue:`ICLR 2019`
1035 | 
1036 | `Dyrep: Learning Representations over Dynamic Graphs
1037 | <https://openreview.net/pdf?id=HyePrhR5KX>`_
1038 |     | :authors:`Rakshit, Trivedi, Mehrdad, Farajtabar, Prasenjeet, Biswal, Hongyuan, Zha`
1039 |     | :venue:`ICLR 2019`
1040 | 
1041 | `Rotate: Knowledge Graph Embedding by Relational Rotation in Complex Space
1042 | <https://arxiv.org/abs/1902.10197>`_
1043 |     | :authors:`Zhiqing, Sun, Zhi-Hong, Deng, Jian-Yun, Nie, Jian, Tang`
1044 |     | :venue:`ICLR 2019`
1045 | 
1046 | `How Powerful Are Graph Neural Networks?
1047 | <https://arxiv.org/pdf/1810.00826>`_
1048 |     | :authors:`Keyulu, Xu, Weihua, Hu, Jure, Leskovec, Stefanie, Jegelka`
1049 |     | :venue:`ICLR 2019`
1050 | 
1051 | `Lanczosnet: Multi-scale Deep Graph Convolutional Networks
1052 | <https://arxiv.org/pdf/1901.01484>`_
1053 |     | :authors:`Renjie, Liao, Zhizhen, Zhao, Raquel, Urtasun, Richard, S., Zemel`
1054 |     | :venue:`ICLR 2019`
1055 | 
1056 | `Graph Wavelet Neural Network
1057 | <https://arxiv.org/pdf/1904.07785>`_
1058 |     | :authors:`Bingbing, Xu, Huawei, Shen, Qi, Cao, Yunqi, Qiu, Xueqi, Cheng`
1059 |     | :venue:`ICLR 2019`
1060 | 
1061 | `Supervised Community Detection with Line Graph Neural Networks
1062 | <https://openreview.net/pdf?id=H1g0Z3A9Fm>`_
1063 |     | :authors:`Zhengdao, Chen, Xiang, Li, Joan, Bruna`
1064 |     | :venue:`ICLR 2019`
1065 | 
1066 | `Predict Then Propagate: Graph Neural Networks Meet Personalized Pagerank
1067 | <https://arxiv.org/pdf/1810.05997>`_
1068 |     | :authors:`Johannes, Klicpera, Aleksandar, Bojchevski, Stephan, Günnemann`
1069 |     | :venue:`ICLR 2019`
1070 | 
1071 | `Invariant and Equivariant Graph Networks
1072 | <https://arxiv.org/pdf/1812.09902>`_
1073 |     | :authors:`Haggai, Maron, Heli, Ben-Hamu, Nadav, Shamir, Yaron, Lipman`
1074 |     | :venue:`ICLR 2019`
1075 | 
1076 | `Capsule Graph Neural Network
1077 | <https://openreview.net/pdf?id=Byl8BnRcYm>`_
1078 |     | :authors:`Zhang, Xinyi, Lihui, Chen`
1079 |     | :venue:`ICLR 2019`
1080 | 
1081 | `Differentiable Perturb-and-parse: Semi-supervised Parsing with a Structured Variational Autoencoder
1082 | <https://openreview.net/pdf?id=BJlgNh0qKQ>`_
1083 |     | :authors:`Caio, Corro, Ivan, Titov`
1084 |     | :venue:`ICLR 2019`
1085 | 
1086 | `Structured Neural Summarization
1087 | <https://arxiv.org/pdf/1811.01824.pdf>`_
1088 |     | :authors:`Patrick, Fernandes, Miltiadis, Allamanis, Marc, Brockschmid`
1089 |     | :venue:`ICLR 2019`
1090 | 
1091 | `Learning Localized Generative Models for 3d Point Clouds via Graph Convolution
1092 | <https://openreview.net/forum?id=SJeXSo09FQ>`_
1093 |     | :authors:`Diego, Valsesia, Giulia, Fracastoro, Enrico, Magli`
1094 |     | :venue:`ICLR 2019`
1095 | 
1096 | `Graph Hypernetworks for Neural Architecture Search
1097 | <https://openreview.net/pdf?id=rkgW0oA9FX>`_
1098 |     | :authors:`Chris, Zhang, Mengye, Ren, Raquel, Urtasun`
1099 |     | :venue:`ICLR 2019`
1100 | 
1101 | `Neural Graph Evolution: Towards Efficient Automatic Robot Design
1102 | <https://openreview.net/pdf?id=BkgWHnR5tm>`_
1103 |     | :authors:`Tingwu, Wang, Yuhao, Zhou, Sanja, Fidler, Jimmy, Ba`
1104 |     | :venue:`ICLR 2019`
1105 | 
1106 | `Attention, Learn to Solve Routing Problems!
1107 | <https://arxiv.org/abs/1803.08475>`_
1108 |     | :authors:`Wouter, Kool, Herke, van, Hoof, Max, Welling`
1109 |     | :venue:`ICLR 2019`
1110 | 
1111 | `Learning a Sat Solver from Single-bit Supervision
1112 | <https://arxiv.org/abs/1802.03685>`_
1113 |     | :authors:`Daniel, Selsam, Matthew, Lamm, Benedikt, Bünz, Percy, Liang, Leonardo, de, Moura, David, L., Dill`
1114 |     | :venue:`ICLR 2019`
1115 | 
1116 | `Adversarial Attacks on Graph Neural Networks via Meta Learning
1117 | <https://arxiv.org/abs/1902.08412>`_
1118 |     | :authors:`Daniel, Zügner, Stephan, Günnemann`
1119 |     | :venue:`ICLR 2019`
1120 | 
1121 | `Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning
1122 | <https://arxiv.org/abs/1805.10002>`_
1123 |     | :authors:`Yanbin, Liu, Juho, Lee, Minseop, Park, Saehoon, Kim, Eunho, Yang, Sung, Ju, Hwang, Yi, Yang`
1124 |     | :venue:`ICLR 2019`
1125 | 
1126 | `Learning Multimodal Graph-to-graph Translation for Molecule Optimization
1127 | <https://arxiv.org/abs/1812.01070>`_
1128 |     | :authors:`Wengong, Jin, Kevin, Yang, Regina, Barzilay, Tommi, Jaakkola`
1129 |     | :venue:`ICLR 2019`
1130 | 
1131 | `Generative Code Modeling with Graphs
1132 | <https://openreview.net/forum?id=Bke4KsA5FX>`_
1133 |     | :authors:`Marc, Brockschmidt, Miltiadis, Allamanis, Alexander, L., Gaunt, Oleksandr, Polozov`
1134 |     | :venue:`ICLR 2019`
1135 | 
1136 | `Graphtsne: A Visualization Technique for Graph-structured Data
1137 | <https://arxiv.org/pdf/1904.06915.pdf>`_
1138 |     | :authors:`Yao, Yang, Leow, Thomas, Laurent, Xavier, Bresson`
1139 |     | :venue:`ICLR 2019 Workshop`
1140 | 
1141 | WWW
1142 | ---
1143 | 
1144 | `Netsmf: Large-scale Network Embedding As Sparse Matrix Factorization
1145 | <http://keg.cs.tsinghua.edu.cn/jietang/publications/www19-Qiu-et-al-NetSMF-Large-Scale-Network-Embedding.pdf>`_
1146 |     | :authors:`Jiezhong, Qiu, Yuxiao, Dong, Hao, Ma, Jian, Li, Chi, Wang, Kuansan, Wang, Jie, Tang`
1147 |     | :venue:`WWW 2019`
1148 | 
1149 | `Adversarial Training Methods for Network Embedding
1150 | <https://dl.acm.org/citation.cfm?id=3313445>`_
1151 |     | :authors:`Quanyu, Dai, Xiao, Shen, Liang, Zhang, Qiang, Li, Dan, Wang`
1152 |     | :venue:`WWW 2019`
1153 | 
1154 | `Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning
1155 | <https://arxiv.org/abs/1903.08948>`_
1156 |     | :authors:`Wen, Zhang, Bibek, Paudel, Liang, Wang, Jiaoyan, Chen, Hai, Zhu, Wei, Zhang, Abraham, Bernstein, Huajun, Chen`
1157 |     | :venue:`WWW 2019`
1158 | 
1159 | `Heterogeneous Graph Attention Network
1160 | <https://arxiv.org/pdf/1903.07293>`_
1161 |     | :authors:`Xiao, Wang, Houye, Ji, Chuan, Shi, Bai, Wang, Peng, Cui, P., Yu, Yanfang, Ye`
1162 |     | :venue:`WWW 2019`
1163 | 
1164 | `Learning Graph Pooling and Hybrid Convolutional Operations for Text Representations
1165 | <https://arxiv.org/pdf/1901.06965.pdf>`_
1166 |     | :authors:`Hongyang, Gao, Yongjun, Chen, Shuiwang, Ji`
1167 |     | :venue:`WWW 2019`
1168 | 
1169 | `Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in
1170 | <https://arxiv.org/pdf/1903.10433.pdf>`_
1171 |     | :authors:`Qitian, Wu, Hengrui, Zhang, Xiaofeng, Gao, Peng, He, Paul, Weng, Han, Gao, Guihai, Chen`
1172 |     | :venue:`WWW 2019`
1173 |     | :keywords:`Social, recommendation, GAT`
1174 | 
1175 | `Graph Neural Networks for Social Recommendation
1176 | <https://arxiv.org/pdf/1902.07243.pdf>`_
1177 |     | :authors:`Wenqi, Fan, Yao, Ma, Qing, Li, Yuan, He, Eric, Zhao, Jiliang, Tang, Dawei, Yin`
1178 |     | :venue:`WWW 2019`
1179 |     | :keywords:`Social, recommendation, GNN`
1180 | 
1181 | `Graphvite: A High-performance Cpu-gpu Hybrid System for Node Embedding
1182 | <https://arxiv.org/pdf/1903.00757>`_
1183 |     | :authors:`Zhaocheng, Zhu, Shizhen, Xu, Meng, Qu, Jian, Tang`
1184 |     | :venue:`WWW 2019`
1185 | 
1186 | AAAI
1187 | ----
1188 | 
1189 | `Bayesian Graph Convolutional Neural Networks for Semi-supervised Classification
1190 | <https://arxiv.org/pdf/1811.11103.pdf>`_
1191 |     | :authors:`Yingxue, Zhang, Soumyasundar, Pal, Mark, Coates, Deniz, Üstebay`
1192 |     | :venue:`AAAI 2019`
1193 | 
1194 | `Graph Convolutional Networks for Text Classification
1195 | <https://arxiv.org/pdf/1809.05679.pdf>`_
1196 |     | :authors:`Liang, Yao, Chengsheng, Mao, Yuan, Luo`
1197 |     | :venue:`AAAI 2019`
1198 | 
1199 | `Multi-task Learning over Graph Structures
1200 | <https://arxiv.org/pdf/1811.10211.pdf>`_
1201 |     | :authors:`Pengfei, Liu, Jie, Fu, Yue, Dong, Xipeng, Qiu, Jackie, Chi, Kit, Cheung`
1202 |     | :venue:`AAAI 2019`
1203 | 
1204 | `Session-based Recommendation with Graph Neural Networks
1205 | <https://arxiv.org/pdf/1811.00855.pdf>`_
1206 |     | :authors:`Shu, Wu, Yuyuan, Tang, Yanqiao, Zhu, Liang, Wang, Xing, Xie, Tieniu, Tan`
1207 |     | :venue:`AAAI 2019`
1208 |     | :keywords:`Session-based, recommendation, GNN`
1209 | 
1210 | `Atomic: an Atlas of Machine Commonsense for If-then Reasoning
1211 | <https://wvvw.aaai.org/ojs/index.php/AAAI/article/download/4160/4038>`_
1212 |     | :authors:`Maarten, Sap, Ronan, Le, Bras, Emily, Allaway, Chandra, Bhagavatula, Nicholas, Lourie, Hannah, Rashkin, Brendan, Roof, Noah, A., Smith, Yejin, Choi`
1213 |     | :venue:`AAAI 2019`
1214 | 
1215 | ICML
1216 | ----
1217 | 
1218 | `Mixhop: Higher-order Graph Convolutional Architectures via Sparsified Neighborhood Mixing
1219 | <https://arxiv.org/pdf/1905.00067>`_
1220 |     | :authors:`Sami, Abu-El-Haija, Bryan, Perozzi, Amol, Kapoor, Nazanin, Alipourfard, Kristina, Lerman, Hrayr, Harutyunyan, Greg, Ver, Steeg, Aram, Galstyan`
1221 |     | :venue:`ICML 2019`
1222 | 
1223 | `Graph U-nets
1224 | <https://arxiv.org/pdf/1905.05178>`_
1225 |     | :authors:`Hongyang, Gao, Shuiwang, Ji`
1226 |     | :venue:`ICML 2019`
1227 | 
1228 | `Disentangled Graph Convolutional Networks
1229 | <http://proceedings.mlr.press/v97/ma19a/ma19a.pdf>`_
1230 |     | :authors:`Jianxin, Ma, Peng, Cui, Kun, Kuang, Xin, Wang, Wenwu, Zhu`
1231 |     | :venue:`ICML 2019`
1232 | 
1233 | `Gmnn: Graph Markov Neural Networks
1234 | <https://arxiv.org/pdf/1905.06214>`_
1235 |     | :authors:`Meng, Qu, Yoshua, Bengio, Jian, Tang`
1236 |     | :venue:`ICML 2019`
1237 | 
1238 | `Simplifying Graph Convolutional Networks
1239 | <https://arxiv.org/pdf/1902.07153>`_
1240 |     | :authors:`Felix, Wu, Tianyi, Zhang, Amauri, Holanda, de, Souza, Jr., Christopher, Fifty, Tao, Yu, Kilian, Q., Weinberger`
1241 |     | :venue:`ICML 2019`
1242 | 
1243 | `Position-aware Graph Neural Networks
1244 | <https://arxiv.org/pdf/1906.04817>`_
1245 |     | :authors:`Jiaxuan, You, Rex, Ying, Jure, Leskovec`
1246 |     | :venue:`ICML 2019`
1247 | 
1248 | `Self-attention Graph Pooling
1249 | <https://arxiv.org/pdf/1904.08082>`_
1250 |     | :authors:`Junhyun, Lee, Inyeop, Lee, Jaewoo, Kang`
1251 |     | :venue:`ICML 2019`
1252 | 
1253 | `Relational Pooling for Graph Representations
1254 | <https://arxiv.org/pdf/1903.02541>`_
1255 |     | :authors:`Ryan, L., Murphy, Balasubramaniam, Srinivasan, Vinayak, Rao, Bruno, Ribeiro`
1256 |     | :venue:`ICML 2019`
1257 | 
1258 | `Graph Learning Network: A Structure Learning Algorithm
1259 | <https://arxiv.org/abs/1905.12665>`_
1260 |     | :authors:`Darwin, Saire, Pilco, Adín, Ramírez, Rivera`
1261 |     | :venue:`ICML 2019 Workshop`
1262 | 
1263 | `Learning Discrete Structures for Graph Neural Networks
1264 | <https://arxiv.org/abs/1903.11960>`_
1265 |     | :authors:`Luca, Franceschi, Mathias, Niepert, Massimiliano, Pontil, Xiao, He`
1266 |     | :venue:`ICML 2019`
1267 | 
1268 | `Graphite: Iterative Generative Modeling of Graphs
1269 | <https://arxiv.org/abs/1803.10459>`_
1270 |     | :authors:`Aditya, Grover, Aaron, Zweig, Stefano, Ermon`
1271 |     | :venue:`ICML 2019`
1272 | 
1273 | `Drug-drug Adverse Effect Prediction with Graph Co-attention
1274 | <https://arxiv.org/pdf/1905.00534.pdf>`_
1275 |     | :authors:`Andreea, Deac, Yu-Hsiang, Huang, Petar, Veličković, Pietro, Liò, Jian, Tang`
1276 |     | :venue:`ICML 2019 Workshop`
1277 | 
1278 | `Dag-gnn: Dag Structure Learning with Graph Neural Networks
1279 | <https://arxiv.org/abs/1904.10098>`_
1280 |     | :authors:`Yue, Yu, Jie, Chen, Tian, Gao, Mo, Yu`
1281 |     | :venue:`ICML 2019`
1282 | 
1283 | arXiv
1284 | -----
1285 | 
1286 | `Continuous Graph Neural Networks
1287 | <https://arxiv.org/pdf/1912.00967.pdf>`_
1288 |     | :authors:`Louis-Pascal, A., C., Xhonneux, Meng, Qu, Jian, Tang`
1289 |     | :venue:`arXiv 2019`
1290 | 
1291 | `An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem
1292 | <https://arxiv.org/abs/1906.01227>`_
1293 |     | :authors:`Chaitanya, K., Joshi, Thomas, Laurent, Xavier, Bresson`
1294 |     | :venue:`arXiv 2019`
1295 | 
1296 | `Brain Signal Classification via Learning Connectivity Structure
1297 | <https://arxiv.org/abs/1905.11678>`_
1298 |     | :authors:`Soobeom, Jang, Seong-Eun, Moon, Jong-Seok, Lee`
1299 |     | :venue:`arXiv 2019`
1300 | 
1301 | `Joint Embedding of Structure and Features via Graph Convolutional Networks
1302 | <https://arxiv.org/abs/1905.08636>`_
1303 |     | :authors:`Sébastien, Lerique, Jacob, Levy, Abitbol, Márton, Karsai`
1304 |     | :venue:`arXiv 2019`
1305 | 
1306 | `Variational Spectral Graph Convolutional Networks
1307 | <https://arxiv.org/abs/1906.01852>`_
1308 |     | :authors:`Louis, Tiao, Pantelis, Elinas, Harrison, Nguyen, Edwin, V., Bonilla`
1309 |     | :venue:`arXiv 2019`
1310 | 
1311 | `Selfies: a Robust Representation of Semantically Constrained Graphs with an Example Application in Chemistry
1312 | <https://arxiv.org/pdf/1905.13741.pdf>`_
1313 |     | :authors:`Mario, Krenn, Florian, Häse, AkshatKumar, Nigam, Pascal, Friederich, Alán, Aspuru-Guzik`
1314 |     | :venue:`arXiv 2019`
1315 | 
1316 | `Detecting Drug-drug Interactions Using Artificial Neural Networks and Classic Graph Similarity Measures
1317 | <https://arxiv.org/pdf/1903.04571.pdf>`_
1318 |     | :authors:`Guy, Shtar, Lior, Rokach, Bracha, Shapira`
1319 |     | :venue:`arXiv 2019`
1320 | 
1321 | NAACL
1322 | -----
1323 | 
1324 | `Imposing Label-relational Inductive Bias for Extremely Fine-grained Entity Typing
1325 | <https://arxiv.org/pdf/1903.02591.pdf>`_
1326 |     | :authors:`Wenhan, Xiong, Jiawei, Wu, Deren, Lei, Mo, Yu, Shiyu, Chang, Xiaoxiao, Guo, William, Yang, Wang`
1327 |     | :venue:`NAACL 2019`
1328 | 
1329 | `Single Document Summarization As Tree Induction
1330 | <https://www.aclweb.org/anthology/N19-1173>`_
1331 |     | :authors:`Yang, Liu, Ivan, Titov, Mirella, Lapata`
1332 |     | :venue:`NAACL 2019`
1333 | 
1334 | `Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks
1335 | <https://arxiv.org/pdf/1903.01306.pdf>`_
1336 |     | :authors:`Ningyu, Zhang, Shumin, Deng, Zhanlin, Sun, Guanying, Wang, Xi, Chen, Wei, Zhang, Huajun, Chen`
1337 |     | :venue:`NAACL 2019`
1338 | 
1339 | CVPR
1340 | ----
1341 | 
1342 | `Graph-based Global Reasoning Networks
1343 | <https://arxiv.org/abs/1811.12814>`_
1344 |     | :authors:`Yunpeng, Chen, Marcus, Rohrbach, Zhicheng, Yan, Shuicheng, Yan, Jiashi, Feng, Yannis, Kalantidis`
1345 |     | :venue:`CVPR 2019`
1346 | 
1347 | `Deep Graph Laplacian Regularization for Robust Denoising of Real Images
1348 | <https://arxiv.org/abs/1807.11637>`_
1349 |     | :authors:`Jin, Zeng, Jiahao, Pang, Wenxiu, Sun, Gene, Cheung`
1350 |     | :venue:`CVPR 2019`
1351 | 
1352 | `Learning Context Graph for Person Search
1353 | <https://arxiv.org/abs/1904.01830>`_
1354 |     | :authors:`Yichao, Yan, Qiang, Zhang, Bingbing, Ni, Wendong, Zhang, Minghao, Xu, Xiaokang, Yang`
1355 |     | :venue:`CVPR 2019`
1356 | 
1357 | `Graphonomy: Universal Human Parsing via Graph Transfer Learning
1358 | <https://arxiv.org/abs/1904.04536>`_
1359 |     | :authors:`Ke, Gong, Yiming, Gao, Xiaodan, Liang, Xiaohui, Shen, Meng, Wang, Liang, Lin`
1360 |     | :venue:`CVPR 2019`
1361 | 
1362 | `Masked Graph Attention Network for Person Re-identification
1363 | <http://openaccess.thecvf.com/content_CVPRW_2019/papers/TRMTMCT/Bao_Masked_Graph_Attention_Network_for_Person_Re-Identification_CVPRW_2019_paper.pdf>`_
1364 |     | :authors:`Liqiang, Bao, Bingpeng, Ma, Hong, Chang, Xilin, Chen`
1365 |     | :venue:`CVPR 2019`
1366 | 
1367 | `Learning to Cluster Faces on an Affinity Graph
1368 | <https://arxiv.org/abs/1904.02749>`_
1369 |     | :authors:`Lei, Yang, Xiaohang, Zhan, Dapeng, Chen, Junjie, Yan, Chen, Change, Loy, Dahua, Lin`
1370 |     | :venue:`CVPR 2019`
1371 | 
1372 | `Actional-structural Graph Convolutional Networks for Skeleton-based Action Recognition
1373 | <https://arxiv.org/abs/1904.12659>`_
1374 |     | :authors:`Maosen, Li, Siheng, Chen, Xu, Chen, Ya, Zhang, Yanfeng, Wang, Qi, Tian`
1375 |     | :venue:`CVPR 2019`
1376 | 
1377 | `Adaptively Connected Neural Networks
1378 | <https://arxiv.org/abs/1904.03579>`_
1379 |     | :authors:`Guangrun, Wang, Keze, Wang, Liang, Lin`
1380 |     | :venue:`CVPR 2019`
1381 | 
1382 | `Reasoning Visual Dialogs with Structural and Partial Observations
1383 | <https://arxiv.org/abs/1904.03579>`_
1384 |     | :authors:`Zilong, Zheng, Wenguan, Wang, Siyuan, Qi, Song-Chun, Zhu`
1385 |     | :venue:`CVPR 2019`
1386 | 
1387 | SIGGRAPH
1388 | --------
1389 | 
1390 | `Meshcnn: A Network with an Edge
1391 | <https://arxiv.org/pdf/1809.05910.pdf>`_
1392 |     | :authors:`Rana, Hanocka, Amir, Hertz, Noa, Fish, Raja, Giryes, Shachar, Fleishman, Daniel, Cohen-Or`
1393 |     | :venue:`SIGGRAPH 2019`
1394 |     | :keywords:`h, t, t, p, s, :, /, /, r, a, n, a, h, a, n, o, c, k, a, ., g, i, t, h, u, b, ., i, o, /, M, e, s, h, C, N, N, /`
1395 | 
1396 | SIGIR
1397 | -----
1398 | 
1399 | `A Neural Influence Diffusion Model for Social Recommendation
1400 | <https://arxiv.org/pdf/1904.10322.pdf>`_
1401 |     | :authors:`Le, Wu, Peijie, Sun, Yanjie, Fu, Richang, Hong, Xiting, Wang, Meng, Wang`
1402 |     | :venue:`SIGIR 2019`
1403 |     | :keywords:`Social, Recommendation, diffusion`
1404 | 
1405 | `Neural Graph Collaborative Filtering
1406 | <https://arxiv.org/pdf/1905.08108.pdf>`_
1407 |     | :authors:`Xiang, Wang, Xiangnan, He, Meng, Wang, Fuli, Feng, Tat-Seng, Chua`
1408 |     | :venue:`SIGIR 2019`
1409 |     | :keywords:`Collaborative, Filtering, GNN`
1410 | 
1411 | IJCAI
1412 | -----
1413 | 
1414 | `Binarized Collaborative Filtering with Distilling Graph Convolutional Networks
1415 | <https://arxiv.org/pdf/1906.01829.pdf>`_
1416 |     | :authors:`Haoyu, Wang, Defu, Lian, Yong, Ge`
1417 |     | :venue:`IJCAI 2019`
1418 | 
1419 | bioRxiv
1420 | -------
1421 | 
1422 | `Pgcn: Disease Gene Prioritization by Disease and Gene Embedding through Graph Convolutional Neural Networks
1423 | <https://www.biorxiv.org/content/biorxiv/early/2019/01/28/532226.full.pdf>`_
1424 |     | :authors:`Yu, Li, Hiroyuki, Kuwahara, Peng, Yang, Le, Song, Xin, Gao`
1425 |     | :venue:`bioRxiv 2019`
1426 | 
1427 | ICSC
1428 | ----
1429 | 
1430 | `Identifying Protein-protein Interaction Using Tree Lstm and Structured Attention
1431 | <https://ieeexplore.ieee.org/abstract/document/8665584>`_
1432 |     | :authors:`Mahtab, Ahmed, Jumayel, Islam, Muhammad, Rifayat, Samee, Robert, E., Mercer`
1433 |     | :venue:`ICSC 2019`
1434 | 
1435 | Nature
1436 | ------
1437 | 
1438 | `Towards Perturbation Prediction of Biological Networks Using Deep Learning
1439 | <https://www.nature.com/articles/s41598-019-48391-y>`_
1440 |     | :authors:`Diya, Li, Jianxi, Gao`
1441 |     | :venue:`Nature 2019`
1442 | 
1443 | AISTATS
1444 | -------
1445 | 
1446 | `Graph to Graph: a Topology Aware Approach for Graph Structures Learning and Generation
1447 | <http://proceedings.mlr.press/v89/sun19c.html>`_
1448 |     | :authors:`Mingming, Sun, Ping, Li`
1449 |     | :venue:`AISTATS 2019`
1450 | 
1451 | SysML
1452 | -----
1453 | 
1454 | `Pytorch-biggraph: A Large-scale Graph Embedding System
1455 | <https://arxiv.org/pdf/1903.12287>`_
1456 |     | :authors:`Adam, Lerer, Ledell, Wu, Jiajun, Shen, Timothee, Lacroix, Luca, Wehrstedt, Abhijit, Bose, Alex, Peysakhovich`
1457 |     | :venue:`SysML 2019`
1458 | 
1459 | VLDB
1460 | ----
1461 | 
1462 | `Aligraph: A Comprehensive Graph Neural Network Platform
1463 | <https://arxiv.org/pdf/1902.08730>`_
1464 |     | :authors:`Rong, Zhu, Kun, Zhao, Hongxia, Yang, Wei, Lin, Chang, Zhou, Baole, Ai, Yong, Li, Jingren, Zhou`
1465 |     | :venue:`VLDB 2019`
1466 | 
1467 | ACL
1468 | ---
1469 | 
1470 | `Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs
1471 | <https://arxiv.org/abs/1906.01195>`_
1472 |     | :authors:`Deepak, Nathani, Jatin, Chauhan, Charu, Sharma, Manohar, Kaul`
1473 |     | :venue:`ACL 2019`
1474 | 
1475 | `Graph Neural Networks with Generated Parameters for Relation Extraction
1476 | <https://arxiv.org/pdf/1902.00756.pdf>`_
1477 |     | :authors:`Hao, Zhu, Yankai, Lin, Zhiyuan, Liu, Jie, Fu, Tat-seng, Chua, Maosong, Sun`
1478 |     | :venue:`ACL 2019`
1479 | 
1480 | `Dynamically Fused Graph Network for Multi-hop Reasoning
1481 | <https://arxiv.org/pdf/1905.06933.pdf>`_
1482 |     | :authors:`Yunxuan, Xiao, Yanru, Qu, Lin, Qiu, Hao, Zhou, Lei, Li, Weinan, Zhang, Yong, Yu`
1483 |     | :venue:`ACL 2019`
1484 | 
1485 | `Encoding Social Information with Graph Convolutional Networks for Political Perspective Detection
1486 | <https://www.cs.purdue.edu/homes/dgoldwas//downloads/papers/LiG_acl_2019.pdf>`_
1487 |     | :authors:`Chang, Li, Dan, Goldwasser`
1488 |     | :venue:`ACL 2019`
1489 | 
1490 | `Attention Guided Graph Convolutional Networks for Relation Extraction
1491 | <https://arxiv.org/pdf/1906.07510.pdf>`_
1492 |     | :authors:`Zhijiang, Guo, Yan, Zhang, Wei, Lu`
1493 |     | :venue:`ACL 2019`
1494 | 
1495 | `Incorporating Syntactic and Semantic Information in Word Embeddings Using Graph Convolutional Networks
1496 | <https://arxiv.org/pdf/1809.04283.pdf>`_
1497 |     | :authors:`Shikhar, Vashishth, Manik, Bhandari, Prateek, Yadav, Piyush, Rai, Chiranjib, Bhattacharyya, Partha, Talukdar`
1498 |     | :venue:`ACL 2019`
1499 | 
1500 | `Graphrel: Modeling Text As Relational Graphs for Joint Entity and Relation Extraction
1501 | <https://tsujuifu.github.io/pubs/acl19_graph-rel.pdf>`_
1502 |     | :authors:`Tsu-Jui, Fu, Peng-Hsuan, Li, Wei-Yun, Ma`
1503 |     | :venue:`ACL 2019`
1504 | 
1505 | `Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs
1506 | <https://arxiv.org/pdf/1905.07374.pdf>`_
1507 |     | :authors:`Ming, Tu, Guangtao, Wang, Jing, Huang, Yun, Tang, Xiaodong, He, Bowen, Zhou`
1508 |     | :venue:`ACL 2019`
1509 | 
1510 | `Cognitive Graph for Multi-hop Reading Comprehension at Scale
1511 | <https://arxiv.org/pdf/1905.05460.pdf>`_
1512 |     | :authors:`Ming, Ding, Chang, Zhou, Qibin, Chen, Hongxia, Yang, Jie, Tang`
1513 |     | :venue:`ACL 2019`
1514 | 
1515 | `Coherent Comment Generation for Chinese Articles with a Graph-to-sequence Model
1516 | <https://arxiv.org/pdf/1906.01231.pdf>`_
1517 |     | :authors:`Wei, Li, Jingjing, Xu, Yancheng, He, Shengli, Yan, Yunfang, Wu, Xu, Sun`
1518 |     | :venue:`ACL 2019`
1519 | 
1520 | `Matching Article Pairs with Graphical Decomposition and Convolutions
1521 | <https://arxiv.org/pdf/1802.07459.pdf>`_
1522 |     | :authors:`Bang, Liu, Di, Niu, Haojie, Wei, Jinghong, Lin, Yancheng, He, Kunfeng, Lai, Yu, Xu`
1523 |     | :venue:`ACL 2019`
1524 | 
1525 | `Embedding Imputation with Grounded Language Information
1526 | <https://arxiv.org/pdf/1906.03753.pdf>`_
1527 |     | :authors:`Ziyi, Yang, Chenguang, Zhu, Vin, Sachidananda, Eric, Darve`
1528 |     | :venue:`ACL 2019`
1529 | 
1530 | `Encoding Social Information with Graph Convolutional Networks Forpolitical Perspective Detection in News Media
1531 | <https://www.aclweb.org/anthology/P19-1247.pdf>`_
1532 |     | :authors:`Chang, Li, Dan, Goldwasser`
1533 |     | :venue:`ACL 2019`
1534 | 
1535 | `A Neural Multi-digraph Model for Chinese Ner with Gazetteers
1536 | <https://www.aclweb.org/anthology/P19-1141.pdf>`_
1537 |     | :authors:`Ruixue, Ding, Pengjun, Xie, Xiaoyan, Zhang, Wei, Lu, Linlin, Li, Luo, Si`
1538 |     | :venue:`ACL 2019`
1539 | 
1540 | `Tree Communication Models for Sentiment Analysis
1541 | <https://www.aclweb.org/anthology/P19-1342.pdf>`_
1542 |     | :authors:`Yuan, Zhang, Yue, Zhang`
1543 |     | :venue:`ACL 2019`
1544 | 
1545 | `A2n: Attending to Neighbors for Knowledge Graph Inference
1546 | <https://www.aclweb.org/anthology/P19-1431.pdf>`_
1547 |     | :authors:`Trapit, Bansal, Da-Cheng, Juan, Sujith, Ravi, Andrew, McCallum`
1548 |     | :venue:`ACL 2019`
1549 | 
1550 | `Textbook Question Answering with Multi-modal Context Graph Understanding and Self-supervised Open-set Comprehension
1551 | <https://www.aclweb.org/anthology/P19-1347.pdf>`_
1552 |     | :authors:`Daesik, Kim, Seonhoon, Kim, Nojun, Kwak`
1553 |     | :venue:`ACL 2019`
1554 | 
1555 | `Look Again at the Syntax: Relational Graph Convolutional Network for Gendered Ambiguous Pronoun Resolution
1556 | <https://arxiv.org/pdf/1905.08868.pdf>`_
1557 |     | :authors:`Yinchuan, Xu, Junlin, Yang`
1558 |     | :venue:`ACL 2019 Workshop`
1559 |     | :keywords:`h, t, t, p, s, :, /, /, g, i, t, h, u, b, ., c, o, m, /, i, a, n, y, c, x, u, /, R, G, C, N, -, w, i, t, h, -, B, E, R, T`
1560 | 
1561 | KDD
1562 | ---
1563 | 
1564 | `Progan: Network Embedding via Proximity Generative Adversarial Network
1565 | <https://dl.acm.org/citation.cfm?id=3330866>`_
1566 |     | :authors:`Hongchang, Gao, Jian, Pei, Heng, Huang`
1567 |     | :venue:`KDD 2019`
1568 | 
1569 | `Learning Network-to-network Model for Content-rich Network Embedding
1570 | <https://dl.acm.org/citation.cfm?id=3330924>`_
1571 |     | :authors:`	Zhicheng, He, Jie, Liu, Na, Li, Yalou, Huang`
1572 |     | :venue:`KDD 2019`
1573 | 
1574 | `Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks
1575 | <https://cs.stanford.edu/~srijan/pubs/jodie-kdd2019.pdf>`_
1576 |     | :authors:`Srijan, Kumar, Xikun, Zhang, Jure, Leskovec`
1577 |     | :venue:`KDD 2019`
1578 | 
1579 | `Graph Representation Learning via Hard and Channel-wise Attention Networks
1580 | <https://arxiv.org/pdf/1907.04652.pdf>`_
1581 |     | :authors:`Hongyang, Gao, Shuiwang, Ji`
1582 |     | :venue:`KDD 2019`
1583 | 
1584 | `Conditional Random Field Enhanced Graph Convolutional Neural Networks
1585 | <https://www.kdd.org/kdd2019/accepted-papers/view/conditional-random-field-enhanced-graph-convolutional-neural-networks>`_
1586 |     | :authors:`Hongchang, Gao, Jian, Pei, Heng, Huang`
1587 |     | :venue:`KDD 2019`
1588 | 
1589 | `Cluster-gcn: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
1590 | <https://arxiv.org/abs/1905.07953>`_
1591 |     | :authors:`Wei-Lin, Chiang, Xuanqing, Liu, Si, Si, Yang, Li, Samy, Bengio, Cho-Jui, Hsieh`
1592 |     | :venue:`KDD 2019`
1593 | 
1594 | `Demo-net: Degree-specific Graph Neural Networks for Node and Graph Classification
1595 | <https://arxiv.org/abs/1906.02319>`_
1596 |     | :authors:`Jun, Wu, Jingrui, He, Jiejun, Xu`
1597 |     | :venue:`KDD 2019`
1598 | 
1599 | `Hetgnn: Heterogeneous Graph Neural Network
1600 | <https://www.kdd.org/kdd2019/accepted-papers/view/hetgnn-heterogeneous-graph-neural-network>`_
1601 |     | :authors:`Chuxu, Zhang, Dongjin, Song, Chao, Huang, Ananthram, Swami, Nitesh, V., Chawla`
1602 |     | :venue:`KDD 2019`
1603 | 
1604 | `Graph Recurrent Networks with Attributed Random Walks
1605 | <https://dl.acm.org/citation.cfm?id=3292500.3330941>`_
1606 |     | :authors:`Xiao, Huang, Qingquan, Song, Yuening, Li, Xia, Hu`
1607 |     | :venue:`KDD 2019`
1608 | 
1609 | `Graph Convolutional Networks with Eigenpooling
1610 | <https://arxiv.org/abs/1904.13107>`_
1611 |     | :authors:`Yao, Ma, Suhang, Wang, Charu, Aggarwal, Jiliang, Tang`
1612 |     | :venue:`KDD 2019`
1613 | 
1614 | `Intentgc: A Scalable Graph Convolution Framework Fusing Heterogeneous Information for Recommendation
1615 | <https://dl.acm.org/citation.cfm?id=3330686>`_
1616 |     | :authors:`Jun, Zhao, Zhou, Zhou, Ziyu, Guan, Wei, Zhao, Wei, Ning, Guang, Qiu, Xiaofei, He`
1617 |     | :venue:`KDD 2019`
1618 | 
1619 | `An End-to-end Neighborhood-based Interaction Model for Knowledge-enhanced Recommendation
1620 | <https://arxiv.org/pdf/1908.04032.pdf>`_
1621 |     | :authors:`Yanru, Qu, Ting, Bai, Weinan, Zhang, Jianyun, Nie, Jian, Tang`
1622 |     | :venue:`KDD 2019 Workshop`
1623 | 
1624 | `Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks
1625 | <https://arxiv.org/pdf/1905.08865.pdf>`_
1626 |     | :authors:`Namyong, Park, Andrey, Kan, Xin, Luna, Dong, Tong, Zhao, Christos, Faloutsos`
1627 |     | :venue:`KDD 2019`
1628 | 
1629 | `Robust Graph Convolutional Networks Against Adversarial Attacks
1630 | <http://pengcui.thumedialab.com/papers/RGCN.pdf>`_
1631 |     | :authors:`Dingyuan, Zhu, Ziwei, Zhang, Peng, Cui, Wenwu, Zhu`
1632 |     | :venue:`KDD 2019`
1633 | 
1634 | `Certifiable Robustness and Robust Training for Graph Convolutional Networks
1635 | <https://arxiv.org/pdf/1906.12269.pdf>`_
1636 |     | :authors:`Daniel, Zügner, Stephan, Günnemann`
1637 |     | :venue:`KDD 2019`
1638 | 
1639 | `Gcn-mf: Disease-gene Association Identification By Graph Convolutional Networks and Matrix Factorization
1640 | <https://www.kdd.org/kdd2019/accepted-papers/view/gcn-mf-disease-gene-association-identification-by-graph-convolutional-netwo>`_
1641 |     | :authors:`Peng, Han, Peng, Yang, Peilin, Zhao, Shuo, Shang, Yong, Liu, Jiayu, Zhou, Xin, Gao, Panos, Kalnis`
1642 |     | :venue:`KDD 2019`
1643 | 
1644 | `Gcn-mf: Disease-gene Association Identification By Graph Convolutional Networks and Matrix Factorization
1645 | <https://dl.acm.org/citation.cfm?id=3330912>`_
1646 |     | :authors:`Peng, Han, Peng, Yang, Peilin, Zhao, Shuo, Shang, Yong, Liu, Jiayu, Zhou, Xin, Gao, Panos, Kalnis`
1647 |     | :venue:`KDD 2019`
1648 | 
1649 | ICCV
1650 | ----
1651 | 
1652 | `Deepgcns: Can Gcns Go As Deep As Cnns?
1653 | <https://arxiv.org/pdf/1904.03751.pdf>`_
1654 |     | :authors:`Guohao, Li, Matthias, Muller, Ali, Thabet, Bernard, Ghanem`
1655 |     | :venue:`ICCV 2019`
1656 | 
1657 | `Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learning
1658 | <https://arxiv.org/pdf/1908.02441.pdf>`_
1659 |     | :authors:`Jiwoong, Park, Minsik, Lee, Hyung, Jin, Chang, Kyuewang, Lee, Jin, Young, Choi`
1660 |     | :venue:`ICCV 2019`
1661 | 
1662 | `Pixel2mesh++: Multi-view 3d Mesh Generation via Deformation
1663 | <https://arxiv.org/pdf/1908.01491.pdf>`_
1664 |     | :authors:`Chao, Wen, Yinda, Zhang, Zhuwen, Li, Yanwei, Fu`
1665 |     | :venue:`ICCV 2019`
1666 | 
1667 | `Learning Trajectory Dependencies for Human Motion Prediction
1668 | <https://arxiv.org/pdf/1908.05436.pdf>`_
1669 |     | :authors:`Wei, Mao, Miaomiao, Liu, Mathieu, Salzmann, Hongdong, Li`
1670 |     | :venue:`ICCV 2019`
1671 | 
1672 | `Graph-based Object Classification for Neuromorphic Vision Sensing
1673 | <https://arxiv.org/pdf/1908.06648.pdf>`_
1674 |     | :authors:`Yin, Bi, Aaron, Chadha, Alhabib, Abbas, Eirina, Bourtsoulatze, Yiannis, Andreopoulos`
1675 |     | :venue:`ICCV 2019`
1676 | 
1677 | `Fashion Retrieval via Graph Reasoning Networks on a Similarity Pyramid
1678 | <https://arxiv.org/pdf/1908.11754.pdf>`_
1679 |     | :authors:`Zhanghui, Kuang, Yiming, Gao, Guanbin, Li, Ping, Luo, Yimin, Chen, Liang, Lin, Wayne, Zhang`
1680 |     | :venue:`ICCV 2019`
1681 | 
1682 | `Understanding Human Gaze Communication by Spatio-temporal Graph Reasoning
1683 | <https://arxiv.org/pdf/1909.02144.pdf>`_
1684 |     | :authors:`Lifeng, Fan, Wenguan, Wang, Siyuan, Huang, Xinyu, Tang, Song-Chun, Zhu`
1685 |     | :venue:`ICCV 2019`
1686 | 
1687 | `Visual Semantic Reasoning for Image-text Matching
1688 | <https://arxiv.org/pdf/1909.02701.pdf>`_
1689 |     | :authors:`Kunpeng, Li, Yulun, Zhang, Kai, Li, Yuanyuan, Li, Yun, Fu`
1690 |     | :venue:`ICCV 2019`
1691 | 
1692 | `Graph Convolutional Networks for Temporal Action Localization
1693 | <https://arxiv.org/pdf/1909.03252.pdf>`_
1694 |     | :authors:`Runhao, Zeng, Wenbing, Huang, Mingkui, Tan, Yu, Rong, Peilin, Zhao, Junzhou, Huang, Chuang, Gan`
1695 |     | :venue:`ICCV 2019`
1696 | 
1697 | `Learning Combinatorial Embedding Networks for Deep Graph Matching
1698 | <http://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Learning_Combinatorial_Embedding_Networks_for_Deep_Graph_Matching_ICCV_2019_paper.pdf>`_
1699 |     | :authors:`Runzhong, Wang, Junchi, Yan, Xiaokang, Yang`
1700 |     | :venue:`ICCV 2019`
1701 | 
1702 | EMNLP
1703 | -----
1704 | 
1705 | `Learning to Create Sentence Semantic Relation Graphs for Multi-document Summarization
1706 | <https://arxiv.org/pdf/1909.12231.pdf>`_
1707 |     | :authors:`Diego, Antognini, Boi, Faltings`
1708 |     | :venue:`EMNLP 2019`
1709 | 
1710 | `Dependency-guided Lstm-crf for Named Entity Recognition
1711 | <https://arxiv.org/pdf/1909.10148.pdf>`_
1712 |     | :authors:`Zhanming, Jie, Wei, Lu`
1713 |     | :venue:`EMNLP 2019`
1714 | 
1715 | `Modeling Conversation Structure and Temporal Dynamics for Jointly Predicting Rumor Stance and Veracity
1716 | <https://arxiv.org/pdf/1909.08211.pdf>`_
1717 |     | :authors:`Penghui, Wei, Nan, Xu, Wenji, Mao`
1718 |     | :venue:`EMNLP 2019`
1719 | 
1720 | `Dialoguegcn: A Graph Convolutional Neural Network for Emotion Recognition in Conversation
1721 | <https://arxiv.org/pdf/1908.11540.pdf>`_
1722 |     | :authors:`Deepanway, Ghosal, Navonil, Majumder, Soujanya, Poria, Niyati, Chhaya, Alexander, Gelbukh`
1723 |     | :venue:`EMNLP 2019`
1724 | 
1725 | `Modeling Graph Structure in Transformer for Better Amr-to-text Generation
1726 | <https://arxiv.org/pdf/1909.00136.pdf>`_
1727 |     | :authors:`Jie, Zhu, Junhui, Li, Muhua, Zhu, Longhua, Qian, Min, Zhang, Guodong, Zhou`
1728 |     | :venue:`EMNLP 2019`
1729 | 
1730 | `Kagnet: Knowledge-aware Graph Networks for Commonsense Reasoning
1731 | <https://arxiv.org/pdf/1909.02151.pdf>`_
1732 |     | :authors:`Bill, Yuchen, Lin, Xinyue, Chen, Jamin, Chen, Xiang, Ren`
1733 |     | :venue:`EMNLP 2019`
1734 | 
1735 | NeurIPS
1736 | -------
1737 | 
1738 | `Vgraph: A Generative Model for Joint Community Detection and Node Representation Learning
1739 | <https://arxiv.org/pdf/1906.07159.pdf>`_
1740 |     | :authors:`Fan-Yun, Sun, Meng, Qu, Jordan, Hoffmann, Chin-Wei, Huang, Jian, Tang`
1741 |     | :venue:`NeurIPS 2019`
1742 | 
1743 | `Variational Graph Recurrent Neural Networks
1744 | <https://arxiv.org/pdf/1908.09710.pdf>`_
1745 |     | :authors:`Ehsan, Hajiramezanali, Arman, Hasanzadeh, Nick, Duffield, Krishna, R, Narayanan, Mingyuan, Zhou, Xiaoning, Qian`
1746 |     | :venue:`NeurIPS 2019`
1747 | 
1748 | `Social-bigat: Multimodal Trajectory Forecasting Using Bicycle-gan and Graph Attention Networks
1749 | <https://arxiv.org/pdf/1907.03395.pdf>`_
1750 |     | :authors:`Vineet, Kosaraju, Amir, Sadeghian, Roberto, Martín-Martín, Ian, Reid, S., Hamid, Rezatofighi, Silvio, Savarese`
1751 |     | :venue:`NeurIPS 2019`
1752 | 
1753 | `Probabilistic Logic Neural Networks for Reasoning
1754 | <https://arxiv.org/pdf/1906.08495.pdf>`_
1755 |     | :authors:`Meng, Qu, Jian, Tang`
1756 |     | :venue:`NeurIPS 2019`
1757 | 
1758 | `Quaternion Knowledge Graph Embeddings
1759 | <https://arxiv.org/pdf/1904.10281.pdf>`_
1760 |     | :authors:`Shuai, Zhang, Yi, Tay, Lina, Yao, Qi, Liu`
1761 |     | :venue:`NeurIPS 2019`
1762 | 
1763 | `Quantum Embedding of Knowledge for Reasoning
1764 | <https://papers.nips.cc/paper/8797-quantum-embedding-of-knowledge-for-reasoning.pdf>`_
1765 |     | :authors:`Dinesh, Garg, Santosh, K., Srivastava, Hima, Karanam`
1766 |     | :venue:`NeurIPS 2019`
1767 | 
1768 | `Multi-relational Poincaré Graph Embeddings
1769 | <https://arxiv.org/pdf/1905.09791.pdf>`_
1770 |     | :authors:`Ivana, Balaževic, Carl, Allen, Timothy, Hospedales`
1771 |     | :venue:`NeurIPS 2019`
1772 | 
1773 | `Dfnets: Spectral Cnns for Graphs with Feedback-looped Filters
1774 | <http://users.cecs.anu.edu.au/~u5170295/papers/nips-wijesinghe-2019.pdf>`_
1775 |     | :authors:`Asiri, Wijesinghe, Qing, Wang`
1776 |     | :venue:`NeurIPS 2019`
1777 | 
1778 | `Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology
1779 | <https://arxiv.org/pdf/1907.05008.pdf>`_
1780 |     | :authors:`Nima, Dehmamy, Albert-László, Barabási, Rose, Yu`
1781 |     | :venue:`NeurIPS 2019`
1782 | 
1783 | `A Flexible Generative Framework for Graph-based Semi-supervised Learning
1784 | <https://arxiv.org/pdf/1905.10769.pdf>`_
1785 |     | :authors:`Jiaqi, Ma, Weijing, Tang, Ji, Zhu, Qiaozhu, Mei`
1786 |     | :venue:`NeurIPS 2019`
1787 | 
1788 | `Rethinking Kernel Methods for Node Representation Learning on Graphs
1789 | <https://arxiv.org/pdf/1910.02548.pdf>`_
1790 |     | :authors:`Yu, Tian, Long, Zhao, Xi, Peng, Dimitris, N., Metaxas`
1791 |     | :venue:`NeurIPS 2019`
1792 | 
1793 | `Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
1794 | <https://arxiv.org/pdf/1906.02174.pdf>`_
1795 |     | :authors:`Sitao, Luan, Mingde, Zhao, Xiao-Wen, Chang, Doina, Precup`
1796 |     | :venue:`NeurIPS 2019`
1797 | 
1798 | `N-gram Graph: A Simple Unsupervised Representation for Molecules
1799 | <https://arxiv.org/pdf/1806.09206.pdf>`_
1800 |     | :authors:`Shengchao, Liu, Thevaa, Chandereng, Yingyu, Liang`
1801 |     | :venue:`NeurIPS 2019`
1802 | 
1803 | `Semantically-regularized Logic Graph Embeddings
1804 | <https://arxiv.org/pdf/1909.01161.pdf>`_
1805 |     | :authors:`Yaqi, Xie, Ziwei, Xu, Kuldeep, Meel, Mohan, S, Kankanhalli, Harold, Soh`
1806 |     | :venue:`NeurIPS 2019`
1807 | 
1808 | `Semi-implicit Graph Variational Auto-encoders
1809 | <https://arxiv.org/pdf/1908.07078.pdf>`_
1810 |     | :authors:`Arman, Hasanzadeh, Ehsan, Hajiramezanali, Nick, Duffield, Krishna, Narayanan, Mingyuan, Zhou, Xiaoning, Qian`
1811 |     | :venue:`NeurIPS 2019`
1812 | 
1813 | `D-vae: A Variational Autoencoder for Directed Acyclic Graphs
1814 | <https://arxiv.org/pdf/1904.11088.pdf>`_
1815 |     | :authors:`Muhan, Zhang, Shali, Jiang, Zhicheng, Cui, Roman, Garnett, Yixin, Chen`
1816 |     | :venue:`NeurIPS 2019`
1817 | 
1818 | `No Press Diplomacy: Modeling Multi-agent Gameplay
1819 | <https://arxiv.org/pdf/1909.02128.pdf>`_
1820 |     | :authors:`Philip, Paquette, Yuchen, Lu, Steven, Bocco, Max, O., Smith, Satya, Ortiz-Gagne, Jonathan, K., Kummerfeld, Satinder, Singh, Joelle, Pineau, Aaron, Courville`
1821 |     | :venue:`NeurIPS 2019`
1822 | 
1823 | `Approximation Ratios of Graph Neural Networks for Combinatorial Problems
1824 | <https://arxiv.org/pdf/1905.10261.pdf>`_
1825 |     | :authors:`Ryoma, Sato, Makoto, Yamada, Hisashi, Kashima`
1826 |     | :venue:`NeurIPS 2019`
1827 | 
1828 | `Exact Combinatorial Optimization with Graph Convolutional Neural Networks
1829 | <https://arxiv.org/pdf/1906.01629.pdf>`_
1830 |     | :authors:`Maxime, Gasse, Didier, Chételat, Nicola, Ferroni, Laurent, Charlin, Andrea, Lodi`
1831 |     | :venue:`NeurIPS 2019`
1832 | 
1833 | `On Learning Paradigms for the Travelling Salesman Problem
1834 | <https://arxiv.org/pdf/1910.07210.pdf>`_
1835 |     | :authors:`Chaitanya, K., Joshi, Thomas, Laurent, Xavier, Bresson`
1836 |     | :venue:`NeurIPS 2019 Workshop`
1837 | 
1838 | `Learning to Propagate for Graph Meta-learning
1839 | <https://arxiv.org/pdf/1909.05024.pdf>`_
1840 |     | :authors:`Lu, Liu, Tianyi, Zhou, Guodong, Long, Jing, Jiang, Chengqi, Zhang`
1841 |     | :venue:`NeurIPS 2019`
1842 | 
1843 | `A Flexible Generative Framework for Graph-based Semi-supervised Learning
1844 | <https://arxiv.org/abs/1905.10769>`_
1845 |     | :authors:`Jiaqi, Ma, Weijing, Tang, Ji, Zhu, Qiaozhu, Mei`
1846 |     | :venue:`NeurIPS 2019`
1847 | 
1848 | `Graph Normalizing Flows
1849 | <https://arxiv.org/abs/1905.13177>`_
1850 |     | :authors:`Jenny, Liu, Aviral, Kumar, Jimmy, Ba, Jamie, Kiros, Kevin, Swersky`
1851 |     | :venue:`NeurIPS 2019`
1852 | 
1853 | `Conditional Structure Generation through Graph Variational Generative Adversarial Nets
1854 | <http://jiyang3.web.engr.illinois.edu/files/condgen.pdf>`_
1855 |     | :authors:`Carl, Yang, Peiye, Zhuang, Wenhan, Shi, Alan, Luu, Pan, Li`
1856 |     | :venue:`NeurIPS 2019`
1857 | 
1858 | `Efficient Graph Generation with Graph Recurrent Attention Networks
1859 | <https://arxiv.org/pdf/1910.00760.pdf>`_
1860 |     | :authors:`Renjie, Liao, Yujia, Li, Yang, Song, Shenlong, Wang, Charlie, Nash, William, L., Hamilton, David, Duvenaud, Raquel, Urtasun, Richard, Zemel`
1861 |     | :venue:`NeurIPS 2019`
1862 | 
1863 | 2020
1864 | ====
1865 | 
1866 | ICLR
1867 | ----
1868 | 
1869 | `Graphzoom: A Multi-level Spectral Approach for Accurate and Scalable Graph Embedding
1870 | <https://openreview.net/pdf?id=r1lGO0EKDH>`_
1871 |     | :authors:`Chenhui, Deng, Zhiqiang, Zhao, Yongyu, Wang, Zhiru, Zhang, Zhuo, Feng`
1872 |     | :venue:`ICLR 2020`
1873 | 
1874 | `Dynamically Pruned Message Passing Networks for Large-scale Knowledge Graph Reasoning
1875 | <https://openreview.net/forum?id=rkeuAhVKvB>`_
1876 |     | :authors:`Xiaoran, Xu, Wei, Feng, Yunsheng, Jiang, Xiaohui, Xie, Zhiqing, Sun, Zhi-Hong, Deng`
1877 |     | :venue:`ICLR 2020`
1878 | 
1879 | `Curvature Graph Network
1880 | <https://openreview.net/pdf?id=BylEqnVFDB>`_
1881 |     | :authors:`Ze, Ye, Kin, Sum, Liu, Tengfei, Ma, Jie, Gao, Chao, Chen`
1882 |     | :venue:`ICLR 2020`
1883 | 
1884 | `Memory-based Graph Networks
1885 | <https://openreview.net/pdf?id=r1laNeBYPB>`_
1886 |     | :authors:`Amir, hosein, Khasahmadi, Kaveh, Hassani, Parsa, Moradi, Leo, Lee, Quaid, Morris`
1887 |     | :venue:`ICLR 2020`
1888 | 
1889 | `Strategies for Pre-training Graph Neural Networks
1890 | <https://openreview.net/pdf?id=HJlWWJSFDH>`_
1891 |     | :authors:`Weihua, Hu, Bowen, Liu, Joseph, Gomes, Marinka, Zitnik, Percy, Liang, Vijay, Pande, Jure, Leskovec`
1892 |     | :venue:`ICLR 2020`
1893 | 
1894 | `Deep Graph Matching Consensus
1895 | <https://openreview.net/pdf?id=HyeJf1HKvS>`_
1896 |     | :authors:`Matthias, Fey, Jan, E., Lenssen, Christopher, Morris, Jonathan, Masci, Nils, M., Kriege`
1897 |     | :venue:`ICLR 2020`
1898 | 
1899 | `Few-shot Learning on Graphs via Super-classes Based on Graph Spectral Measures
1900 | <https://openreview.net/forum?id=Bkeeca4Kvr>`_
1901 |     | :authors:`Jatin, Chauhan, Deepak, Nathani, Manohar, Kaul`
1902 |     | :venue:`ICLR 2020`
1903 | 
1904 | `Automated Relational Meta-learning
1905 | <https://openreview.net/pdf?id=rklp93EtwH>`_
1906 |     | :authors:`Huaxiu, Yao, Xian, Wu, Zhiqiang, Tao, Yaliang, Li, Bolin, Ding, Ruirui, Li, Zhenhui, Li`
1907 |     | :venue:`ICLR 2020`
1908 | 
1909 | `Directional Message Passing for Molecular Graphs
1910 | <https://openreview.net/pdf?id=B1eWbxStPH>`_
1911 |     | :authors:`Johannes, Klicpera, Janek, Groß, Stephan, Günnemann`
1912 |     | :venue:`ICLR 2020`
1913 | 
1914 | `Neural Execution of Graph Algorithms
1915 | <https://openreview.net/pdf?id=SkgKO0EtvS>`_
1916 |     | :authors:`Petar, Veličković, Rex, Ying, Matilde, Padovano, Raia, Hadsell, Charles, Blundell`
1917 |     | :venue:`ICLR 2020`
1918 | 
1919 | `Graphaf: a Flow-based Autoregressive Model for Molecular Graph Generation
1920 | <https://openreview.net/pdf?id=S1esMkHYPr>`_
1921 |     | :authors:`Chence, Shi, Minkai, Xu, Zhaocheng, Zhu, Weinan, Zhang, Ming, Zhang, Jian, Tang`
1922 |     | :venue:`ICLR 2020`
1923 | 
1924 | Others
1925 | ======
1926 | 
1927 | `Deep Graph Library
1928 | <https://www.dgl.ai>`_
1929 |     | :authors:`DGL, Team`
1930 | 
1931 | `Ampligraph
1932 | <https://github.com/Accenture/AmpliGraph>`_
1933 |     | :authors:`Luca, Costabello, Sumit, Pai, Chan, Le, Van, Rory, McGrath, Nicholas, McCarthy, Pedro, Tabacof`
1934 | 
1935 | `Euler
1936 | <https://github.com/alibaba/euler>`_
1937 |     | :authors:`Alimama, Engineering, Platform, Team, Alimama, Search, Advertising, Algorithm, Team`
1938 | 
1939 | 


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--------------------------------------------------------------------------------
/README.rst:
--------------------------------------------------------------------------------
   1 | Literature of Deep Learning for Graphs
   2 | **************************************
   3 | 
   4 | This is a paper list about deep learning for graphs.
   5 | 
   6 | .. raw:: html
   7 | 
   8 |     <div><a href="README.rst">Sort by topic</a></div>
   9 |     <div><a href="BYVENUE.rst">Sort by venue</a></div>
  10 | 
  11 | .. contents::
  12 |     :local:
  13 |     :depth: 2
  14 | 
  15 | .. sectnum::
  16 |     :depth: 2
  17 | 
  18 | .. role:: authors(emphasis)
  19 | 
  20 | .. role:: venue(strong)
  21 | 
  22 | .. role:: keywords(emphasis)
  23 | 
  24 | Node Representation Learning
  25 | ============================
  26 | 
  27 | Unsupervised Node Representation Learning
  28 | -----------------------------------------
  29 | 
  30 | `DeepWalk: Online Learning of Social Representations
  31 | <https://arxiv.org/pdf/1403.6652>`_
  32 |     | :authors:`Bryan Perozzi, Rami Al-Rfou, Steven Skiena`
  33 |     | :venue:`KDD 2014`
  34 |     | :keywords:`Node classification, Random walk, Skip-gram`
  35 | 
  36 | `LINE: Large-scale Information Network Embedding
  37 | <https://arxiv.org/pdf/1503.03578>`_
  38 |     | :authors:`Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, Qiaozhu Mei`
  39 |     | :venue:`WWW 2015`
  40 |     | :keywords:`First-order, Second-order, Node classification`
  41 | 
  42 | `GraRep: Learning Graph Representations with Global Structural Information
  43 | <https://dl.acm.org/citation.cfm?id=2806512>`_
  44 |     | :authors:`Shaosheng Cao, Wei Lu, Qiongkai Xu`
  45 |     | :venue:`CIKM 2015`
  46 |     | :keywords:`High-order, SVD`
  47 | 
  48 | `node2vec: Scalable Feature Learning for Networks
  49 | <https://arxiv.org/pdf/1607.00653>`_
  50 |     | :authors:`Aditya Grover, Jure Leskovec`
  51 |     | :venue:`KDD 2016`
  52 |     | :keywords:`Breadth-first Search, Depth-first Search, Node Classification, Link Prediction`
  53 | 
  54 | `Variational Graph Auto-Encoders
  55 | <https://arxiv.org/abs/1611.07308>`_
  56 |     | :authors:`Thomas N. Kipf, Max Welling`
  57 |     | :venue:`arXiv 2016`
  58 | 
  59 | `Scalable Graph Embedding for Asymmetric Proximity
  60 | <https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14696>`_
  61 |     | :authors:`Chang Zhou, Yuqiong Liu, Xiaofei Liu, Zhongyi Liu, Jun Gao`
  62 |     | :venue:`AAAI 2017`
  63 | 
  64 | `Fast Network Embedding Enhancement via High Order Proximity Approximation
  65 | <https://www.ijcai.org/proceedings/2017/544>`_
  66 |     | :authors:`Cheng Yang, Maosong Sun, Zhiyuan Liu, Cunchao Tu`
  67 |     | :venue:`IJCAI 2017`
  68 | 
  69 | `struc2vec: Learning Node Representations from Structural Identity
  70 | <https://arxiv.org/pdf/1704.03165>`_
  71 |     | :authors:`Leonardo F. R. Ribeiro, Pedro H. P. Savarese, Daniel R. Figueiredo`
  72 |     | :venue:`KDD 2017`
  73 |     | :keywords:`Structural Identity`
  74 | 
  75 | `Poincaré Embeddings for Learning Hierarchical Representations
  76 | <https://arxiv.org/pdf/1705.08039>`_
  77 |     | :authors:`Maximilian Nickel, Douwe Kiela`
  78 |     | :venue:`NIPS 2017`
  79 | 
  80 | `VERSE: Versatile Graph Embeddings from Similarity Measures
  81 | <https://arxiv.org/pdf/1803.04742>`_
  82 |     | :authors:`Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Emmanuel Müller`
  83 |     | :venue:`WWW 2018`
  84 | 
  85 | `Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec
  86 | <https://arxiv.org/pdf/1710.02971>`_
  87 |     | :authors:`Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, Jie Tang`
  88 |     | :venue:`WSDM 2018`
  89 | 
  90 | `Learning Structural Node Embeddings via Diffusion Wavelets
  91 | <https://arxiv.org/pdf/1710.10321>`_
  92 |     | :authors:`Claire Donnat, Marinka Zitnik, David Hallac, Jure Leskovec`
  93 |     | :venue:`KDD 2018`
  94 | 
  95 | `Adversarial Network Embedding
  96 | <https://arxiv.org/pdf/1711.07838>`_
  97 |     | :authors:`Quanyu Dai, Qiang Li, Jian Tang, Dan Wang`
  98 |     | :venue:`AAAI 2018`
  99 | 
 100 | `GraphGAN: Graph Representation Learning with Generative Adversarial Nets
 101 | <https://arxiv.org/pdf/1711.08267>`_
 102 |     | :authors:`Hongwei Wang, Jia Wang, Jialin Wang, Miao Zhao, Weinan Zhang, Fuzheng Zhang, Xing Xie, Minyi Guo`
 103 |     | :venue:`AAAI 2018`
 104 | 
 105 | `A General View for Network Embedding as Matrix Factorization
 106 | <https://dl.acm.org/citation.cfm?id=3291029>`_
 107 |     | :authors:`Xin Liu, Tsuyoshi Murata, Kyoung-Sook Kim, Chatchawan Kotarasu, Chenyi Zhuang`
 108 |     | :venue:`WSDM 2019`
 109 | 
 110 | `Deep Graph Infomax
 111 | <https://arxiv.org/pdf/1809.10341>`_
 112 |     | :authors:`Petar Veličković, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, R Devon Hjelm`
 113 |     | :venue:`ICLR 2019`
 114 | 
 115 | `NetSMF: Large-Scale Network Embedding as Sparse Matrix Factorization
 116 | <http://keg.cs.tsinghua.edu.cn/jietang/publications/www19-Qiu-et-al-NetSMF-Large-Scale-Network-Embedding.pdf>`_
 117 |     | :authors:`Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Chi Wang, Kuansan Wang, Jie Tang`
 118 |     | :venue:`WWW 2019`
 119 | 
 120 | `Adversarial Training Methods for Network Embedding
 121 | <https://dl.acm.org/citation.cfm?id=3313445>`_
 122 |     | :authors:`Quanyu Dai, Xiao Shen, Liang Zhang, Qiang Li, Dan Wang`
 123 |     | :venue:`WWW 2019`
 124 | 
 125 | `vGraph: A Generative Model for Joint Community Detection and Node Representation Learning
 126 | <https://arxiv.org/pdf/1906.07159.pdf>`_
 127 |     | :authors:`Fan-Yun Sun, Meng Qu, Jordan Hoffmann, Chin-Wei Huang, Jian Tang`
 128 |     | :venue:`NeurIPS 2019`
 129 | 
 130 | `ProGAN: Network Embedding via Proximity Generative Adversarial Network
 131 | <https://dl.acm.org/citation.cfm?id=3330866>`_
 132 |     | :authors:`Hongchang Gao, Jian Pei, Heng Huang`
 133 |     | :venue:`KDD 2019`
 134 | 
 135 | `GraphZoom: A Multi-level Spectral Approach for Accurate and Scalable Graph Embedding
 136 | <https://openreview.net/pdf?id=r1lGO0EKDH>`_
 137 | 	| :authors:`Chenhui Deng, Zhiqiang Zhao, Yongyu Wang, Zhiru Zhang, Zhuo Feng`
 138 | 	| :venue:`ICLR 2020`
 139 | 
 140 | Node Representation Learning in Heterogeneous Graphs
 141 | ----------------------------------------------------
 142 | 
 143 | `Learning Latent Representations of Nodes for Classifying in Heterogeneous Social Networks
 144 | <https://dl.acm.org/citation.cfm?id=2556225>`_
 145 |     | :authors:`Yann Jacob, Ludovic Denoyer, Patrick Gallinari`
 146 |     | :venue:`WSDM 2014`
 147 | 
 148 | `PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks
 149 | <https://arxiv.org/pdf/1508.00200>`_
 150 |     | :authors:`Jian Tang, Meng Qu, Qiaozhu Mei`
 151 |     | :venue:`KDD 2015`
 152 |     | :keywords:`Text Embedding, Heterogeneous Text Graphs`
 153 | 
 154 | `Heterogeneous Network Embedding via Deep Architectures
 155 | <https://dl.acm.org/citation.cfm?id=2783296>`_
 156 |     | :authors:`Shiyu Chang, Wei Han, Jiliang Tang, Guo-Jun Qi, Charu C. Aggarwal, Thomas S. Huang`
 157 |     | :venue:`KDD 2015`
 158 | 
 159 | `Network Representation Learning with Rich Text Information
 160 | <https://www.aaai.org/ocs/index.php/IJCAI/IJCAI15/paper/view/11098>`_
 161 |     | :authors:`Cheng Yang, Zhiyuan Liu, Deli Zhao, Maosong Sun, Edward Chang`
 162 |     | :venue:`AAAI 2015`
 163 | 
 164 | `Max-Margin DeepWalk: Discriminative Learning of Network Representation
 165 | <https://www.ijcai.org/Proceedings/16/Papers/547.pdf>`_
 166 |     | :authors:`Cunchao Tu, Weicheng Zhang, Zhiyuan Liu, Maosong Sun`
 167 |     | :venue:`IJCAI 2016`
 168 | 
 169 | `metapath2vec: Scalable Representation Learning for Heterogeneous Networks
 170 | <https://dl.acm.org/citation.cfm?id=3098036>`_
 171 |     | :authors:`Yuxiao Dong, Nitesh V. Chawla, Ananthram Swami`
 172 |     | :venue:`KDD 2017`
 173 | 
 174 | `Meta-Path Guided Embedding for Similarity Search in Large-Scale Heterogeneous Information Networks
 175 | <https://arxiv.org/pdf/1610.09769>`_
 176 |     | :authors:`Jingbo Shang, Meng Qu, Jialu Liu, Lance M. Kaplan, Jiawei Han, Jian Peng`
 177 |     | :venue:`arXiv 2016`
 178 | 
 179 | `HIN2Vec: Explore Meta-paths in Heterogeneous Information Networks for Representation Learning
 180 | <https://dl.acm.org/citation.cfm?id=3132953>`_
 181 |     | :authors:`Tao-yang Fu, Wang-Chien Lee, Zhen Lei`
 182 |     | :venue:`CIKM 2017`
 183 | 
 184 | `An Attention-based Collaboration Framework for Multi-View Network Representation Learning
 185 | <https://arxiv.org/pdf/1709.06636>`_
 186 |     | :authors:`Meng Qu, Jian Tang, Jingbo Shang, Xiang Ren, Ming Zhang, Jiawei Han`
 187 |     | :venue:`CIKM 2017`
 188 | 
 189 | `Multi-view Clustering with Graph Embedding for Connectome Analysis
 190 | <https://dl.acm.org/citation.cfm?id=3132909>`_
 191 |     | :authors:`Guixiang Ma, Lifang He, Chun-Ta Lu, Weixiang Shao, Philip S. Yu, Alex D. Leow, Ann B. Ragin`
 192 |     | :venue:`CIKM 2017`
 193 | 
 194 | `Attributed Signed Network Embedding
 195 | <https://dl.acm.org/citation.cfm?id=3132847.3132905>`_
 196 |     | :authors:`Suhang Wang, Charu Aggarwal, Jiliang Tang, Huan Liu`
 197 |     | :venue:`CIKM 2017`
 198 | 
 199 | `CANE: Context-Aware Network Embedding for Relation Modeling
 200 | <https://aclweb.org/anthology/papers/P/P17/P17-1158/>`_
 201 |     | :authors:`Cunchao Tu, Han Liu, Zhiyuan Liu, Maosong Sun`
 202 |     | :venue:`ACL 2017`
 203 | 
 204 | `PME: Projected Metric Embedding on Heterogeneous Networks for Link Prediction
 205 | <https://dl.acm.org/citation.cfm?id=3219986>`_
 206 |     | :authors:`Hongxu Chen, Hongzhi Yin, Weiqing Wang, Hao Wang, Quoc Viet Hung Nguyen, Xue Li`
 207 |     | :venue:`KDD 2018`
 208 | 
 209 | `BiNE: Bipartite Network Embedding
 210 | <https://dl.acm.org/citation.cfm?id=3209978.3209987>`_
 211 |     | :authors:`Ming Gao, Leihui Chen, Xiangnan He, Aoying Zhou`
 212 |     | :venue:`SIGIR 2018`
 213 | 
 214 | `StarSpace: Embed All The Things
 215 | <https://arxiv.org/pdf/1709.03856>`_
 216 |     | :authors:`Ledell Wu, Adam Fisch, Sumit Chopra, Keith Adams, Antoine Bordes, Jason Weston`
 217 |     | :venue:`AAAI 2018`
 218 | 
 219 | `Exploring Expert Cognition for Attributed Network Embedding
 220 | <https://dl.acm.org/citation.cfm?id=3159655>`_
 221 |     | :authors:`Xiao Huang, Qingquan Song, Jundong Li, Xia Hu`
 222 |     | :venue:`WSDM 2018`
 223 | 
 224 | `SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction
 225 | <https://arxiv.org/pdf/1712.00732>`_
 226 |     | :authors:`Hongwei Wang, Fuzheng Zhang, Min Hou, Xing Xie, Minyi Guo, Qi Liu`
 227 |     | :venue:`WSDM 2018`
 228 | 
 229 | `Multidimensional Network Embedding with Hierarchical Structures
 230 | <https://dl.acm.org/citation.cfm?id=3159680>`_
 231 |     | :authors:`Yao Ma, Zhaochun Ren, Ziheng Jiang, Jiliang Tang, Dawei Yin`
 232 |     | :venue:`WSDM 2018`
 233 | 
 234 | `Curriculum Learning for Heterogeneous Star Network Embedding via Deep Reinforcement Learning
 235 | <https://dl.acm.org/citation.cfm?id=3159711>`_
 236 |     | :authors:`Meng Qu, Jian Tang, Jiawei Han`
 237 |     | :venue:`WSDM 2018`
 238 | 
 239 | `Generative Adversarial Network based Heterogeneous Bibliographic Network Representation for Personalized Citation Recommendation
 240 | <https://www.semanticscholar.org/paper/Generative-Adversarial-Network-Based-Heterogeneous-Cai-Han/1596d6487012696ba400fb69904a2c372a08a2be>`_
 241 |     | :authors:`Xiaoyan Cai, Junwei Han, Libin Yang`
 242 |     | :venue:`AAAI 2018`
 243 | 
 244 | `ANRL: Attributed Network Representation Learning via Deep Neural Networks
 245 | <https://www.ijcai.org/proceedings/2018/438>`_
 246 |     | :authors:`Zhen Zhang, Hongxia Yang, Jiajun Bu, Sheng Zhou, Pinggang Yu, Jianwei Zhang, Martin Ester, Can Wang`
 247 |     | :venue:`IJCAI 2018`
 248 | 
 249 | `Efficient Attributed Network Embedding via Recursive Randomized Hashing
 250 | <https://www.ijcai.org/proceedings/2018/397>`_
 251 |     | :authors:`Wei Wu, Bin Li, Ling Chen, Chengqi Zhang`
 252 |     | :venue:`IJCAI 2018`
 253 | 
 254 | `Deep Attributed Network Embedding
 255 | <https://www.ijcai.org/proceedings/2018/467>`_
 256 |     | :authors:`Hongchang Gao, Heng Huang`
 257 |     | :venue:`IJCAI 2018`
 258 | 
 259 | `Co-Regularized Deep Multi-Network Embedding
 260 | <https://dl.acm.org/citation.cfm?id=3186113>`_
 261 |     | :authors:`Jingchao Ni, Shiyu Chang, Xiao Liu, Wei Cheng, Haifeng Chen, Dongkuan Xu, Xiang Zhang`
 262 |     | :venue:`WWW 2018`
 263 | 
 264 | `Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks
 265 | <https://arxiv.org/pdf/1807.03490>`_
 266 |     | :authors:`Yu Shi, Qi Zhu, Fang Guo, Chao Zhang, Jiawei Han`
 267 |     | :venue:`KDD 2018`
 268 | 
 269 | `Meta-Graph Based HIN Spectral Embedding: Methods, Analyses, and Insights
 270 | <https://www.semanticscholar.org/paper/Meta-Graph-Based-HIN-Spectral-Embedding%3A-Methods%2C-Yang-Feng/4d5f4d6785d550383e3f3afb04c3015bf0d28405>`_
 271 |     | :authors:`Carl Yang, Yichen Feng, Pan Li, Yu Shi, Jiawei Han`
 272 |     | :venue:`ICDM 2018`
 273 | 
 274 | `SIDE: Representation Learning in Signed Directed Networks
 275 | <https://dl.acm.org/citation.cfm?id=3186117>`_
 276 |     | :authors:`Junghwan Kim, Haekyu Park, Ji-Eun Lee, U Kang`
 277 |     | :venue:`WWW 2018`
 278 | 
 279 | `Learning Network-to-Network Model for Content-rich Network Embedding
 280 | <https://dl.acm.org/citation.cfm?id=3330924>`_
 281 |     | :authors:`	Zhicheng He, Jie Liu, Na Li, Yalou Huang`
 282 |     | :venue:`KDD 2019`
 283 | 
 284 | Node Representation Learning in Dynamic Graphs
 285 | ----------------------------------------------
 286 | 
 287 | `Know-evolve: Deep temporal reasoning for dynamic knowledge graphs
 288 | <https://arxiv.org/pdf/1705.05742.pdf>`_
 289 |     | :authors:`Rakshit Trivedi, Hanjun Dai, Yichen Wang, Le Song`
 290 |     | :venue:`ICML 2017`
 291 | 
 292 | `Dyngem: Deep embedding method for dynamic graphs
 293 | <https://arxiv.org/pdf/1805.11273.pdf>`_
 294 |     | :authors:`Palash Goyal, Nitin Kamra, Xinran He, Yan Liu`
 295 |     | :venue:`ICLR 2017 Workshop`
 296 | 
 297 | `Attributed network embedding for learning in a dynamic environment
 298 | <https://arxiv.org/pdf/1706.01860.pdf>`_
 299 |     | :authors:`Jundong Li, Harsh Dani, Xia Hu, Jiliang Tang, Yi Chang, Huan Liu`
 300 |     | :venue:`CIKM 2017`
 301 | 
 302 | `Dynamic Network Embedding by Modeling Triadic Closure Process
 303 | <http://yangy.org/works/dynamictriad/dynamic_triad.pdf>`_
 304 |     | :authors:`Lekui Zhou, Yang Yang, Xiang Ren, Fei Wu, Yueting Zhuang`
 305 |     | :venue:`AAAI 2018`
 306 | 
 307 | `DepthLGP: Learning Embeddings of Out-of-Sample Nodes in Dynamic Networks
 308 | <https://pdfs.semanticscholar.org/9499/b38866b1eb87ae43fa5be02f9d08cd3c20a8.pdf?_ga=2.6780794.935636364.1561139530-1831876308.1523264869>`_
 309 |     | :authors:`Jianxin Ma, Peng Cui, Wenwu Zhu`
 310 |     | :venue:`AAAI 2018`
 311 | 
 312 | `TIMERS: Error-Bounded SVD Restart on Dynamic Networks
 313 | <https://arxiv.org/pdf/1711.09541.pdf>`_
 314 |     | :authors:`Ziwei Zhang, Peng Cui, Jian Pei, Xiao Wang, Wenwu Zhu`
 315 |     | :venue:`AAAI 2018`
 316 | 
 317 | `Dynamic Embeddings for User Profiling in Twitter
 318 | <https://dl.acm.org/citation.cfm?id=3219819.3220043>`_
 319 |     | :authors:`Shangsong Liang, Xiangliang Zhang, Zhaochun Ren, Evangelos Kanoulas`
 320 |     | :venue:`KDD 2018`
 321 | 
 322 | `Dynamic Network Embedding : An Extended Approach for Skip-gram based Network Embedding
 323 | <https://www.ijcai.org/proceedings/2018/0288.pdf>`_
 324 |     | :authors:`Lun Du, Yun Wang, Guojie Song, Zhicong Lu, Junshan Wang`
 325 |     | :venue:`IJCAI 2018`
 326 | 
 327 | `DyRep: Learning Representations over Dynamic Graphs
 328 | <https://openreview.net/pdf?id=HyePrhR5KX>`_
 329 |     | :authors:`Rakshit Trivedi, Mehrdad Farajtabar, Prasenjeet Biswal, Hongyuan Zha`
 330 |     | :venue:`ICLR 2019`
 331 | 
 332 | `Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks
 333 | <https://cs.stanford.edu/~srijan/pubs/jodie-kdd2019.pdf>`_
 334 |     | :authors:`Srijan Kumar, Xikun Zhang, Jure Leskovec`
 335 |     | :venue:`KDD 2019`
 336 | 
 337 | `Variational Graph Recurrent Neural Networks
 338 | <https://arxiv.org/pdf/1908.09710.pdf>`_
 339 |     | :authors:`Ehsan Hajiramezanali, Arman Hasanzadeh, Nick Duffield, Krishna R Narayanan, Mingyuan Zhou, Xiaoning Qian`
 340 |     | :venue:`NeurIPS 2019`
 341 | 
 342 | `Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks
 343 | <https://arxiv.org/pdf/1907.03395.pdf>`_
 344 |     | :authors:`Vineet Kosaraju, Amir Sadeghian, Roberto Martín-Martín, Ian Reid, S. Hamid Rezatofighi, Silvio Savarese`
 345 |     | :venue:`NeurIPS 2019`
 346 | 
 347 | Knowledge Graph Embedding
 348 | =========================
 349 | 
 350 | `A Three-Way Model for Collective Learning on Multi-Relational Data.
 351 | <http://www.icml-2011.org/papers/438_icmlpaper.pdf>`_
 352 |     | :authors:`Maximilian Nickel, Volker Tresp, Hans-Peter Kriegel`
 353 |     | :venue:`ICML 2011`
 354 | 
 355 | `Translating Embeddings for Modeling Multi-relational Data
 356 | <https://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data.pdf>`_
 357 |     | :authors:`Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, Oksana Yakhnenko`
 358 |     | :venue:`NIPS 2013`
 359 | 
 360 | `Knowledge Graph Embedding by Translating on Hyperplanes
 361 | <https://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/viewFile/8531/8546>`_
 362 |     | :authors:`Zhen Wang, Jianwen Zhang, Jianlin Feng, Zheng Chen`
 363 |     | :venue:`AAAI 2014`
 364 | 
 365 | `Reducing the Rank of Relational Factorization Models by Including Observable Patterns
 366 | <http://papers.nips.cc/paper/5448-reducing-the-rank-in-relational-factorization-models-by-including-observable-patterns.pdf>`_
 367 |     | :authors:`Maximilian Nickel, Xueyan Jiang, Volker Tresp`
 368 |     | :venue:`NIPS 2014`
 369 | 
 370 | `Learning Entity and Relation Embeddings for Knowledge Graph Completion
 371 | <https://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/viewFile/9571/9523>`_
 372 |     | :authors:`Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, Xuan Zhu`
 373 |     | :venue:`AAAI 2015`
 374 | 
 375 | `A Review of Relational Machine Learning for Knowledge Graph
 376 | <https://arxiv.org/pdf/1503.00759.pdf>`_
 377 |     | :authors:`Maximilian Nickel, Kevin Murphy, Volker Tresp, Evgeniy Gabrilovich`
 378 |     | :venue:`IEEE 2015`
 379 | 
 380 | `Knowledge Graph Embedding via Dynamic Mapping Matrix
 381 | <https://www.aclweb.org/anthology/P15-1067>`_
 382 |     | :authors:`Guoliang Ji, Shizhu He, Liheng Xu, Kang Liu, Jun Zha`
 383 |     | :venue:`ACL 2015`
 384 | 
 385 | `Modeling Relation Paths for Representation Learning of Knowledge Bases
 386 | <https://arxiv.org/pdf/1506.00379>`_
 387 |     | :authors:`Yankai Lin, Zhiyuan Liu, Huanbo Luan, Maosong Sun, Siwei Rao, Song Liu`
 388 |     | :venue:`EMNLP 2015`
 389 | 
 390 | `Embedding Entities and Relations for Learning and Inference in Knowledge Bases
 391 | <https://arxiv.org/pdf/1412.6575>`_
 392 |     | :authors:`Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, Li Deng`
 393 |     | :venue:`ICLR 2015`
 394 | 
 395 | `Holographic Embeddings of Knowledge Graphs
 396 | <https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/viewPDFInterstitial/12484/11828>`_
 397 |     | :authors:`Maximilian Nickel, Lorenzo Rosasco, Tomaso Poggio`
 398 |     | :venue:`AAAI 2016`
 399 | 
 400 | `Complex Embeddings for Simple Link Prediction
 401 | <http://www.jmlr.org/proceedings/papers/v48/trouillon16.pdf>`_
 402 |     | :authors:`Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, Guillaume Bouchard`
 403 |     | :venue:`ICML 2016`
 404 | 
 405 | `Modeling Relational Data with Graph Convolutional Networks
 406 | <https://arxiv.org/pdf/1703.06103>`_
 407 |     | :authors:`Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, Max Welling`
 408 |     | :venue:`arXiv 2017`
 409 | 
 410 | `Fast Linear Model for Knowledge Graph Embeddings
 411 | <https://arxiv.org/pdf/1710.10881>`_
 412 |     | :authors:`Armand Joulin, Edouard Grave, Piotr Bojanowski, Maximilian Nickel, Tomas Mikolov`
 413 |     | :venue:`arXiv 2017`
 414 | 
 415 | `Convolutional 2D Knowledge Graph Embeddings
 416 | <https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/download/17366/15884>`_
 417 |     | :authors:`Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, Sebastian Riedel`
 418 |     | :venue:`AAAI 2018`
 419 | 
 420 | `Knowledge Graph Embedding With Iterative Guidance From Soft Rules
 421 | <https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/download/16369/16011>`_
 422 |     | :authors:`Shu Guo, Quan Wang, Lihong Wang, Bin Wang, Li Guo`
 423 |     | :venue:`AAAI 2018`
 424 | 
 425 | `KBGAN: Adversarial Learning for Knowledge Graph Embeddings
 426 | <https://arxiv.org/abs/1711.04071>`_
 427 |     | :authors:`Liwei Cai, William Yang Wang`
 428 |     | :venue:`NAACL 2018`
 429 | 
 430 | `Improving Knowledge Graph Embedding Using Simple Constraints
 431 | <https://arxiv.org/abs/1805.02408>`_
 432 |     | :authors:`Boyang Ding, Quan Wang, Bin Wang, Li Guo`
 433 |     | :venue:`ACL 2018`
 434 | 
 435 | `SimplE Embedding for Link Prediction in Knowledge Graphs
 436 | <https://arxiv.org/abs/1802.04868>`_
 437 |     | :authors:`Seyed Mehran Kazemi, David Poole`
 438 |     | :venue:`NeurIPS 2018`
 439 | 
 440 | `A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network
 441 | <https://aclweb.org/anthology/papers/N/N18/N18-2053/>`_
 442 |     | :authors:`Dai Quoc Nguyen, Tu Dinh Nguyen, Dat Quoc Nguyen, Dinh Phung`
 443 |     | :venue:`NAACL 2018`
 444 | 
 445 | `Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning
 446 | <https://arxiv.org/abs/1903.08948>`_
 447 |     | :authors:`Wen Zhang, Bibek Paudel, Liang Wang, Jiaoyan Chen, Hai Zhu, Wei Zhang, Abraham Bernstein, Huajun Chen`
 448 |     | :venue:`WWW 2019`
 449 | 
 450 | `RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
 451 | <https://arxiv.org/abs/1902.10197>`_
 452 |     | :authors:`Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, Jian Tang`
 453 |     | :venue:`ICLR 2019`
 454 | 
 455 | `Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs
 456 | <https://arxiv.org/abs/1906.01195>`_
 457 |     | :authors:`Deepak Nathani, Jatin Chauhan, Charu Sharma, Manohar Kaul`
 458 |     | :venue:`ACL 2019`
 459 | 
 460 | `Probabilistic Logic Neural Networks for Reasoning
 461 | <https://arxiv.org/pdf/1906.08495.pdf>`_
 462 |     | :authors:`Meng Qu, Jian Tang`
 463 |     | :venue:`NeurIPS 2019`
 464 | 
 465 | `Quaternion Knowledge Graph Embeddings
 466 | <https://arxiv.org/pdf/1904.10281.pdf>`_
 467 |     | :authors:`Shuai Zhang, Yi Tay, Lina Yao, Qi Liu`
 468 |     | :venue:`NeurIPS 2019`
 469 | 
 470 | `Quantum Embedding of Knowledge for Reasoning
 471 | <https://papers.nips.cc/paper/8797-quantum-embedding-of-knowledge-for-reasoning.pdf>`_
 472 | 	| :authors:`Dinesh Garg, Santosh K. Srivastava, Hima Karanam`
 473 | 	| :venue:`NeurIPS 2019`
 474 | 	
 475 | `Multi-relational Poincaré Graph Embeddings
 476 | <https://arxiv.org/pdf/1905.09791.pdf>`_
 477 |     | :authors:`Ivana Balaževic, Carl Allen, Timothy Hospedales`
 478 |     | :venue:`NeurIPS 2019`
 479 | 
 480 | `Dynamically Pruned Message Passing Networks for Large-scale Knowledge Graph Reasoning
 481 | <https://openreview.net/forum?id=rkeuAhVKvB>`_
 482 | 	| :authors:`Xiaoran Xu, Wei Feng, Yunsheng Jiang, Xiaohui Xie, Zhiqing Sun, Zhi-Hong Deng`
 483 | 	| :venue:`ICLR 2020`
 484 | 	
 485 | Graph Neural Networks
 486 | =====================
 487 | 
 488 | `Revisiting Semi-supervised Learning with Graph Embeddings
 489 | <https://arxiv.org/pdf/1603.08861>`_
 490 |     | :authors:`Zhilin Yang, William W. Cohen, Ruslan Salakhutdinov`
 491 |     | :venue:`ICML 2016`
 492 | 
 493 | `Semi-Supervised Classification with Graph Convolutional Networks
 494 | <https://arxiv.org/pdf/1609.02907>`_
 495 |     | :authors:`Thomas N. Kipf, Max Welling`
 496 |     | :venue:`ICLR 2017`
 497 | 
 498 | `Neural Message Passing for Quantum Chemistry
 499 | <https://arxiv.org/pdf/1704.01212>`_
 500 |     | :authors:`Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, George E. Dahl`
 501 |     | :venue:`ICML 2017`
 502 | 
 503 | `Motif-Aware Graph Embeddings
 504 | <http://gearons.org/assets/docs/motif-aware-graph-final.pdf>`_
 505 |     | :authors:`Hoang Nguyen, Tsuyoshi Murata`
 506 |     | :venue:`IJCAI 2017`
 507 | 
 508 | `Learning Graph Representations with Embedding Propagation
 509 | <https://arxiv.org/pdf/1710.03059>`_
 510 |     | :authors:`Alberto Garcia-Duran, Mathias Niepert`
 511 |     | :venue:`NIPS 2017`
 512 | 
 513 | `Inductive Representation Learning on Large Graphs
 514 | <https://arxiv.org/pdf/1706.02216>`_
 515 |     | :authors:`William L. Hamilton, Rex Ying, Jure Leskovec`
 516 |     | :venue:`NIPS 2017`
 517 | 
 518 | `Graph Attention Networks
 519 | <https://arxiv.org/pdf/1710.10903>`_
 520 |     | :authors:`Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, Yoshua Bengio`
 521 |     | :venue:`ICLR 2018`
 522 | 
 523 | `FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling
 524 | <https://arxiv.org/pdf/1801.10247>`_
 525 |     | :authors:`Jie Chen, Tengfei Ma, Cao Xiao`
 526 |     | :venue:`ICLR 2018`
 527 | 
 528 | `Representation Learning on Graphs with Jumping Knowledge Networks
 529 | <https://arxiv.org/pdf/1806.03536>`_
 530 |     | :authors:`Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, Stefanie Jegelka`
 531 |     | :venue:`ICML 2018`
 532 | 
 533 | `Stochastic Training of Graph Convolutional Networks with Variance Reduction
 534 | <https://arxiv.org/pdf/1710.10568>`_
 535 |     | :authors:`Jianfei Chen, Jun Zhu, Le Song`
 536 |     | :venue:`ICML 2018`
 537 | 
 538 | `Large-Scale Learnable Graph Convolutional Networks
 539 | <https://arxiv.org/pdf/1808.03965>`_
 540 |     | :authors:`Hongyang Gao, Zhengyang Wang, Shuiwang Ji`
 541 |     | :venue:`KDD 2018`
 542 | 
 543 | `Adaptive Sampling Towards Fast Graph Representation Learning
 544 | <https://papers.nips.cc/paper/7707-adaptive-sampling-towards-fast-graph-representation-learning.pdf>`_
 545 |     | :authors:`Wenbing Huang, Tong Zhang, Yu Rong, Junzhou Huang`
 546 |     | :venue:`NeurIPS 2018`
 547 | 
 548 | `Hierarchical Graph Representation Learning with Differentiable Pooling
 549 | <https://arxiv.org/pdf/1806.08804>`_
 550 |     | :authors:`Rex Ying, Jiaxuan You, Christopher Morris, Xiang Ren, William L. Hamilton, Jure Leskovec`
 551 |     | :venue:`NeurIPS 2018`
 552 | 
 553 | `Bayesian Semi-supervised Learning with Graph Gaussian Processes
 554 | <https://papers.nips.cc/paper/7440-bayesian-semi-supervised-learning-with-graph-gaussian-processes.pdf>`_
 555 |     | :authors:`Yin Cheng Ng, Nicolò Colombo, Ricardo Silva`
 556 |     | :venue:`NeurIPS 2018`
 557 | 
 558 | `Pitfalls of Graph Neural Network Evaluation
 559 | <https://arxiv.org/pdf/1811.05868>`_
 560 |     | :authors:`Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, Stephan Günnemann`
 561 |     | :venue:`arXiv 2018`
 562 | 
 563 | `Heterogeneous Graph Attention Network
 564 | <https://arxiv.org/pdf/1903.07293>`_
 565 |     | :authors:`Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Peng Cui, P. Yu, Yanfang Ye`
 566 |     | :venue:`WWW 2019`
 567 | 
 568 | `Bayesian graph convolutional neural networks for semi-supervised classification
 569 | <https://arxiv.org/pdf/1811.11103.pdf>`_
 570 |     | :authors:`Yingxue Zhang, Soumyasundar Pal, Mark Coates, Deniz Üstebay`
 571 |     | :venue:`AAAI 2019`
 572 | 
 573 | `How Powerful are Graph Neural Networks?
 574 | <https://arxiv.org/pdf/1810.00826>`_
 575 |     | :authors:`Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka`
 576 |     | :venue:`ICLR 2019`
 577 | 
 578 | `LanczosNet: Multi-Scale Deep Graph Convolutional Networks
 579 | <https://arxiv.org/pdf/1901.01484>`_
 580 |     | :authors:`Renjie Liao, Zhizhen Zhao, Raquel Urtasun, Richard S. Zemel`
 581 |     | :venue:`ICLR 2019`
 582 | 
 583 | `Graph Wavelet Neural Network
 584 | <https://arxiv.org/pdf/1904.07785>`_
 585 |     | :authors:`Bingbing Xu, Huawei Shen, Qi Cao, Yunqi Qiu, Xueqi Cheng`
 586 |     | :venue:`ICLR 2019`
 587 | 
 588 | `Supervised Community Detection with Line Graph Neural Networks
 589 | <https://openreview.net/pdf?id=H1g0Z3A9Fm>`_
 590 |     | :authors:`Zhengdao Chen, Xiang Li, Joan Bruna`
 591 |     | :venue:`ICLR 2019`
 592 | 
 593 | `Predict then Propagate: Graph Neural Networks meet Personalized PageRank
 594 | <https://arxiv.org/pdf/1810.05997>`_
 595 |     | :authors:`Johannes Klicpera, Aleksandar Bojchevski, Stephan Günnemann`
 596 |     | :venue:`ICLR 2019`
 597 | 
 598 | `Invariant and Equivariant Graph Networks
 599 | <https://arxiv.org/pdf/1812.09902>`_
 600 |     | :authors:`Haggai Maron, Heli Ben-Hamu, Nadav Shamir, Yaron Lipman`
 601 |     | :venue:`ICLR 2019`
 602 | 
 603 | `Capsule Graph Neural Network
 604 | <https://openreview.net/pdf?id=Byl8BnRcYm>`_
 605 |     | :authors:`Zhang Xinyi, Lihui Chen`
 606 |     | :venue:`ICLR 2019`
 607 | 
 608 | `MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing
 609 | <https://arxiv.org/pdf/1905.00067>`_
 610 |     | :authors:`Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, Aram Galstyan`
 611 |     | :venue:`ICML 2019`
 612 | 
 613 | `Graph U-Nets
 614 | <https://arxiv.org/pdf/1905.05178>`_
 615 |     | :authors:`Hongyang Gao, Shuiwang Ji`
 616 |     | :venue:`ICML 2019`
 617 | 
 618 | `Disentangled Graph Convolutional Networks
 619 | <http://proceedings.mlr.press/v97/ma19a/ma19a.pdf>`_
 620 |     | :authors:`Jianxin Ma, Peng Cui, Kun Kuang, Xin Wang, Wenwu Zhu`
 621 |     | :venue:`ICML 2019`
 622 | 
 623 | `GMNN: Graph Markov Neural Networks
 624 | <https://arxiv.org/pdf/1905.06214>`_
 625 |     | :authors:`Meng Qu, Yoshua Bengio, Jian Tang`
 626 |     | :venue:`ICML 2019`
 627 | 
 628 | `Simplifying Graph Convolutional Networks
 629 | <https://arxiv.org/pdf/1902.07153>`_
 630 |     | :authors:`Felix Wu, Tianyi Zhang, Amauri Holanda de Souza Jr., Christopher Fifty, Tao Yu, Kilian Q. Weinberger`
 631 |     | :venue:`ICML 2019`
 632 | 
 633 | `Position-aware Graph Neural Networks
 634 | <https://arxiv.org/pdf/1906.04817>`_
 635 |     | :authors:`Jiaxuan You, Rex Ying, Jure Leskovec`
 636 |     | :venue:`ICML 2019`
 637 | 
 638 | `Self-Attention Graph Pooling
 639 | <https://arxiv.org/pdf/1904.08082>`_
 640 |     | :authors:`Junhyun Lee, Inyeop Lee, Jaewoo Kang`
 641 |     | :venue:`ICML 2019`
 642 | 
 643 | `Relational Pooling for Graph Representations
 644 | <https://arxiv.org/pdf/1903.02541>`_
 645 |     | :authors:`Ryan L. Murphy, Balasubramaniam Srinivasan, Vinayak Rao, Bruno Ribeiro`
 646 |     | :venue:`ICML 2019`
 647 | 
 648 | `Graph Representation Learning via Hard and Channel-Wise Attention Networks
 649 | <https://arxiv.org/pdf/1907.04652.pdf>`_
 650 |     | :authors:`Hongyang Gao, Shuiwang Ji`
 651 |     | :venue:`KDD 2019`
 652 | 
 653 | `Conditional Random Field Enhanced Graph Convolutional Neural Networks
 654 | <https://www.kdd.org/kdd2019/accepted-papers/view/conditional-random-field-enhanced-graph-convolutional-neural-networks>`_
 655 |     | :authors:`Hongchang Gao, Jian Pei, Heng Huang`
 656 |     | :venue:`KDD 2019`
 657 | 
 658 | `Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
 659 | <https://arxiv.org/abs/1905.07953>`_
 660 |     | :authors:`Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, Cho-Jui Hsieh`
 661 |     | :venue:`KDD 2019`
 662 | 
 663 | `DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification
 664 | <https://arxiv.org/abs/1906.02319>`_
 665 |     | :authors:`Jun Wu, Jingrui He, Jiejun Xu`
 666 |     | :venue:`KDD 2019`
 667 | 
 668 | `HetGNN: Heterogeneous Graph Neural Network
 669 | <https://www.kdd.org/kdd2019/accepted-papers/view/hetgnn-heterogeneous-graph-neural-network>`_
 670 |     | :authors:`Chuxu Zhang, Dongjin Song, Chao Huang, Ananthram Swami, Nitesh V. Chawla`
 671 |     | :venue:`KDD 2019`
 672 | 
 673 | `Graph Recurrent Networks with Attributed Random Walks
 674 | <https://dl.acm.org/citation.cfm?id=3292500.3330941>`_
 675 |     | :authors:`Xiao Huang, Qingquan Song, Yuening Li, Xia Hu`
 676 |     | :venue:`KDD 2019`
 677 | 
 678 | `Graph Convolutional Networks with EigenPooling
 679 | <https://arxiv.org/abs/1904.13107>`_
 680 |     | :authors:`Yao Ma, Suhang Wang, Charu Aggarwal, Jiliang Tang`
 681 |     | :venue:`KDD 2019`
 682 | 
 683 | `DFNets: Spectral CNNs for Graphs with Feedback-Looped Filters
 684 | <http://users.cecs.anu.edu.au/~u5170295/papers/nips-wijesinghe-2019.pdf>`_
 685 |     | :authors:`Asiri Wijesinghe, Qing Wang`
 686 |     | :venue:`NeurIPS 2019`
 687 | 
 688 | `Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology
 689 | <https://arxiv.org/pdf/1907.05008.pdf>`_
 690 |     | :authors:`Nima Dehmamy, Albert-László Barabási, Rose Yu`
 691 |     | :venue:`NeurIPS 2019`
 692 | 
 693 | `A Flexible Generative Framework for Graph-based Semi-supervised Learning
 694 | <https://arxiv.org/pdf/1905.10769.pdf>`_
 695 |     | :authors:`Jiaqi Ma, Weijing Tang, Ji Zhu, Qiaozhu Mei`
 696 |     | :venue:`NeurIPS 2019`
 697 | 
 698 | `Rethinking Kernel Methods for Node Representation Learning on Graphs
 699 | <https://arxiv.org/pdf/1910.02548.pdf>`_
 700 |     | :authors:`Yu Tian, Long Zhao, Xi Peng, Dimitris N. Metaxas`
 701 |     | :venue:`NeurIPS 2019`
 702 | 
 703 | `Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
 704 | <https://arxiv.org/pdf/1906.02174.pdf>`_
 705 |     | :authors:`Sitao Luan, Mingde Zhao, Xiao-Wen Chang, Doina Precup`
 706 |     | :venue:`NeurIPS 2019`
 707 | 
 708 | `N-Gram Graph: A Simple Unsupervised Representation for Molecules
 709 | <https://arxiv.org/pdf/1806.09206.pdf>`_
 710 |     | :authors:`Shengchao Liu, Thevaa Chandereng, Yingyu Liang`
 711 |     | :venue:`NeurIPS 2019`
 712 | 
 713 | `DeepGCNs: Can GCNs Go as Deep as CNNs?
 714 | <https://arxiv.org/pdf/1904.03751.pdf>`_
 715 |     | :authors:`Guohao Li, Matthias Muller, Ali Thabet, Bernard Ghanem`
 716 |     | :venue:`ICCV 2019`
 717 | 
 718 | `Continuous Graph Neural Networks
 719 | <https://arxiv.org/pdf/1912.00967.pdf>`_
 720 |     | :authors:`Louis-Pascal A. C. Xhonneux, Meng Qu, Jian Tang`
 721 |     | :venue:`arXiv 2019`
 722 | 
 723 | `Curvature Graph Network
 724 | <https://openreview.net/pdf?id=BylEqnVFDB>`_
 725 | 	| :authors:`Ze Ye, Kin Sum Liu, Tengfei Ma, Jie Gao, Chao Chen`
 726 | 	| :venue:`ICLR 2020`
 727 | 
 728 | `Memory-based Graph Networks
 729 | <https://openreview.net/pdf?id=r1laNeBYPB>`_
 730 | 	| :authors:`Amir hosein Khasahmadi, Kaveh Hassani, Parsa Moradi, Leo Lee, Quaid Morris`
 731 | 	| :venue:`ICLR 2020`
 732 | 	
 733 | `Strategies for Pre-training Graph Neural Networks
 734 | <https://openreview.net/pdf?id=HJlWWJSFDH>`_
 735 | 	| :authors:`Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, Jure Leskovec`
 736 | 	| :venue:`ICLR 2020`
 737 | 
 738 | Applications of Graph Deep Learning
 739 | =================================
 740 | 
 741 | Natural Language Processing
 742 | ---------------------------
 743 | 
 744 | `Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling
 745 | <https://www.aclweb.org/anthology/D17-1159>`_
 746 |     | :authors:`Diego Marcheggiani, Ivan Titov`
 747 |     | :venue:`EMNLP 2017`
 748 | 
 749 | `Graph Convolutional Encoders for Syntax-aware Neural Machine Translation
 750 | <https://www.aclweb.org/anthology/D17-1209>`_
 751 |     | :authors:`Joost Bastings, Ivan Titov, Wilker Aziz, Diego Marcheggiani, Khalil Sima’an`
 752 |     | :venue:`EMNLP 2017`
 753 | 
 754 | `Graph-based Neural Multi-Document Summarization
 755 | <https://www.aclweb.org/anthology/K17-1045>`_
 756 |     | :authors:`Michihiro Yasunaga, Rui Zhang, Kshitijh Meelu, Ayush Pareek, Krishnan Srinivasan, Dragomir Radev`
 757 |     | :venue:`CoNLL 2017`
 758 | 
 759 | `QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension
 760 | <https://arxiv.org/pdf/1804.09541.pdf>`_
 761 |     | :authors:`Adams Wei Yu, David Dohan, Minh-Thang Luong, Rui Zhao, Kai Chen, Mohammad Norouzi, Quoc V. Le`
 762 |     | :venue:`ICLR 2018`
 763 | 
 764 | `A Structured Self-attentive Sentence Embedding
 765 | <https://arxiv.org/pdf/1703.03130.pdf>`_
 766 |     | :authors:`Zhouhan Lin, Minwei Feng, Cicero Nogueira dos Santos, Mo Yu, Bing Xiang, Bowen Zhou, Yoshua Bengio`
 767 |     | :venue:`ICLR 2018`
 768 | 
 769 | `Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering
 770 | <https://aclweb.org/anthology/C18-1280>`_
 771 |     | :authors:`Daniil Sorokin, Iryna Gurevych`
 772 |     | :venue:`COLING 2018`
 773 | 
 774 | `Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks
 775 | <https://www.aclweb.org/anthology/N18-2078>`_
 776 |     | :authors:`Diego Marcheggiani, Joost Bastings, Ivan Titov`
 777 |     | :venue:`NAACL 2018`
 778 | 
 779 | `Linguistically-Informed Self-Attention for Semantic Role Labeling
 780 | <https://www.aclweb.org/anthology/D18-1548>`_
 781 |     | :authors:`Emma Strubell, Patrick Verga, Daniel Andor, David Weiss, Andrew McCallum`
 782 |     | :venue:`EMNLP 2018`
 783 | 
 784 | `Graph Convolution over Pruned Dependency Trees Improves Relation Extraction
 785 | <https://aclweb.org/anthology/D18-1244>`_
 786 |     | :authors:`Yuhao Zhang, Peng Qi, Christopher D. Manning`
 787 |     | :venue:`EMNLP 2018`
 788 | 
 789 | `A Graph-to-Sequence Model for AMR-to-Text Generation
 790 | <https://www.aclweb.org/anthology/P18-1150>`_
 791 |     | :authors:`Linfeng Song, Yue Zhang, Zhiguo Wang, Daniel Gildea`
 792 |     | :venue:`ACL 2018`
 793 | 
 794 | `Graph-to-Sequence Learning using Gated Graph Neural Networks
 795 | <https://www.aclweb.org/anthology/P18-1026>`_
 796 |     | :authors:`Daniel Beck, Gholamreza Haffari, Trevor Cohn`
 797 |     | :venue:`ACL 2018`
 798 | 
 799 | `Graph Convolutional Networks for Text Classification
 800 | <https://arxiv.org/pdf/1809.05679.pdf>`_
 801 |     | :authors:`Liang Yao, Chengsheng Mao, Yuan Luo`
 802 |     | :venue:`AAAI 2019`
 803 | 
 804 | `Differentiable Perturb-and-Parse: Semi-Supervised Parsing with a Structured Variational Autoencoder
 805 | <https://openreview.net/pdf?id=BJlgNh0qKQ>`_
 806 |     | :authors:`Caio Corro, Ivan Titov`
 807 |     | :venue:`ICLR 2019`
 808 | 
 809 | `Structured Neural Summarization
 810 | <https://arxiv.org/pdf/1811.01824.pdf>`_
 811 |     | :authors:`Patrick Fernandes, Miltiadis Allamanis, Marc Brockschmid`
 812 |     | :venue:`ICLR 2019`
 813 | 
 814 | `Multi-task Learning over Graph Structures
 815 | <https://arxiv.org/pdf/1811.10211.pdf>`_
 816 |     | :authors:`Pengfei Liu, Jie Fu, Yue Dong, Xipeng Qiu, Jackie Chi Kit Cheung`
 817 |     | :venue:`AAAI 2019`
 818 | 
 819 | `Imposing Label-Relational Inductive Bias for Extremely Fine-Grained Entity Typing
 820 | <https://arxiv.org/pdf/1903.02591.pdf>`_
 821 |     | :authors:`Wenhan Xiong, Jiawei Wu, Deren Lei, Mo Yu, Shiyu Chang, Xiaoxiao Guo, William Yang Wang`
 822 |     | :venue:`NAACL 2019`
 823 | 
 824 | `Single Document Summarization as Tree Induction
 825 | <https://www.aclweb.org/anthology/N19-1173>`_
 826 |     | :authors:`Yang Liu, Ivan Titov, Mirella Lapata`
 827 |     | :venue:`NAACL 2019`
 828 | 
 829 | `Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks
 830 | <https://arxiv.org/pdf/1903.01306.pdf>`_
 831 |     | :authors:`Ningyu Zhang, Shumin Deng, Zhanlin Sun, Guanying Wang, Xi Chen, Wei Zhang, Huajun Chen`
 832 |     | :venue:`NAACL 2019`
 833 | 
 834 | `Graph Neural Networks with Generated Parameters for Relation Extraction
 835 | <https://arxiv.org/pdf/1902.00756.pdf>`_
 836 |     | :authors:`Hao Zhu, Yankai Lin, Zhiyuan Liu, Jie Fu, Tat-seng Chua, Maosong Sun`
 837 |     | :venue:`ACL 2019`
 838 | 
 839 | `Dynamically Fused Graph Network for Multi-hop Reasoning
 840 | <https://arxiv.org/pdf/1905.06933.pdf>`_
 841 |     | :authors:`Yunxuan Xiao, Yanru Qu, Lin Qiu, Hao Zhou, Lei Li, Weinan Zhang, Yong Yu`
 842 |     | :venue:`ACL 2019`
 843 | 
 844 | `Encoding Social Information with Graph Convolutional Networks for Political Perspective Detection
 845 | in News Media
 846 | <https://www.cs.purdue.edu/homes/dgoldwas//downloads/papers/LiG_acl_2019.pdf>`_
 847 |     | :authors:`Chang Li, Dan Goldwasser`
 848 |     | :venue:`ACL 2019`
 849 | 
 850 | `Attention Guided Graph Convolutional Networks for Relation Extraction
 851 | <https://arxiv.org/pdf/1906.07510.pdf>`_
 852 |     | :authors:`Zhijiang Guo, Yan Zhang, Wei Lu`
 853 |     | :venue:`ACL 2019`
 854 | 
 855 | `Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks
 856 | <https://arxiv.org/pdf/1809.04283.pdf>`_
 857 |     | :authors:`Shikhar Vashishth, Manik Bhandari, Prateek Yadav, Piyush Rai, Chiranjib Bhattacharyya, Partha Talukdar`
 858 |     | :venue:`ACL 2019`
 859 | 
 860 | `GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction
 861 | <https://tsujuifu.github.io/pubs/acl19_graph-rel.pdf>`_
 862 |     | :authors:`Tsu-Jui Fu, Peng-Hsuan Li, Wei-Yun Ma`
 863 |     | :venue:`ACL 2019`
 864 | 
 865 | `Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs
 866 | <https://arxiv.org/pdf/1905.07374.pdf>`_
 867 |     | :authors:`Ming Tu, Guangtao Wang, Jing Huang, Yun Tang, Xiaodong He, Bowen Zhou`
 868 |     | :venue:`ACL 2019`
 869 | 
 870 | `Cognitive Graph for Multi-Hop Reading Comprehension at Scale
 871 | <https://arxiv.org/pdf/1905.05460.pdf>`_
 872 |     | :authors:`Ming Ding, Chang Zhou, Qibin Chen, Hongxia Yang, Jie Tang`
 873 |     | :venue:`ACL 2019`
 874 | 
 875 | `Coherent Comment Generation for Chinese Articles with a Graph-to-Sequence Model
 876 | <https://arxiv.org/pdf/1906.01231.pdf>`_
 877 |     | :authors:`Wei Li, Jingjing Xu, Yancheng He, Shengli Yan, Yunfang Wu, Xu Sun`
 878 |     | :venue:`ACL 2019`
 879 | 
 880 | `Matching Article Pairs with Graphical Decomposition and Convolutions
 881 | <https://arxiv.org/pdf/1802.07459.pdf>`_
 882 |     | :authors:`Bang Liu, Di Niu, Haojie Wei, Jinghong Lin, Yancheng He, Kunfeng Lai, Yu Xu`
 883 |     | :venue:`ACL 2019`
 884 | 
 885 | `Embedding Imputation with Grounded Language Information
 886 | <https://arxiv.org/pdf/1906.03753.pdf>`_
 887 |     | :authors:`Ziyi Yang, Chenguang Zhu, Vin Sachidananda, Eric Darve`
 888 |     | :venue:`ACL 2019`
 889 | 
 890 | `Encoding Social Information with Graph Convolutional Networks forPolitical Perspective Detection in News Media
 891 | <https://www.aclweb.org/anthology/P19-1247.pdf>`_
 892 |     | :authors:`Chang Li, Dan Goldwasser`
 893 |     | :venue:`ACL 2019`
 894 | 
 895 | `A Neural Multi-digraph Model for Chinese NER with Gazetteers
 896 | <https://www.aclweb.org/anthology/P19-1141.pdf>`_
 897 |     | :authors:`Ruixue Ding, Pengjun Xie, Xiaoyan Zhang, Wei Lu, Linlin Li, Luo Si`
 898 |     | :venue:`ACL 2019`
 899 | 
 900 | `Tree Communication Models for Sentiment Analysis
 901 | <https://www.aclweb.org/anthology/P19-1342.pdf>`_
 902 |     | :authors:`Yuan Zhang, Yue Zhang`
 903 |     | :venue:`ACL 2019`
 904 | 
 905 | `A2N: Attending to Neighbors for Knowledge Graph Inference
 906 | <https://www.aclweb.org/anthology/P19-1431.pdf>`_
 907 |     | :authors:`Trapit Bansal, Da-Cheng Juan, Sujith Ravi, Andrew McCallum`
 908 |     | :venue:`ACL 2019`
 909 | 
 910 | `Textbook Question Answering with Multi-modal Context Graph Understanding and Self-supervised Open-set Comprehension
 911 | <https://www.aclweb.org/anthology/P19-1347.pdf>`_
 912 |     | :authors:`Daesik Kim, Seonhoon Kim, Nojun Kwak`
 913 |     | :venue:`ACL 2019`
 914 | 
 915 | `Look Again at the Syntax: Relational Graph Convolutional Network for Gendered Ambiguous Pronoun Resolution
 916 | <https://arxiv.org/pdf/1905.08868.pdf>`_
 917 |     | :authors:`Yinchuan Xu, Junlin Yang`
 918 |     | :venue:`ACL 2019 Workshop`
 919 |     | :keywords:`https://github.com/ianycxu/RGCN-with-BERT`
 920 | 
 921 | `Learning Graph Pooling and Hybrid Convolutional Operations for Text Representations
 922 | <https://arxiv.org/pdf/1901.06965.pdf>`_
 923 |     | :authors:`Hongyang Gao, Yongjun Chen, Shuiwang Ji`
 924 |     | :venue:`WWW 2019`
 925 | 
 926 | `Learning to Create Sentence Semantic Relation Graphs for Multi-Document Summarization
 927 | <https://arxiv.org/pdf/1909.12231.pdf>`_
 928 |     | :authors:`Diego Antognini, Boi Faltings`
 929 |     | :venue:`EMNLP 2019`
 930 | 
 931 | `Dependency-Guided LSTM-CRF for Named Entity Recognition
 932 | <https://arxiv.org/pdf/1909.10148.pdf>`_
 933 |     | :authors:`Zhanming Jie, Wei Lu`
 934 |     | :venue:`EMNLP 2019`
 935 | 
 936 | `Modeling Conversation Structure and Temporal Dynamics for Jointly Predicting Rumor Stance and Veracity
 937 | <https://arxiv.org/pdf/1909.08211.pdf>`_
 938 |     | :authors:`Penghui Wei, Nan Xu, Wenji Mao`
 939 |     | :venue:`EMNLP 2019`
 940 | 
 941 | `DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation
 942 | <https://arxiv.org/pdf/1908.11540.pdf>`_
 943 |     | :authors:`Deepanway Ghosal, Navonil Majumder, Soujanya Poria, Niyati Chhaya, Alexander Gelbukh`
 944 |     | :venue:`EMNLP 2019`
 945 | 
 946 | `Modeling Graph Structure in Transformer for Better AMR-to-Text Generation
 947 | <https://arxiv.org/pdf/1909.00136.pdf>`_
 948 |     | :authors:`Jie Zhu, Junhui Li, Muhua Zhu, Longhua Qian, Min Zhang, Guodong Zhou`
 949 |     | :venue:`EMNLP 2019`
 950 | 
 951 | `KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning
 952 | <https://arxiv.org/pdf/1909.02151.pdf>`_
 953 |     | :authors:`Bill Yuchen Lin, Xinyue Chen, Jamin Chen, Xiang Ren`
 954 |     | :venue:`EMNLP 2019`
 955 | 
 956 | Computer Vision
 957 | ---------------
 958 | 
 959 | `3D Graph Neural Networks for RGBD Semantic Segmentation
 960 | <http://www.cs.toronto.edu/~rjliao/papers/iccv_2017_3DGNN.pdf>`_
 961 |     | :authors:`Xiaojuan Qi, Renjie Liao, Jiaya Jia, Sanja Fidler, Raquel Urtasun`
 962 |     | :venue:`ICCV 2017`
 963 | 
 964 | `Situation Recognition With Graph Neural Networks
 965 | <https://arxiv.org/abs/1708.04320>`_
 966 |     | :authors:`Ruiyu Li, Makarand Tapaswi, Renjie Liao, Jiaya Jia, Raquel Urtasun, Sanja Fidler`
 967 |     | :venue:`ICCV 2017`
 968 | 
 969 | `Graph-Based Classification of Omnidirectional Images
 970 | <https://arxiv.org/abs/1707.08301>`_
 971 |     | :authors:`Renata Khasanova, Pascal Frossard`
 972 |     | :venue:`ICCV 2017`
 973 | 
 974 | `Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
 975 | <https://arxiv.org/abs/1801.07455>`_
 976 |     | :authors:`Sijie Yan, Yuanjun Xiong, Dahua Lin`
 977 |     | :venue:`AAAI 2018`
 978 | 
 979 | `Image Generation from Scene Graphs
 980 | <https://arxiv.org/abs/1804.01622>`_
 981 |     | :authors:`Justin Johnson, Agrim Gupta, Li Fei-Fei`
 982 |     | :venue:`CVPR 2018`
 983 | 
 984 | `FoldingNet: Point Cloud Auto-Encoder via Deep Grid Deformation
 985 | <https://arxiv.org/abs/1712.07262>`_
 986 |     | :authors:`Yaoqing Yang, Chen Feng, Yiru Shen, Dong Tian`
 987 |     | :venue:`CVPR 2018`
 988 | 
 989 | `PPFNet: Global Context Aware Local Features for Robust 3D Point Matching
 990 | <https://arxiv.org/abs/1802.02669>`_
 991 |     | :authors:`Haowen Deng, Tolga Birdal, Slobodan Ilic`
 992 |     | :venue:`CVPR 2018`
 993 | 
 994 | `Iterative Visual Reasoning Beyond Convolutions
 995 | <https://arxiv.org/abs/1803.11189>`_
 996 |     | :authors:`Xinlei Chen, Li-Jia Li, Li Fei-Fei, Abhinav Gupta`
 997 |     | :venue:`CVPR 2018`
 998 | 
 999 | `Surface Networks
1000 | <https://arxiv.org/abs/1705.10819>`_
1001 |     | :authors:`Ilya Kostrikov, Zhongshi Jiang, Daniele Panozzo, Denis Zorin, Joan Bruna`
1002 |     | :venue:`CVPR 2018`
1003 | 
1004 | `FeaStNet: Feature-Steered Graph Convolutions for 3D Shape Analysis
1005 | <https://arxiv.org/abs/1706.05206>`_
1006 |     | :authors:`Nitika Verma, Edmond Boyer, Jakob Verbeek`
1007 |     | :venue:`CVPR 2018`
1008 | 
1009 | `Learning to Act Properly: Predicting and Explaining Affordances From Images
1010 | <https://arxiv.org/abs/1712.07576>`_
1011 |     | :authors:`Ching-Yao Chuang, Jiaman Li, Antonio Torralba, Sanja Fidler`
1012 |     | :venue:`CVPR 2018`
1013 | 
1014 | `Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling
1015 | <https://arxiv.org/abs/1712.06760>`_
1016 |     | :authors:`Yiru Shen, Chen Feng, Yaoqing Yang, Dong Tian`
1017 |     | :venue:`CVPR 2018`
1018 | 
1019 | `Deformable Shape Completion With Graph Convolutional Autoencoders
1020 | <https://arxiv.org/abs/1712.00268>`_
1021 |     | :authors:`Or Litany, Alex Bronstein, Michael Bronstein, Ameesh Makadia`
1022 |     | :venue:`CVPR 2018`
1023 | 
1024 | `Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images
1025 | <https://arxiv.org/abs/1804.01654>`_
1026 |     | :authors:`Nanyang Wang, Yinda Zhang, Zhuwen Li, Yanwei Fu, Wei Liu, Yu-Gang Jiang`
1027 |     | :venue:`ECCV 2018`
1028 | 
1029 | `Learning Human-Object Interactions by Graph Parsing Neural Networks
1030 | <https://arxiv.org/abs/1808.07962>`_
1031 |     | :authors:`Siyuan Qi, Wenguan Wang, Baoxiong Jia, Jianbing Shen, Song-Chun Zhu`
1032 |     | :venue:`ECCV 2018`
1033 | 
1034 | `Generating 3D Faces using Convolutional Mesh Autoencoders
1035 | <https://arxiv.org/abs/1807.10267>`_
1036 |     | :authors:`Anurag Ranjan, Timo Bolkart, Soubhik Sanyal, Michael J. Black`
1037 |     | :venue:`ECCV 2018`
1038 | 
1039 | `Learning SO(3) Equivariant Representations with Spherical CNNs
1040 | <https://arxiv.org/abs/1711.06721>`_
1041 |     | :authors:`Carlos Esteves, Christine Allen-Blanchette, Ameesh Makadia, Kostas Daniilidis`
1042 |     | :venue:`ECCV 2018`
1043 | 
1044 | `Neural Graph Matching Networks for Fewshot 3D Action Recognition
1045 | <http://openaccess.thecvf.com/content_ECCV_2018/papers/Michelle_Guo_Neural_Graph_Matching_ECCV_2018_paper.pdf>`_
1046 |     | :authors:`Michelle Guo, Edward Chou, De-An Huang, Shuran Song, Serena Yeung, Li Fei-Fei`
1047 |     | :venue:`ECCV 2018`
1048 | 
1049 | `Multi-Kernel Diffusion CNNs for Graph-Based Learning on Point Clouds
1050 | <https://arxiv.org/abs/1809.05370>`_
1051 |     | :authors:`Lasse Hansen, Jasper Diesel, Mattias P. Heinrich`
1052 |     | :venue:`ECCV 2018`
1053 | 
1054 | `Hierarchical Video Frame Sequence Representation with Deep Convolutional Graph Network
1055 | <https://arxiv.org/abs/1906.00377>`_
1056 |     | :authors:`Feng Mao, Xiang Wu, Hui Xue, Rong Zhang`
1057 |     | :venue:`ECCV 2018`
1058 | 
1059 | `Graph R-CNN for Scene Graph Generation
1060 | <https://arxiv.org/abs/1808.00191>`_
1061 |     | :authors:`Jianwei Yang, Jiasen Lu, Stefan Lee, Dhruv Batra, Devi Parikh`
1062 |     | :venue:`ECCV 2018`
1063 | 
1064 | `Exploring Visual Relationship for Image Captioning
1065 | <https://arxiv.org/abs/1809.07041>`_
1066 |     | :authors:`Ting Yao, Yingwei Pan, Yehao Li, Tao Mei`
1067 |     | :venue:`ECCV 2018`
1068 | 
1069 | `Beyond Grids: Learning Graph Representations for Visual Recognition
1070 | <https://papers.nips.cc/paper/8135-beyond-grids-learning-graph-representations-for-visual-recognition>`_
1071 |     | :authors:`Yin Li, Abhinav Gupta`
1072 |     | :venue:`NeurIPS 2018`
1073 | 
1074 | `Learning Conditioned Graph Structures for Interpretable Visual Question Answering
1075 | <https://arxiv.org/abs/1806.07243>`_
1076 |     | :authors:`Will Norcliffe-Brown, Efstathios Vafeias, Sarah Parisot`
1077 |     | :venue:`NeurIPS 2018`
1078 | 
1079 | `LinkNet: Relational Embedding for Scene Graph
1080 | <https://arxiv.org/abs/1811.06410>`_
1081 |     | :authors:`Sanghyun Woo, Dahun Kim, Donghyeon Cho, In So Kweon`
1082 |     | :venue:`NeurIPS 2018`
1083 | 
1084 | `Flexible Neural Representation for Physics Prediction
1085 | <https://arxiv.org/abs/1806.08047>`_
1086 |     | :authors:`Damian Mrowca, Chengxu Zhuang, Elias Wang, Nick Haber, Li Fei-Fei, Joshua B. Tenenbaum, Daniel L. K. Yamins`
1087 |     | :venue:`NeurIPS 2018`
1088 | 
1089 | `Learning Localized Generative Models for 3D Point Clouds via Graph Convolution
1090 | <https://openreview.net/forum?id=SJeXSo09FQ>`_
1091 |     | :authors:`Diego Valsesia, Giulia Fracastoro, Enrico Magli`
1092 |     | :venue:`ICLR 2019`
1093 | 
1094 | `Graph-Based Global Reasoning Networks
1095 | <https://arxiv.org/abs/1811.12814>`_
1096 |     | :authors:`Yunpeng Chen, Marcus Rohrbach, Zhicheng Yan, Shuicheng Yan, Jiashi Feng, Yannis Kalantidis`
1097 |     | :venue:`CVPR 2019`
1098 | 
1099 | `Deep Graph Laplacian Regularization for Robust Denoising of Real Images
1100 | <https://arxiv.org/abs/1807.11637>`_
1101 |     | :authors:`Jin Zeng, Jiahao Pang, Wenxiu Sun, Gene Cheung`
1102 |     | :venue:`CVPR 2019`
1103 | 
1104 | `Learning Context Graph for Person Search
1105 | <https://arxiv.org/abs/1904.01830>`_
1106 |     | :authors:`Yichao Yan, Qiang Zhang, Bingbing Ni, Wendong Zhang, Minghao Xu, Xiaokang Yang`
1107 |     | :venue:`CVPR 2019`
1108 | 
1109 | `Graphonomy: Universal Human Parsing via Graph Transfer Learning
1110 | <https://arxiv.org/abs/1904.04536>`_
1111 |     | :authors:`Ke Gong, Yiming Gao, Xiaodan Liang, Xiaohui Shen, Meng Wang, Liang Lin`
1112 |     | :venue:`CVPR 2019`
1113 | 
1114 | `Masked Graph Attention Network for Person Re-Identification
1115 | <http://openaccess.thecvf.com/content_CVPRW_2019/papers/TRMTMCT/Bao_Masked_Graph_Attention_Network_for_Person_Re-Identification_CVPRW_2019_paper.pdf>`_
1116 | for_Person_Re-Identification_CVPRW_2019_paper.html>`_
1117 |     | :authors:`Liqiang Bao, Bingpeng Ma, Hong Chang, Xilin Chen`
1118 |     | :venue:`CVPR 2019`
1119 | 
1120 | `Learning to Cluster Faces on an Affinity Graph
1121 | <https://arxiv.org/abs/1904.02749>`_
1122 |     | :authors:`Lei Yang, Xiaohang Zhan, Dapeng Chen, Junjie Yan, Chen Change Loy, Dahua Lin`
1123 |     | :venue:`CVPR 2019`
1124 | 
1125 | `Actional-Structural Graph Convolutional Networks for Skeleton-Based Action Recognition
1126 | <https://arxiv.org/abs/1904.12659>`_
1127 |     | :authors:`Maosen Li, Siheng Chen, Xu Chen, Ya Zhang, Yanfeng Wang, Qi Tian`
1128 |     | :venue:`CVPR 2019`
1129 | 
1130 | `Adaptively Connected Neural Networks
1131 | <https://arxiv.org/abs/1904.03579>`_
1132 |     | :authors:`Guangrun Wang, Keze Wang, Liang Lin`
1133 |     | :venue:`CVPR 2019`
1134 | 
1135 | `Reasoning Visual Dialogs with Structural and Partial Observations
1136 | <https://arxiv.org/abs/1904.03579>`_
1137 |     | :authors:`Zilong Zheng, Wenguan Wang, Siyuan Qi, Song-Chun Zhu`
1138 |     | :venue:`CVPR 2019`
1139 | 
1140 | `MeshCNN: A Network with an Edge
1141 | <https://arxiv.org/pdf/1809.05910.pdf>`_
1142 |     | :authors:`Rana Hanocka, Amir Hertz, Noa Fish, Raja Giryes, Shachar Fleishman, Daniel Cohen-Or`
1143 |     | :venue:`SIGGRAPH 2019`
1144 |     | :keywords:`https://ranahanocka.github.io/MeshCNN/`
1145 | 
1146 | `Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learning
1147 | <https://arxiv.org/pdf/1908.02441.pdf>`_
1148 |     | :authors:`Jiwoong Park, Minsik Lee, Hyung Jin Chang, Kyuewang Lee, Jin Young Choi`
1149 |     | :venue:`ICCV 2019`
1150 | 
1151 | `Pixel2Mesh++: Multi-View 3D Mesh Generation via Deformation
1152 | <https://arxiv.org/pdf/1908.01491.pdf>`_
1153 |     | :authors:`Chao Wen, Yinda Zhang, Zhuwen Li, Yanwei Fu`
1154 |     | :venue:`ICCV 2019`
1155 | 
1156 | `Learning Trajectory Dependencies for Human Motion Prediction
1157 | <https://arxiv.org/pdf/1908.05436.pdf>`_
1158 |     | :authors:`Wei Mao, Miaomiao Liu, Mathieu Salzmann, Hongdong Li`
1159 |     | :venue:`ICCV 2019`
1160 | 
1161 | `Graph-Based Object Classification for Neuromorphic Vision Sensing
1162 | <https://arxiv.org/pdf/1908.06648.pdf>`_
1163 |     | :authors:`Yin Bi, Aaron Chadha, Alhabib Abbas, Eirina Bourtsoulatze, Yiannis Andreopoulos`
1164 |     | :venue:`ICCV 2019`
1165 | 
1166 | `Fashion Retrieval via Graph Reasoning Networks on a Similarity Pyramid
1167 | <https://arxiv.org/pdf/1908.11754.pdf>`_
1168 |     | :authors:`Zhanghui Kuang, Yiming Gao, Guanbin Li, Ping Luo, Yimin Chen, Liang Lin, Wayne Zhang`
1169 |     | :venue:`ICCV 2019`
1170 | 
1171 | `Understanding Human Gaze Communication by Spatio-Temporal Graph Reasoning
1172 | <https://arxiv.org/pdf/1909.02144.pdf>`_
1173 |     | :authors:`Lifeng Fan, Wenguan Wang, Siyuan Huang, Xinyu Tang, Song-Chun Zhu`
1174 |     | :venue:`ICCV 2019`
1175 | 
1176 | `Visual Semantic Reasoning for Image-Text Matching
1177 | <https://arxiv.org/pdf/1909.02701.pdf>`_
1178 |     | :authors:`Kunpeng Li, Yulun Zhang, Kai Li, Yuanyuan Li, Yun Fu`
1179 |     | :venue:`ICCV 2019`
1180 | 
1181 | `Graph Convolutional Networks for Temporal Action Localization
1182 | <https://arxiv.org/pdf/1909.03252.pdf>`_
1183 |     | :authors:`Runhao Zeng, Wenbing Huang, Mingkui Tan, Yu Rong, Peilin Zhao, Junzhou Huang, Chuang Gan`
1184 |     | :venue:`ICCV 2019`
1185 | 
1186 | `Semantically-Regularized Logic Graph Embeddings
1187 | <https://arxiv.org/pdf/1909.01161.pdf>`_
1188 |     | :authors:`Yaqi Xie, Ziwei Xu, Kuldeep Meel, Mohan S Kankanhalli, Harold Soh`
1189 |     | :venue:`NeurIPS 2019`
1190 | 
1191 | Recommender Systems
1192 | -------------------
1193 | 
1194 | `Graph Convolutional Neural Networks for Web-Scale Recommender Systems
1195 | <https://arxiv.org/pdf/1806.01973.pdf>`_
1196 |     | :authors:`Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, Jure Leskovec`
1197 |     | :venue:`KDD 2018`
1198 |     | :keywords:`PinSage`
1199 | 
1200 | `SocialGCN: An Efficient Graph Convolutional Network based Model for Social Recommendation
1201 | <https://arxiv.org/pdf/1811.02815.pdf>`_
1202 |     | :authors:`Le Wu, Peijie Sun, Richang Hong, Yanjie Fu, Xiting Wang, Meng Wang`
1203 |     | :venue:`AAAI 2018`
1204 |     | :keywords:`GCN, Social recommendation`
1205 | 
1206 | `Session-based Social Recommendation via Dynamic Graph Attention Networks
1207 | <https://arxiv.org/pdf/1902.09362.pdf>`_
1208 |     | :authors:`Weiping Song, Zhiping Xiao, Yifan Wang, Laurent Charlin, Ming Zhang, Jian Tang`
1209 |     | :venue:`WSDM 2019`
1210 |     | :keywords:`Social recommendation, session-based, GAT`
1211 | 
1212 | `Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in
1213 | Recommender Systems
1214 | <https://arxiv.org/pdf/1903.10433.pdf>`_
1215 |     | :authors:`Qitian Wu, Hengrui Zhang, Xiaofeng Gao, Peng He, Paul Weng, Han Gao, Guihai Chen`
1216 |     | :venue:`WWW 2019`
1217 |     | :keywords:`Social recommendation, GAT`
1218 | 
1219 | `Graph Neural Networks for Social Recommendation
1220 | <https://arxiv.org/pdf/1902.07243.pdf>`_
1221 |     | :authors:`Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, Dawei Yin`
1222 |     | :venue:`WWW 2019`
1223 |     | :keywords:`Social recommendation, GNN`
1224 | 
1225 | `Session-based Recommendation with Graph Neural Networks
1226 | <https://arxiv.org/pdf/1811.00855.pdf>`_
1227 |     | :authors:`Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, Tieniu Tan`
1228 |     | :venue:`AAAI 2019`
1229 |     | :keywords:`Session-based recommendation, GNN`
1230 | 
1231 | `A Neural Influence Diffusion Model for Social Recommendation
1232 | <https://arxiv.org/pdf/1904.10322.pdf>`_
1233 |     | :authors:`Le Wu, Peijie Sun, Yanjie Fu, Richang Hong, Xiting Wang, Meng Wang`
1234 |     | :venue:`SIGIR 2019`
1235 |     | :keywords:`Social Recommendation, diffusion`
1236 | 
1237 | `Neural Graph Collaborative Filtering
1238 | <https://arxiv.org/pdf/1905.08108.pdf>`_
1239 |     | :authors:`Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, Tat-Seng Chua`
1240 |     | :venue:`SIGIR 2019`
1241 |     | :keywords:`Collaborative Filtering, GNN`
1242 | 
1243 | `Binarized Collaborative Filtering with Distilling Graph Convolutional Networks
1244 | <https://arxiv.org/pdf/1906.01829.pdf>`_
1245 |     | :authors:`Haoyu Wang, Defu Lian, Yong Ge`
1246 |     | :venue:`IJCAI 2019`
1247 | 
1248 | `IntentGC: A Scalable Graph Convolution Framework Fusing Heterogeneous Information for Recommendation
1249 | <https://dl.acm.org/citation.cfm?id=3330686>`_
1250 |     | :authors:`Jun Zhao, Zhou Zhou, Ziyu Guan, Wei Zhao, Wei Ning, Guang Qiu, Xiaofei He`
1251 |     | :venue:`KDD 2019`
1252 | 
1253 | `An End-to-End Neighborhood-based Interaction Model for Knowledge-enhanced Recommendation
1254 | <https://arxiv.org/pdf/1908.04032.pdf>`_
1255 |     | :authors:`Yanru Qu, Ting Bai, Weinan Zhang, Jianyun Nie, Jian Tang`
1256 |     | :venue:`KDD 2019 Workshop`
1257 | 
1258 | Link Prediction
1259 | ---------------
1260 | 
1261 | `Link Prediction Based on Graph Neural Networks
1262 | <https://papers.nips.cc/paper/7763-link-prediction-based-on-graph-neural-networks.pdf>`_
1263 |     | :authors:`Muhan Zhang, Yixin Chen`
1264 |     | :venue:`NeurIPS 2018`
1265 | 
1266 | `Link Prediction via Subgraph Embedding-Based Convex Matrix Completion
1267 | <http://iiis.tsinghua.edu.cn/~weblt/papers/link-prediction-subgraphembeddings.pdf>`_
1268 |     | :authors:`Zhu Cao, Linlin Wang, Gerard de Melo`
1269 |     | :venue:`AAAI 2018`
1270 | 
1271 | `Graph Convolutional Matrix Completion
1272 | <https://www.kdd.org/kdd2018/files/deep-learning-day/DLDay18_paper_32.pdf>`_
1273 |     | :authors:`Rianne van den Berg, Thomas N. Kipf, Max Welling`
1274 |     | :venue:`KDD 2018 Workshop`
1275 | 
1276 | `Semi-Implicit Graph Variational Auto-Encoders
1277 | <https://arxiv.org/pdf/1908.07078.pdf>`_
1278 |     | :authors:`Arman Hasanzadeh, Ehsan Hajiramezanali, Nick Duffield , Krishna Narayanan, Mingyuan Zhou, Xiaoning Qian`
1279 |     | :venue:`NeurIPS 2019`
1280 | 
1281 | Influence Prediction
1282 | --------------------
1283 | 
1284 | `DeepInf: Social Influence Prediction with Deep Learning
1285 | <https://arxiv.org/pdf/1807.05560.pdf>`_
1286 |     | :authors:`Jiezhong Qiu, Jian Tang, Hao Ma, Yuxiao Dong, Kuansan Wang, Jie Tang`
1287 |     | :venue:`KDD 2018`
1288 | 
1289 | `Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks
1290 | <https://arxiv.org/pdf/1905.08865.pdf>`_
1291 |     | :authors:`Namyong Park, Andrey Kan, Xin Luna Dong, Tong Zhao, Christos Faloutsos`
1292 |     | :venue:`KDD 2019`
1293 | 
1294 | Neural Architecture Search
1295 | --------------------------
1296 | 
1297 | `Graph HyperNetworks for Neural Architecture Search
1298 | <https://openreview.net/pdf?id=rkgW0oA9FX>`_
1299 |     | :authors:`Chris Zhang, Mengye Ren, Raquel Urtasun`
1300 |     | :venue:`ICLR 2019`
1301 | 
1302 | `D-VAE: A Variational Autoencoder for Directed Acyclic Graphs
1303 | <https://arxiv.org/pdf/1904.11088.pdf>`_
1304 |     | :authors:`Muhan Zhang, Shali Jiang, Zhicheng Cui, Roman Garnett, Yixin Chen`
1305 |     | :venue:`NeurIPS 2019`
1306 | 
1307 | Reinforcement Learning
1308 | ----------------------
1309 | 
1310 | `Action Schema Networks: Generalised Policies with Deep Learning
1311 | <https://arxiv.org/pdf/1709.04271.pdf>`_
1312 |     | :authors:`Sam Toyer, Felipe Trevizan, Sylvie Thiebaux, Lexing Xie`
1313 |     | :venue:`AAAI 2018`
1314 | 
1315 | `NerveNet: Learning Structured Policy with Graph Neural Networks
1316 | <https://openreview.net/pdf?id=S1sqHMZCb>`_
1317 |     | :authors:`Tingwu Wang, Renjie Liao, Jimmy Ba, Sanja Fidler`
1318 |     | :venue:`ICLR 2018`
1319 | 
1320 | `Graph Networks as Learnable Physics Engines for Inference and Control
1321 | <https://arxiv.org/pdf/1806.01242.pdf>`_
1322 |     | :authors:`Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg, Josh Merel, Martin Riedmiller`
1323 |     | :venue:`ICML 2018`
1324 | 
1325 | `Learning Policy Representations in Multiagent Systems
1326 | <https://arxiv.org/pdf/1806.06464.pdf>`_
1327 |     | :authors:`Aditya Grover, Maruan Al-Shedivat, Jayesh K. Gupta, Yura Burda, Harrison Edwards`
1328 |     | :venue:`ICML 2018`
1329 | 
1330 | `Relational recurrent neural networks
1331 | <https://papers.nips.cc/paper/7960-relational-recurrent-neural-networks.pdf>`_
1332 |     | :authors:`Adam Santoro,  Ryan Faulkner, David Raposo, Jack Rae, Mike Chrzanowski,Théophane Weber, Daan Wierstra, Oriol Vinyals, Razvan Pascanu, Timothy Lillicrap`
1333 |     | :venue:`NeurIPS 2018`
1334 | 
1335 | `Transfer of Deep Reactive Policies for MDP Planning
1336 | <http://www.cse.iitd.ac.in/~mausam/papers/nips18.pdf>`_
1337 |     | :authors:`Aniket Bajpai, Sankalp Garg, Mausam`
1338 |     | :venue:`NeurIPS 2018`
1339 | 
1340 | `Neural Graph Evolution: Towards Efficient Automatic Robot Design
1341 | <https://openreview.net/pdf?id=BkgWHnR5tm>`_
1342 |     | :authors:`Tingwu Wang, Yuhao Zhou, Sanja Fidler, Jimmy Ba`
1343 |     | :venue:`ICLR 2019`
1344 | 
1345 | `No Press Diplomacy: Modeling Multi-Agent Gameplay
1346 | <https://arxiv.org/pdf/1909.02128.pdf>`_
1347 |     | :authors:`Philip Paquette, Yuchen Lu, Steven Bocco, Max O. Smith, Satya Ortiz-Gagne, Jonathan K. Kummerfeld, Satinder Singh, Joelle Pineau, Aaron Courville`
1348 |     | :venue:`NeurIPS 2019`
1349 | 
1350 | Combinatorial Optimization
1351 | --------------------------
1352 | 
1353 | `Learning Combinatorial Optimization Algorithms over Graphs
1354 | <https://arxiv.org/abs/1704.01665>`_
1355 |     | :authors:`Hanjun Dai, Elias B. Khalil, Yuyu Zhang, Bistra Dilkina, Le Song`
1356 |     | :venue:`NeurIPS 2017`
1357 | 
1358 | `Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search
1359 | <https://arxiv.org/abs/1810.10659>`_
1360 |     | :authors:`Zhuwen Li, Qifeng Chen, Vladlen Koltun`
1361 |     | :venue:`NeurIPS 2018`
1362 | 
1363 | `Reinforcement Learning for Solving the Vehicle Routing Problem
1364 | <https://arxiv.org/abs/1802.04240>`_
1365 |     | :authors:`Mohammadreza Nazari, Afshin Oroojlooy, Lawrence V. Snyder, Martin Takáč`
1366 |     | :venue:`NeurIPS 2018`
1367 |     
1368 | `Attention, Learn to Solve Routing Problems!
1369 | <https://arxiv.org/abs/1803.08475>`_
1370 |     | :authors:`Wouter Kool, Herke van Hoof, Max Welling`
1371 |     | :venue:`ICLR 2019`
1372 |     
1373 | `Learning a SAT Solver from Single-Bit Supervision
1374 | <https://arxiv.org/abs/1802.03685>`_
1375 |     | :authors:`Daniel Selsam, Matthew Lamm, Benedikt Bünz, Percy Liang, Leonardo de Moura, David L. Dill`
1376 |     | :venue:`ICLR 2019`
1377 |     
1378 | `An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem
1379 | <https://arxiv.org/abs/1906.01227>`_
1380 |     | :authors:`Chaitanya K. Joshi, Thomas Laurent, Xavier Bresson`
1381 |     | :venue:`arXiv 2019`
1382 | 
1383 | `Approximation Ratios of Graph Neural Networks for Combinatorial Problems
1384 | <https://arxiv.org/pdf/1905.10261.pdf>`_
1385 |     | :authors:`Ryoma Sato, Makoto Yamada, Hisashi Kashima`
1386 |     | :venue:`NeurIPS 2019`
1387 | 
1388 | `Exact Combinatorial Optimization with Graph Convolutional Neural Networks
1389 | <https://arxiv.org/pdf/1906.01629.pdf>`_
1390 |     | :authors:`Maxime Gasse, Didier Chételat, Nicola Ferroni, Laurent Charlin, Andrea Lodi`
1391 |     | :venue:`NeurIPS 2019`
1392 |     
1393 | `On Learning Paradigms for the Travelling Salesman Problem
1394 | <https://arxiv.org/pdf/1910.07210.pdf>`_
1395 |     | :authors:`Chaitanya K. Joshi, Thomas Laurent, Xavier Bresson`
1396 |     | :venue:`NeurIPS 2019 Workshop`
1397 | 
1398 | Adversarial Attack and Robustness
1399 | ------------------
1400 | 
1401 | `Adversarial Attack on Graph Structured Data
1402 | <https://arxiv.org/abs/1806.02371>`_
1403 |     | :authors:`Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, Le Song`
1404 |     | :venue:`ICML 2018`
1405 | 
1406 | `Adversarial Attacks on Neural Networks for Graph Data
1407 | <https://arxiv.org/abs/1805.07984>`_
1408 |     | :authors:`Daniel Zügner, Amir Akbarnejad, Stephan Günnemann`
1409 |     | :venue:`KDD 2018`
1410 | 
1411 | `Adversarial Attacks on Graph Neural Networks via Meta Learning
1412 | <https://arxiv.org/abs/1902.08412>`_
1413 |     | :authors:`Daniel Zügner, Stephan Günnemann`
1414 |     | :venue:`ICLR 2019`
1415 | 
1416 | `Robust Graph Convolutional Networks Against Adversarial Attacks
1417 | <http://pengcui.thumedialab.com/papers/RGCN.pdf>`_
1418 |     | :authors:`Dingyuan Zhu, Ziwei Zhang, Peng Cui, Wenwu Zhu`
1419 |     | :venue:`KDD 2019`
1420 | 
1421 | `Certifiable Robustness and Robust Training for Graph Convolutional Networks
1422 | <https://arxiv.org/pdf/1906.12269.pdf>`_
1423 |     | :authors:`Daniel Zügner, Stephan Günnemann`
1424 |     | :venue:`KDD 2019`
1425 | 
1426 | Graph Matching
1427 | -------------
1428 | 
1429 | `REGAL: Representation Learning-based Graph Alignment
1430 | <https://arxiv.org/pdf/1802.06257.pdf>`_
1431 | 	| :authors:`Mark Heimann, Haoming Shen, Tara Safavi, Danai Koutra`
1432 | 	| :venue:`CIKM 2018`
1433 | 
1434 | `Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks
1435 | <https://www.aclweb.org/anthology/D18-1032.pdf>`_
1436 | 	| :authors:`Zhichun Wang, Qingsong Lv, Xiaohan Lan, Yu Zhang`
1437 | 	| :venue:`EMNLP 2018`
1438 | 
1439 | `Learning Combinatorial Embedding Networks for Deep Graph Matching
1440 | <http://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Learning_Combinatorial_Embedding_Networks_for_Deep_Graph_Matching_ICCV_2019_paper.pdf>`_
1441 | 	| :authors:`Runzhong Wang, Junchi Yan, Xiaokang Yang`
1442 | 	| :venue:`ICCV 2019`
1443 | 
1444 | `Deep Graph Matching Consensus
1445 | <https://openreview.net/pdf?id=HyeJf1HKvS>`_
1446 | 	| :authors:`Matthias Fey, Jan E. Lenssen, Christopher Morris, Jonathan Masci, Nils M. Kriege`
1447 | 	| :venue:`ICLR 2020`
1448 | 	
1449 | Meta Learning and Few-shot Learning
1450 | ---------------------------------
1451 | 
1452 | `Few-Shot Learning with Graph Neural Networks
1453 | <https://arxiv.org/abs/1711.04043>`_
1454 |     | :authors:`Victor Garcia, Joan Bruna`
1455 |     | :venue:`ICLR 2018`
1456 | 
1457 | `Learning Steady-States of Iterative Algorithms over Graphs
1458 | <http://proceedings.mlr.press/v80/dai18a.html>`_
1459 |     | :authors:`Hanjun Dai, Zornitsa Kozareva, Bo Dai, Alex Smola, Le Song`
1460 |     | :venue:`ICML 2018`
1461 | 
1462 | `Learning to Propagate for Graph Meta-Learning
1463 | <https://arxiv.org/pdf/1909.05024.pdf>`_
1464 |     | :authors:`Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang`
1465 |     | :venue:`NeurIPS 2019`
1466 | 
1467 | `Few-Shot Learning on Graphs via Super-Classes based on Graph Spectral Measures
1468 | <https://openreview.net/forum?id=Bkeeca4Kvr>`_
1469 | 	| :authors:`Jatin Chauhan, Deepak Nathani, Manohar Kaul`
1470 | 	| :venue:`ICLR 2020`
1471 | 
1472 | `Automated Relational Meta-learning
1473 | <https://openreview.net/pdf?id=rklp93EtwH>`_
1474 | 	| :authors:`Huaxiu Yao, Xian Wu, Zhiqiang Tao, Yaliang Li, Bolin Ding, Ruirui Li, Zhenhui Li`
1475 | 	| :venue:`ICLR 2020`
1476 | 
1477 | Structure Learning
1478 | ------------------
1479 | 
1480 | `Neural Relational Inference for Interacting Systems
1481 | <https://arxiv.org/abs/1802.04687>`_
1482 |     | :authors:`Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, Richard Zemel`
1483 |     | :venue:`ICML 2018`
1484 | 
1485 | `Brain Signal Classification via Learning Connectivity Structure
1486 | <https://arxiv.org/abs/1905.11678>`_
1487 |     | :authors:`Soobeom Jang, Seong-Eun Moon, Jong-Seok Lee`
1488 |     | :venue:`arXiv 2019`
1489 | 
1490 | `A Flexible Generative Framework for Graph-based Semi-supervised Learning
1491 | <https://arxiv.org/abs/1905.10769>`_
1492 |     | :authors:`Jiaqi Ma, Weijing Tang, Ji Zhu, Qiaozhu Mei`
1493 |     | :venue:`NeurIPS 2019`
1494 | 
1495 | `Joint embedding of structure and features via graph convolutional networks
1496 | <https://arxiv.org/abs/1905.08636>`_
1497 |     | :authors:`Sébastien Lerique, Jacob Levy Abitbol, Márton Karsai`
1498 |     | :venue:`arXiv 2019`
1499 | 
1500 | `Variational Spectral Graph Convolutional Networks
1501 | <https://arxiv.org/abs/1906.01852>`_
1502 |     | :authors:`Louis Tiao, Pantelis Elinas, Harrison Nguyen, Edwin V. Bonilla`
1503 |     | :venue:`arXiv 2019`
1504 | 
1505 | `Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning
1506 | <https://arxiv.org/abs/1805.10002>`_
1507 |     | :authors:`Yanbin Liu, Juho Lee, Minseop Park, Saehoon Kim, Eunho Yang, Sung Ju Hwang, Yi Yang`
1508 |     | :venue:`ICLR 2019`
1509 | 
1510 | `Graph Learning Network: A Structure Learning Algorithm
1511 | <https://arxiv.org/abs/1905.12665>`_
1512 |     | :authors:`Darwin Saire Pilco, Adín Ramírez Rivera`
1513 |     | :venue:`ICML 2019 Workshop`
1514 | 
1515 | `Learning Discrete Structures for Graph Neural Networks
1516 | <https://arxiv.org/abs/1903.11960>`_
1517 |     | :authors:`Luca Franceschi, Mathias Niepert, Massimiliano Pontil, Xiao He`
1518 |     | :venue:`ICML 2019`
1519 | 
1520 | `Graphite: Iterative Generative Modeling of Graphs
1521 | <https://arxiv.org/abs/1803.10459>`_
1522 |     | :authors:`Aditya Grover, Aaron Zweig, Stefano Ermon`
1523 |     | :venue:`ICML 2019`
1524 | 
1525 | Bioinformatics and Chemistry
1526 | --------------
1527 | 
1528 | `Protein Interface Prediction using Graph Convolutional Networks
1529 | <https://papers.nips.cc/paper/7231-protein-interface-prediction-using-graph-convolutional-networks.pdf>`_
1530 |     | :authors:`Alex Fout, Jonathon Byrd, Basir Shariat, Asa Ben-Hur`
1531 |     | :venue:`NeurIPS 2017`
1532 | 
1533 | `Modeling Polypharmacy Side Effects with Graph Convolutional Networks
1534 | <https://arxiv.org/abs/1802.00543>`_
1535 |     | :authors:`Marinka Zitnik, Monica Agrawal, Jure Leskovec`
1536 |     | :venue:`Bioinformatics 2018`
1537 | 
1538 | `NeoDTI: Neural Integration of Neighbor Information from a Heterogeneous Network for Discovering New
1539 | Drug–target Interactions
1540 | <https://academic.oup.com/bioinformatics/article-abstract/35/1/104/5047760?redirectedFrom=fulltext>`_
1541 |     | :authors:`Fangping Wan, Lixiang Hong, An Xiao, Tao Jiang, Jianyang Zeng`
1542 |     | :venue:`Bioinformatics 2018`
1543 | 
1544 | `SELFIES: a Robust Representation of Semantically Constrained Graphs with an Example Application in Chemistry
1545 | <https://arxiv.org/pdf/1905.13741.pdf>`_
1546 |     | :authors:`Mario Krenn, Florian Häse, AkshatKumar Nigam, Pascal Friederich, Alán Aspuru-Guzik`
1547 |     | :venue:`arXiv 2019`
1548 | 
1549 | `Drug-Drug Adverse Effect Prediction with Graph Co-Attention
1550 | <https://arxiv.org/pdf/1905.00534.pdf>`_
1551 |     | :authors:`Andreea Deac, Yu-Hsiang Huang, Petar Veličković, Pietro Liò, Jian Tang`
1552 |     | :venue:`ICML 2019 Workshop`
1553 | 
1554 | `GCN-MF: Disease-Gene Association Identification By Graph Convolutional Networks and Matrix Factorization
1555 | <https://www.kdd.org/kdd2019/accepted-papers/view/gcn-mf-disease-gene-association-identification-by-graph-convolutional-netwo>`_
1556 |     | :authors:`Peng Han, Peng Yang, Peilin Zhao, Shuo Shang, Yong Liu, Jiayu Zhou, Xin Gao, Panos Kalnis`
1557 |     | :venue:`KDD 2019`
1558 | 
1559 | `Detecting drug-drug interactions using artificial neural networks and classic graph similarity measures
1560 | <https://arxiv.org/pdf/1903.04571.pdf>`_
1561 |     | :authors:`Guy Shtar, Lior Rokach, Bracha Shapira`
1562 |     | :venue:`arXiv 2019`
1563 | 
1564 | `PGCN: Disease gene prioritization by disease and gene embedding through graph convolutional neural networks
1565 | <https://www.biorxiv.org/content/biorxiv/early/2019/01/28/532226.full.pdf>`_
1566 |     | :authors:`Yu Li, Hiroyuki Kuwahara, Peng Yang, Le Song, Xin Gao`
1567 |     | :venue:`bioRxiv 2019`
1568 | 
1569 | `Identifying Protein-Protein Interaction using Tree LSTM and Structured Attention
1570 | <https://ieeexplore.ieee.org/abstract/document/8665584>`_
1571 |     | :authors:`Mahtab Ahmed, Jumayel Islam, Muhammad Rifayat Samee, Robert E. Mercer`
1572 |     | :venue:`ICSC 2019`
1573 | 
1574 | `GCN-MF: Disease-Gene Association Identification By Graph Convolutional Networks and Matrix Factorization
1575 | <https://dl.acm.org/citation.cfm?id=3330912>`_
1576 |     | :authors:`Peng Han, Peng Yang, Peilin Zhao, Shuo Shang, Yong Liu, Jiayu Zhou, Xin Gao, Panos Kalnis`
1577 |     | :venue:`KDD 2019`
1578 | 
1579 | `Towards perturbation prediction of biological networks using deep learning
1580 | <https://www.nature.com/articles/s41598-019-48391-y>`_
1581 |     | :authors:`Diya Li, Jianxi Gao`
1582 |     | :venue:`Nature 2019`
1583 | 
1584 | `Directional Message Passing for Molecular Graphs
1585 | <https://openreview.net/pdf?id=B1eWbxStPH>`_
1586 | 	| :authors:`Johannes Klicpera, Janek Groß, Stephan Günnemann`
1587 | 	| :venue:`ICLR 2020`
1588 | 
1589 | Graph Algorithms
1590 | ---------------
1591 | 
1592 | `Neural Execution of Graph Algorithms
1593 | <https://openreview.net/pdf?id=SkgKO0EtvS>`_
1594 | 	| :authors:`Petar Veličković, Rex Ying, Matilde Padovano, Raia Hadsell, Charles Blundell`
1595 | 	| :venue:`ICLR 2020`
1596 | 
1597 | Theorem Proving
1598 | ---------------
1599 | 
1600 | `Premise Selection for Theorem Proving by Deep Graph Embedding
1601 | <https://arxiv.org/abs/1709.09994>`_
1602 |     | :authors:`Mingzhe Wang, Yihe Tang, Jian Wang, Jia Deng`
1603 |     | :venue:`NeurIPS 2017`
1604 | 
1605 | Graph Generation
1606 | ================
1607 | 
1608 | `GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models
1609 | <https://arxiv.org/abs/1802.08773>`_
1610 |     | :authors:`Jiaxuan You, Rex Ying, Xiang Ren, William L. Hamilton, Jure Leskovec`
1611 |     | :venue:`ICML 2018`
1612 | 
1613 | `NetGAN: Generating Graphs via Random Walks
1614 | <https://arxiv.org/abs/1803.00816>`_
1615 |     | :authors:`Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, Stephan Günnemann`
1616 |     | :venue:`ICML 2018`
1617 | 
1618 | `Learning Deep Generative Models of Graphs
1619 | <https://arxiv.org/abs/1803.03324>`_
1620 |     | :authors:`Yujia Li, Oriol Vinyals, Chris Dyer, Razvan Pascanu, Peter Battaglia`
1621 |     | :venue:`ICML 2018`
1622 | 
1623 | `Junction Tree Variational Autoencoder for Molecular Graph Generation
1624 | <https://arxiv.org/abs/1802.04364>`_
1625 |     | :authors:`Wengong Jin, Regina Barzilay, Tommi Jaakkola`
1626 |     | :venue:`ICML 2018`
1627 | 
1628 | `MolGAN: An implicit generative model for small molecular graphs
1629 | <https://arxiv.org/abs/1805.11973>`_
1630 |     | :authors:`Nicola De Cao, Thomas Kipf`
1631 |     | :venue:`arXiv 2018`
1632 | 
1633 | `Generative Modeling for Protein Structures
1634 | <https://papers.nips.cc/paper/7978-generative-modeling-for-protein-structures.pdf>`_
1635 |     | :authors:`Namrata Anand, Po-Ssu Huang`
1636 |     | :venue:`NeurIPS 2018`
1637 | 
1638 | `Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders
1639 | <https://arxiv.org/abs/1809.02630>`_
1640 |     | :authors:`Tengfei Ma, Jie Chen, Cao Xiao`
1641 |     | :venue:`NeurIPS 2018`
1642 | 
1643 | `Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
1644 | <https://arxiv.org/abs/1806.02473>`_
1645 |     | :authors:`Jiaxuan You, Bowen Liu, Rex Ying, Vijay Pande, Jure Leskovec`
1646 |     | :venue:`NeurIPS 2018`
1647 | 
1648 | `Constrained Graph Variational Autoencoders for Molecule Design
1649 | <https://arxiv.org/abs/1805.09076>`_
1650 |     | :authors:`Qi Liu, Miltiadis Allamanis, Marc Brockschmidt, Alexander L. Gaunt`
1651 |     | :venue:`NeurIPS 2018`
1652 | 
1653 | `Learning Multimodal Graph-to-Graph Translation for Molecule Optimization
1654 | <https://arxiv.org/abs/1812.01070>`_
1655 |     | :authors:`Wengong Jin, Kevin Yang, Regina Barzilay, Tommi Jaakkola`
1656 |     | :venue:`ICLR 2019`
1657 | 
1658 | `Generative Code Modeling with Graphs
1659 | <https://openreview.net/forum?id=Bke4KsA5FX>`_
1660 |     | :authors:`Marc Brockschmidt, Miltiadis Allamanis, Alexander L. Gaunt, Oleksandr Polozov`
1661 |     | :venue:`ICLR 2019`
1662 | 
1663 | `DAG-GNN: DAG Structure Learning with Graph Neural Networks
1664 | <https://arxiv.org/abs/1904.10098>`_
1665 |     | :authors:`Yue Yu, Jie Chen, Tian Gao, Mo Yu`
1666 |     | :venue:`ICML 2019`
1667 | 
1668 | `Graph to Graph: a Topology Aware Approach for Graph Structures Learning and Generation
1669 | <http://proceedings.mlr.press/v89/sun19c.html>`_
1670 |     | :authors:`Mingming Sun, Ping Li`
1671 |     | :venue:`AISTATS 2019`
1672 | 
1673 | `Graph Normalizing Flows
1674 | <https://arxiv.org/abs/1905.13177>`_
1675 |     | :authors:`Jenny Liu, Aviral Kumar, Jimmy Ba, Jamie Kiros, Kevin Swersky`
1676 |     | :venue:`NeurIPS 2019`
1677 | 
1678 | `Conditional Structure Generation through Graph Variational Generative Adversarial Nets
1679 | <http://jiyang3.web.engr.illinois.edu/files/condgen.pdf>`_
1680 |     | :authors:`Carl Yang, Peiye Zhuang, Wenhan Shi, Alan Luu, Pan Li`
1681 |     | :venue:`NeurIPS 2019`
1682 | 
1683 | `Efficient Graph Generation with Graph Recurrent Attention Networks
1684 | <https://arxiv.org/pdf/1910.00760.pdf>`_
1685 |     | :authors:`Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, Charlie Nash, William L. Hamilton, David Duvenaud, Raquel Urtasun, Richard Zemel`
1686 |     | :venue:`NeurIPS 2019`
1687 | 
1688 | `GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation
1689 | <https://openreview.net/pdf?id=S1esMkHYPr>`_
1690 | 	| :authors:`Chence Shi, Minkai Xu, Zhaocheng Zhu, Weinan Zhang, Ming Zhang, Jian Tang`
1691 | 	| :venue:`ICLR 2020`
1692 | 
1693 | Graph Layout and High-dimensional Data Visualization
1694 | ====================================================
1695 | 
1696 | `Visualizing Data using t-SNE
1697 | <http://www.jmlr.org/papers/volume9/vandermaaten08a/vandermaaten08a.pdf>`_
1698 |     | :authors:`Laurens van der Maaten, Geoffrey Hinton`
1699 |     | :venue:`JMLR 2008`
1700 | 
1701 | `Visualizing non-metric similarities in multiple maps
1702 | <https://link.springer.com/content/pdf/10.1007/s10994-011-5273-4.pdf>`_
1703 |     | :authors:`Laurens van der Maaten, Geoffrey Hinton`
1704 |     | :venue:`ML 2012`
1705 | 
1706 | `Visualizing Large-scale and High-dimensional Data
1707 | <https://arxiv.org/pdf/1602.00370>`_
1708 |     | :authors:`Jian Tang, Jingzhou Liu, Ming Zhang, Qiaozhu Mei`
1709 |     | :venue:`WWW 2016`
1710 | 
1711 | `GraphTSNE: A Visualization Technique for Graph-Structured Data
1712 | <https://arxiv.org/pdf/1904.06915.pdf>`_
1713 |     | :authors:`Yao Yang Leow, Thomas Laurent, Xavier Bresson`
1714 |     | :venue:`ICLR 2019 Workshop`
1715 | 
1716 | Graph Representation Learning Systems
1717 | =====================================
1718 | 
1719 | `GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding
1720 | <https://arxiv.org/pdf/1903.00757>`_
1721 |     | :authors:`Zhaocheng Zhu, Shizhen Xu, Meng Qu, Jian Tang`
1722 |     | :venue:`WWW 2019`
1723 | 
1724 | `PyTorch-BigGraph: A Large-scale Graph Embedding System
1725 | <https://arxiv.org/pdf/1903.12287>`_
1726 |     | :authors:`Adam Lerer, Ledell Wu, Jiajun Shen, Timothee Lacroix, Luca Wehrstedt, Abhijit Bose, Alex Peysakhovich`
1727 |     | :venue:`SysML 2019`
1728 | 
1729 | `AliGraph: A Comprehensive Graph Neural Network Platform
1730 | <https://arxiv.org/pdf/1902.08730>`_
1731 |     | :authors:`Rong Zhu, Kun Zhao, Hongxia Yang, Wei Lin, Chang Zhou, Baole Ai, Yong Li, Jingren Zhou`
1732 |     | :venue:`VLDB 2019`
1733 | 
1734 | `Deep Graph Library
1735 | <https://www.dgl.ai>`_
1736 |     | :authors:`DGL Team`
1737 | 
1738 | `AmpliGraph
1739 | <https://github.com/Accenture/AmpliGraph>`_
1740 |     | :authors:`Luca Costabello, Sumit Pai, Chan Le Van, Rory McGrath, Nicholas McCarthy, Pedro Tabacof`
1741 | 
1742 | `Euler
1743 | <https://github.com/alibaba/euler>`_
1744 |     | :authors:`Alimama Engineering Platform Team, Alimama Search Advertising Algorithm Team`
1745 | 
1746 | Datasets
1747 | ========
1748 | 
1749 | `ATOMIC: an atlas of machine commonsense for if-then reasoning
1750 | <https://wvvw.aaai.org/ojs/index.php/AAAI/article/download/4160/4038>`_
1751 |     | :authors:`Maarten Sap, Ronan Le Bras, Emily Allaway, Chandra Bhagavatula, Nicholas Lourie, Hannah Rashkin, Brendan Roof, Noah A. Smith, Yejin Choi`
1752 |     | :venue:`AAAI 2019`
1753 | 
1754 | 
1755 | 


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