Literature of Deep Learning for Graphs ************************************** This is a paper list about deep learning for graphs. .. contents:: :local: :depth: 2 .. sectnum:: :depth: 2 .. role:: author(emphasis) .. role:: venue(strong) .. role:: keyword(emphasis) Node Representation Learning ============================ Unsupervised Node Representation Learning ----------------------------------------- `DeepWalk: Online Learning of Social Representations <https://arxiv.org/pdf/1403.6652>`_ | :author:`Bryan Perozzi, Rami Al-Rfou, Steven Skiena` | :venue:`KDD 2014` | :keyword:`Node classification, Random walk, Skip-gram` `LINE: Large-scale Information Network Embedding <https://arxiv.org/pdf/1503.03578>`_ | :author:`Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, Qiaozhu Mei` | :venue:`WWW 2015` | :keyword:`First-order, Second-order, Node classification` `GraRep: Learning Graph Representations with Global Structural Information <https://dl.acm.org/citation.cfm?id=2806512>`_ | :author:`Shaosheng Cao, Wei Lu, Qiongkai Xu` | :venue:`CIKM 2015` | :keyword:`High-order, SVD` `node2vec: Scalable Feature Learning for Networks <https://arxiv.org/pdf/1607.00653>`_ | :author:`Aditya Grover, Jure Leskovec` | :venue:`KDD 2016` | :keyword:`Breadth-first Search, Depth-first Search, Node Classification, Link Prediction` `Variational Graph Auto-Encoders <https://arxiv.org/abs/1611.07308>`_ | :author:`Thomas N. Kipf, Max Welling` | :venue:`arXiv 1611` `Scalable Graph Embedding for Asymmetric Proximity <https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14696>`_ | :author:`Chang Zhou, Yuqiong Liu, Xiaofei Liu, Zhongyi Liu, Jun Gao` | :venue:`AAAI 2017` `Fast Network Embedding Enhancement via High Order Proximity Approximation <https://www.ijcai.org/proceedings/2017/544>`_ | :author:`Cheng Yang, Maosong Sun, Zhiyuan Liu, Cunchao Tu` | :venue:`IJCAI 2017` `struc2vec: Learning Node Representations from Structural Identity <https://arxiv.org/pdf/1704.03165>`_ | :author:`Leonardo F. R. Ribeiro, Pedro H. P. Savarese, Daniel R. Figueiredo` | :venue:`KDD 2017` | :keyword:`Structural Identity` `Poincaré Embeddings for Learning Hierarchical Representations <https://arxiv.org/pdf/1705.08039>`_ | :author:`Maximilian Nickel, Douwe Kiela` | :venue:`NIPS 2017` `VERSE: Versatile Graph Embeddings from Similarity Measures <https://arxiv.org/pdf/1803.04742>`_ | :author:`Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Emmanuel Müller` | :venue:`WWW 2018` `Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec <https://arxiv.org/pdf/1710.02971>`_ | :author:`Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, Jie Tang` | :venue:`WSDM 2018` `Learning Structural Node Embeddings via Diffusion Wavelets <https://arxiv.org/pdf/1710.10321>`_ | :author:`Claire Donnat, Marinka Zitnik, David Hallac, Jure Leskovec` | :venue:`KDD 2018` `Adversarial Network Embedding <https://arxiv.org/pdf/1711.07838>`_ | :author:`Quanyu Dai, Qiang Li, Jian Tang, Dan Wang` | :venue:`AAAI 2018` `GraphGAN: Graph Representation Learning with Generative Adversarial Nets <https://arxiv.org/pdf/1711.08267>`_ | :author:`Hongwei Wang, Jia Wang, Jialin Wang, Miao Zhao, Weinan Zhang, Fuzheng Zhang, Xing Xie, Minyi Guo` | :venue:`AAAI 2018` `A General View for Network Embedding as Matrix Factorization <https://dl.acm.org/citation.cfm?id=3291029>`_ | :author:`Xin Liu, Tsuyoshi Murata, Kyoung-Sook Kim, Chatchawan Kotarasu, Chenyi Zhuang` | :venue:`WSDM 2019` `Deep Graph Infomax <https://arxiv.org/pdf/1809.10341>`_ | :author:`Petar Veličković, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, R Devon Hjelm` | :venue:`ICLR 2019` `NetSMF: Large-Scale Network Embedding as Sparse Matrix Factorization <http://keg.cs.tsinghua.edu.cn/jietang/publications/www19-Qiu-et-al-NetSMF-Large-Scale-Network-Embedding.pdf>`_ | :author:`Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Chi Wang, Kuansan Wang, Jie Tang` | :venue:`WWW 2019` `Adversarial Training Methods for Network Embedding <https://dl.acm.org/citation.cfm?id=3313445>`_ | :author:`Quanyu Dai, Xiao Shen, Liang Zhang, Qiang Li, Dan Wang` | :venue:`WWW 2019` `vGraph: A Generative Model for Joint Community Detection and Node Representation Learning <https://arxiv.org/pdf/1906.07159.pdf>`_ | :author:`Fan-Yun Sun, Meng Qu, Jordan Hoffmann, Chin-Wei Huang, Jian Tang` | :venue:`arXiv 1906` Node Representation Learning in Heterogeneous Graphs ---------------------------------------------------- `Learning Latent Representations of Nodes for Classifying in Heterogeneous Social Networks <https://dl.acm.org/citation.cfm?id=2556225>`_ | :author:`Yann Jacob, Ludovic Denoyer, Patrick Gallinari` | :venue:`WSDM 2014` `PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks <https://arxiv.org/pdf/1508.00200>`_ | :author:`Jian Tang, Meng Qu, Qiaozhu Mei` | :venue:`KDD 2015` | :keyword:`Text Embedding, Heterogeneous Text Graphs` `Heterogeneous Network Embedding via Deep Architectures <https://dl.acm.org/citation.cfm?id=2783296>`_ | :author:`Shiyu Chang, Wei Han, Jiliang Tang, Guo-Jun Qi, Charu C. Aggarwal, Thomas S. Huang` | :venue:`KDD 2015` `Network Representation Learning with Rich Text Information <https://www.aaai.org/ocs/index.php/IJCAI/IJCAI15/paper/view/11098>`_ | :author:`Cheng Yang, Zhiyuan Liu, Deli Zhao, Maosong Sun, Edward Chang` | :venue:`AAAI 2015` `Max-Margin DeepWalk: Discriminative Learning of Network Representation <https://www.ijcai.org/Proceedings/16/Papers/547.pdf>`_ | :author:`Cunchao Tu, Weicheng Zhang, Zhiyuan Liu, Maosong Sun` | :venue:`IJCAI 2016` `metapath2vec: Scalable Representation Learning for Heterogeneous Networks <https://dl.acm.org/citation.cfm?id=3098036>`_ | :author:`Yuxiao Dong, Nitesh V. Chawla, Ananthram Swami` | :venue:`KDD 2017` `Meta-Path Guided Embedding for Similarity Search in Large-Scale Heterogeneous Information Networks <https://arxiv.org/pdf/1610.09769>`_ | :author:`Jingbo Shang, Meng Qu, Jialu Liu, Lance M. Kaplan, Jiawei Han, Jian Peng` | :venue:`arXiv 2016` `HIN2Vec: Explore Meta-paths in Heterogeneous Information Networks for Representation Learning <https://dl.acm.org/citation.cfm?id=3132953>`_ | :author:`Tao-yang Fu, Wang-Chien Lee, Zhen Lei` | :venue:`CIKM 2017` `An Attention-based Collaboration Framework for Multi-View Network Representation Learning <https://arxiv.org/pdf/1709.06636>`_ | :author:`Meng Qu, Jian Tang, Jingbo Shang, Xiang Ren, Ming Zhang, Jiawei Han` | :venue:`CIKM 2017` `Multi-view Clustering with Graph Embedding for Connectome Analysis <https://dl.acm.org/citation.cfm?id=3132909>`_ | :author:`Guixiang Ma, Lifang He, Chun-Ta Lu, Weixiang Shao, Philip S. Yu, Alex D. Leow, Ann B. Ragin` | :venue:`CIKM 2017` `Attributed Signed Network Embedding <https://dl.acm.org/citation.cfm?id=3132847.3132905>`_ | :author:`Suhang Wang, Charu Aggarwal, Jiliang Tang, Huan Liu` | :venue:`CIKM 2017` `CANE: Context-Aware Network Embedding for Relation Modeling <https://aclweb.org/anthology/papers/P/P17/P17-1158/>`_ | :author:`Cunchao Tu, Han Liu, Zhiyuan Liu, Maosong Sun` | :venue:`ACL 2017` `PME: Projected Metric Embedding on Heterogeneous Networks for Link Prediction <https://dl.acm.org/citation.cfm?id=3219986>`_ | :author:`Hongxu Chen, Hongzhi Yin, Weiqing Wang, Hao Wang, Quoc Viet Hung Nguyen, Xue Li` | :venue:`KDD 2018` `BiNE: Bipartite Network Embedding <https://dl.acm.org/citation.cfm?id=3209978.3209987>`_ | :author:`Ming Gao, Leihui Chen, Xiangnan He, Aoying Zhou` | :venue:`SIGIR 2018` `StarSpace: Embed All The Things <https://arxiv.org/pdf/1709.03856>`_ | :author:`Ledell Wu, Adam Fisch, Sumit Chopra, Keith Adams, Antoine Bordes, Jason Weston` | :venue:`AAAI 2018` `Exploring Expert Cognition for Attributed Network Embedding <https://dl.acm.org/citation.cfm?id=3159655>`_ | :author:`Xiao Huang, Qingquan Song, Jundong Li, Xia Hu` | :venue:`WSDM 2018` `SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction <https://arxiv.org/pdf/1712.00732>`_ | :author:`Hongwei Wang, Fuzheng Zhang, Min Hou, Xing Xie, Minyi Guo, Qi Liu` | :venue:`WSDM 2018` `Multidimensional Network Embedding with Hierarchical Structures <https://dl.acm.org/citation.cfm?id=3159680>`_ | :author:`Yao Ma, Zhaochun Ren, Ziheng Jiang, Jiliang Tang, Dawei Yin` | :venue:`WSDM 2018` `Curriculum Learning for Heterogeneous Star Network Embedding via Deep Reinforcement Learning <https://dl.acm.org/citation.cfm?id=3159711>`_ | :author:`Meng Qu, Jian Tang, Jiawei Han` | :venue:`WSDM 2018` `Generative Adversarial Network based Heterogeneous Bibliographic Network Representation for Personalized Citation Recommendation <https://www.semanticscholar.org/paper/Generative-Adversarial-Network-Based-Heterogeneous-Cai-Han/1596d6487012696ba400fb69904a2c372a08a2be>`_ | :author:`Xiaoyan Cai, Junwei Han, Libin Yang` | :venue:`AAAI 2018` `ANRL: Attributed Network Representation Learning via Deep Neural Networks <https://www.ijcai.org/proceedings/2018/438>`_ | :author:`Zhen Zhang, Hongxia Yang, Jiajun Bu, Sheng Zhou, Pinggang Yu, Jianwei Zhang, Martin Ester, Can Wang` | :venue:`IJCAI 2018` `Efficient Attributed Network Embedding via Recursive Randomized Hashing <https://www.ijcai.org/proceedings/2018/397>`_ | :author:`Wei Wu, Bin Li, Ling Chen, Chengqi Zhang` | :venue:`IJCAI 2018` `Deep Attributed Network Embedding <https://www.ijcai.org/proceedings/2018/467>`_ | :author:`Hongchang Gao, Heng Huang` | :venue:`IJCAI 2018` `Co-Regularized Deep Multi-Network Embedding <https://dl.acm.org/citation.cfm?id=3186113>`_ | :author:`Jingchao Ni, Shiyu Chang, Xiao Liu, Wei Cheng, Haifeng Chen, Dongkuan Xu, Xiang Zhang` | :venue:`WWW 2018` `Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks <https://arxiv.org/pdf/1807.03490>`_ | :author:`Yu Shi, Qi Zhu, Fang Guo, Chao Zhang, Jiawei Han` | :venue:`KDD 2018` `Meta-Graph Based HIN Spectral Embedding: Methods, Analyses, and Insights <https://www.semanticscholar.org/paper/Meta-Graph-Based-HIN-Spectral-Embedding%3A-Methods%2C-Yang-Feng/4d5f4d6785d550383e3f3afb04c3015bf0d28405>`_ | :author:`Carl Yang, Yichen Feng, Pan Li, Yu Shi, Jiawei Han` | :venue:`ICDM 2018` `SIDE: Representation Learning in Signed Directed Networks <https://dl.acm.org/citation.cfm?id=3186117>`_ | :author:`Junghwan Kim, Haekyu Park, Ji-Eun Lee, U Kang` | :venue:`WWW 2018` Node Representation Learning in Dynamic Graphs ---------------------------------------------- `Know-evolve: Deep temporal reasoning for dynamic knowledge graphs <https://arxiv.org/pdf/1705.05742.pdf>`_ | :author:`Rakshit Trivedi, Hanjun Dai, Yichen Wang, Le Song` | :venue:`ICML 2017` `Dyngem: Deep embedding method for dynamic graphs <https://arxiv.org/pdf/1805.11273.pdf>`_ | :author:`Palash Goyal, Nitin Kamra, Xinran He, Yan Liu` | :venue:`ICLR 2017 Workshop` `Attributed network embedding for learning in a dynamic environment <https://arxiv.org/pdf/1706.01860.pdf>`_ | :author:`Jundong Li, Harsh Dani, Xia Hu, Jiliang Tang, Yi Chang, Huan Liu` | :venue:`CIKM 2017` `Dynamic Network Embedding by Modeling Triadic Closure Process <http://yangy.org/works/dynamictriad/dynamic_triad.pdf>`_ | :author:`Lekui Zhou, Yang Yang, Xiang Ren, Fei Wu, Yueting Zhuang` | :venue:`AAAI 2018` `DepthLGP: Learning Embeddings of Out-of-Sample Nodes in Dynamic Networks <https://pdfs.semanticscholar.org/9499/b38866b1eb87ae43fa5be02f9d08cd3c20a8.pdf?_ga=2.6780794.935636364.1561139530-1831876308.1523264869>`_ | :author:`Jianxin Ma, Peng Cui, Wenwu Zhu` | :venue:`AAAI 2018` `TIMERS: Error-Bounded SVD Restart on Dynamic Networks <https://arxiv.org/pdf/1711.09541.pdf>`_ | :author:`Ziwei Zhang, Peng Cui, Jian Pei, Xiao Wang, Wenwu Zhu` | :venue:`AAAI 2018` `Dynamic Embeddings for User Profiling in Twitter <https://dl.acm.org/citation.cfm?id=3219819.3220043>`_ | :author:`Shangsong Liang, Xiangliang Zhang, Zhaochun Ren, Evangelos Kanoulas` | :venue:`KDD 2018` `Dynamic Network Embedding : An Extended Approach for Skip-gram based Network Embedding <https://www.ijcai.org/proceedings/2018/0288.pdf>`_ | :author:`Lun Du, Yun Wang, Guojie Song, Zhicong Lu, Junshan Wang` | :venue:`IJCAI 2018` `DyRep: Learning Representations over Dynamic Graphs <https://openreview.net/pdf?id=HyePrhR5KX>`_ | :author:`Rakshit Trivedi, Mehrdad Farajtabar, Prasenjeet Biswal, Hongyuan Zha` | :venue:`ICLR 2019` `Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks <https://cs.stanford.edu/~srijan/pubs/jodie-kdd2019.pdf>`_ | :author:`Srijan Kumar, Xikun Zhang, Jure Leskovec` | :venue:`KDD2019` Knowledge Graph Embedding ========================= `Translating Embeddings for Modeling Multi-relational Data <https://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data.pdf>`_ | :author:`Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, Oksana Yakhnenko` | :venue:`NIPS 2013` `Knowledge Graph Embedding by Translating on Hyperplanes <https://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/viewFile/8531/8546>`_ | :author:`Zhen Wang, Jianwen Zhang, Jianlin Feng, Zheng Chen` | :venue:`AAAI 2014` `Learning Entity and Relation Embeddings for Knowledge Graph Completion <https://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/viewFile/9571/9523>`_ | :author:`Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, Xuan Zhu` | :venue:`AAAI 2015` `Knowledge Graph Embedding via Dynamic Mapping Matrix <https://www.aclweb.org/anthology/P15-1067>`_ | :author:`Guoliang Ji, Shizhu He, Liheng Xu, Kang Liu, Jun Zha` | :venue:`ACL 2015` `Modeling Relation Paths for Representation Learning of Knowledge Bases <https://arxiv.org/pdf/1506.00379>`_ | :author:`Yankai Lin, Zhiyuan Liu, Huanbo Luan, Maosong Sun, Siwei Rao, Song Liu` | :venue:`EMNLP 2015` `Embedding Entities and Relations for Learning and Inference in Knowledge Bases <https://arxiv.org/pdf/1412.6575>`_ | :author:`Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, Li Deng` | :venue:`ICLR 2015` `Holographic Embeddings of Knowledge Graphs <https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/viewPDFInterstitial/12484/11828>`_ | :author:`Maximilian Nickel, Lorenzo Rosasco, Tomaso Poggio` | :venue:`AAAI 2016` `Complex Embeddings for Simple Link Prediction <http://www.jmlr.org/proceedings/papers/v48/trouillon16.pdf>`_ | :author:`Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, Guillaume Bouchard` | :venue:`ICML 2016` `Modeling Relational Data with Graph Convolutional Networks <https://arxiv.org/pdf/1703.06103>`_ | :author:`Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, Max Welling` | :venue:`arXiv 2017.03` `Fast Linear Model for Knowledge Graph Embeddings <https://arxiv.org/pdf/1710.10881>`_ | :author:`Armand Joulin, Edouard Grave, Piotr Bojanowski, Maximilian Nickel, Tomas Mikolov` | :venue:`arXiv 2017.10` `Convolutional 2D Knowledge Graph Embeddings <https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/download/17366/15884>`_ | :author:`Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, Sebastian Riedel` | :venue:`AAAI 2018` `Knowledge Graph Embedding With Iterative Guidance From Soft Rules <https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/download/16369/16011>`_ | :author:`Shu Guo, Quan Wang, Lihong Wang, Bin Wang, Li Guo` | :venue:`AAAI 2018` `KBGAN: Adversarial Learning for Knowledge Graph Embeddings <https://arxiv.org/abs/1711.04071>`_ | :author:`Liwei Cai, William Yang Wang` | :venue:`NAACL 2018` `Improving Knowledge Graph Embedding Using Simple Constraints <https://arxiv.org/abs/1805.02408>`_ | :author:`Boyang Ding, Quan Wang, Bin Wang, Li Guo` | :venue:`ACL 2018` `SimplE Embedding for Link Prediction in Knowledge Graphs <https://arxiv.org/abs/1802.04868>`_ | :author:`Seyed Mehran Kazemi, David Poole` | :venue:`NeurIPS 2018` `A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network <https://aclweb.org/anthology/papers/N/N18/N18-2053/>`_ | :author:`Dai Quoc Nguyen, Tu Dinh Nguyen, Dat Quoc Nguyen, Dinh Phung` | :venue:`NAACL 2018` `Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning <https://arxiv.org/abs/1903.08948>`_ | :author:`Wen Zhang, Bibek Paudel, Liang Wang, Jiaoyan Chen, Hai Zhu, Wei Zhang, Abraham Bernstein, Huajun Chen` | :venue:`WWW 2019` `RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space <https://arxiv.org/abs/1902.10197>`_ | :author:`Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, Jian Tang` | :venue:`ICLR 2019` `Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs <https://arxiv.org/abs/1906.01195>`_ | :author:`Deepak Nathani, Jatin Chauhan, Charu Sharma, Manohar Kaul` | :venue:`ACL 2019` `Probabilistic Logic Neural Networks for Reasoning <https://arxiv.org/pdf/1906.08495.pdf>`_ | :author:`Meng Qu, Jian Tang` | :venue:`arXiv 1906` Graph Neural Networks ===================== `Revisiting Semi-supervised Learning with Graph Embeddings <https://arxiv.org/pdf/1603.08861>`_ | :author:`Zhilin Yang, William W. Cohen, Ruslan Salakhutdinov` | :venue:`ICML 2016` `Semi-Supervised Classification with Graph Convolutional Networks <https://arxiv.org/pdf/1609.02907>`_ | :author:`Thomas N. Kipf, Max Welling` | :venue:`ICLR 2017` `Neural Message Passing for Quantum Chemistry <https://arxiv.org/pdf/1704.01212>`_ | :author:`Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, George E. Dahl` | :venue:`ICML 2017` `Motif-Aware Graph Embeddings <http://gearons.org/assets/docs/motif-aware-graph-final.pdf>`_ | :author:`Hoang Nguyen, Tsuyoshi Murata` | :venue:`IJCAI 2017` `Learning Graph Representations with Embedding Propagation <https://arxiv.org/pdf/1710.03059>`_ | :author:`Alberto Garcia-Duran, Mathias Niepert` | :venue:`NIPS 2017` `Inductive Representation Learning on Large Graphs <https://arxiv.org/pdf/1706.02216>`_ | :author:`William L. Hamilton, Rex Ying, Jure Leskovec` | :venue:`NIPS 2017` `Graph Attention Networks <https://arxiv.org/pdf/1710.10903>`_ | :author:`Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, Yoshua Bengio` | :venue:`ICLR 2018` `FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling <https://arxiv.org/pdf/1801.10247>`_ | :author:`Jie Chen, Tengfei Ma, Cao Xiao` | :venue:`ICLR 2018` `Representation Learning on Graphs with Jumping Knowledge Networks <https://arxiv.org/pdf/1806.03536>`_ | :author:`Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, Stefanie Jegelka` | :venue:`ICML 2018` `Stochastic Training of Graph Convolutional Networks with Variance Reduction <https://arxiv.org/pdf/1710.10568>`_ | :author:`Jianfei Chen, Jun Zhu, Le Song` | :venue:`ICML 2018` `Large-Scale Learnable Graph Convolutional Networks <https://arxiv.org/pdf/1808.03965>`_ | :author:`Hongyang Gao, Zhengyang Wang, Shuiwang Ji` | :venue:`KDD 2018` `Adaptive Sampling Towards Fast Graph Representation Learning <https://papers.nips.cc/paper/7707-adaptive-sampling-towards-fast-graph-representation-learning.pdf>`_ | :author:`Wenbing Huang, Tong Zhang, Yu Rong, Junzhou Huang` | :venue:`NeurIPS 2018` `Hierarchical Graph Representation Learning with Differentiable Pooling <https://arxiv.org/pdf/1806.08804>`_ | :author:`Rex Ying, Jiaxuan You, Christopher Morris, Xiang Ren, William L. Hamilton, Jure Leskovec` | :venue:`NeurIPS 2018` `Bayesian Semi-supervised Learning with Graph Gaussian Processes <https://papers.nips.cc/paper/7440-bayesian-semi-supervised-learning-with-graph-gaussian-processes.pdf>`_ | :author:`Yin Cheng Ng, Nicolò Colombo, Ricardo Silva` | :venue:`NeurIPS 2018` `Pitfalls of Graph Neural Network Evaluation <https://arxiv.org/pdf/1811.05868>`_ | :author:`Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, Stephan Günnemann` | :venue:`arXiv 2018.11` `Heterogeneous Graph Attention Network <https://arxiv.org/pdf/1903.07293>`_ | :author:`Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Peng Cui, P. Yu, Yanfang Ye` | :venue:`WWW 2019` `Bayesian graph convolutional neural networks for semi-supervised classification <https://arxiv.org/pdf/1811.11103.pdf>`_ | :author:`Yingxue Zhang, Soumyasundar Pal, Mark Coates, Deniz Üstebay` | :venue:`AAAI 2019` `How Powerful are Graph Neural Networks? <https://arxiv.org/pdf/1810.00826>`_ | :author:`Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka` | :venue:`ICLR 2019` `LanczosNet: Multi-Scale Deep Graph Convolutional Networks <https://arxiv.org/pdf/1901.01484>`_ | :author:`Renjie Liao, Zhizhen Zhao, Raquel Urtasun, Richard S. Zemel` | :venue:`ICLR 2019` `Graph Wavelet Neural Network <https://arxiv.org/pdf/1904.07785>`_ | :author:`Bingbing Xu, Huawei Shen, Qi Cao, Yunqi Qiu, Xueqi Cheng` | :venue:`ICLR 2019` `Supervised Community Detection with Line Graph Neural Networks <https://openreview.net/pdf?id=H1g0Z3A9Fm>`_ | :author:`Zhengdao Chen, Xiang Li, Joan Bruna` | :venue:`ICLR 2019` `Predict then Propagate: Graph Neural Networks meet Personalized PageRank <https://arxiv.org/pdf/1810.05997>`_ | :author:`Johannes Klicpera, Aleksandar Bojchevski, Stephan Günnemann` | :venue:`ICLR 2019` `Invariant and Equivariant Graph Networks <https://arxiv.org/pdf/1812.09902>`_ | :author:`Haggai Maron, Heli Ben-Hamu, Nadav Shamir, Yaron Lipman` | :venue:`ICLR 2019` `Capsule Graph Neural Network <https://openreview.net/pdf?id=Byl8BnRcYm>`_ | :author:`Zhang Xinyi, Lihui Chen` | :venue:`ICLR 2019` `MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing <https://arxiv.org/pdf/1905.00067>`_ | :author:`Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, Aram Galstyan` | :venue:`ICML 2019` `Graph U-Nets <https://arxiv.org/pdf/1905.05178>`_ | :author:`Hongyang Gao, Shuiwang Ji` | :venue:`ICML 2019` `Disentangled Graph Convolutional Networks <http://proceedings.mlr.press/v97/ma19a/ma19a.pdf>`_ | :author:`Jianxin Ma, Peng Cui, Kun Kuang, Xin Wang, Wenwu Zhu` | :venue:`ICML 2019` `GMNN: Graph Markov Neural Networks <https://arxiv.org/pdf/1905.06214>`_ | :author:`Meng Qu, Yoshua Bengio, Jian Tang` | :venue:`ICML 2019` `Simplifying Graph Convolutional Networks <https://arxiv.org/pdf/1902.07153>`_ | :author:`Felix Wu, Tianyi Zhang, Amauri Holanda de Souza Jr., Christopher Fifty, Tao Yu, Kilian Q. Weinberger` | :venue:`ICML 2019` `Position-aware Graph Neural Networks <https://arxiv.org/pdf/1906.04817>`_ | :author:`Jiaxuan You, Rex Ying, Jure Leskovec` | :venue:`ICML 2019` `Self-Attention Graph Pooling <https://arxiv.org/pdf/1904.08082>`_ | :author:`Junhyun Lee, Inyeop Lee, Jaewoo Kang` | :venue:`ICML 2019` `Relational Pooling for Graph Representations <https://arxiv.org/pdf/1903.02541>`_ | :author: `Ryan L. Murphy, Balasubramaniam Srinivasan, Vinayak Rao, Bruno Ribeiro` | :venue:`ICML 2019` `Graph Representation Learning via Hard and Channel-Wise Attention Networks <https://arxiv.org/pdf/1907.04652.pdf>`_ | :author: `Hongyang Gao, Shuiwang Ji` | :venue:`KDD 2019` Applications of Graph Neural Networks ===================================== Natural Language Processing --------------------------- `Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling <https://www.aclweb.org/anthology/D17-1159>`_ | :author:`Diego Marcheggiani, Ivan Titov` | :venue:`EMNLP 2017` `Graph Convolutional Encoders for Syntax-aware Neural Machine Translation <https://www.aclweb.org/anthology/D17-1209>`_ | :author:`Joost Bastings, Ivan Titov, Wilker Aziz, Diego Marcheggiani, Khalil Sima’an` | :venue:`EMNLP 2017` `Graph-based Neural Multi-Document Summarization <https://www.aclweb.org/anthology/K17-1045>`_ | :author:`Michihiro Yasunaga, Rui Zhang, Kshitijh Meelu, Ayush Pareek, Krishnan Srinivasan, Dragomir Radev` | :venue:`CoNLL 2017` `QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension <https://arxiv.org/pdf/1804.09541.pdf>`_ | :author:`Adams Wei Yu, David Dohan, Minh-Thang Luong, Rui Zhao, Kai Chen, Mohammad Norouzi, Quoc V. Le` | :venue:`ICLR 2018` `A Structured Self-attentive Sentence Embedding <https://arxiv.org/pdf/1703.03130.pdf>`_ | :author:`Zhouhan Lin, Minwei Feng, Cicero Nogueira dos Santos, Mo Yu, Bing Xiang, Bowen Zhou, Yoshua Bengio` | :venue:`ICLR 2018` `Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering <https://aclweb.org/anthology/C18-1280>`_ | :author:`Daniil Sorokin, Iryna Gurevych` | :venue:`COLING 2018` `Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks <https://www.aclweb.org/anthology/N18-2078>`_ | :author:`Diego Marcheggiani, Joost Bastings, Ivan Titov` | :venue:`NAACL 2018` `Linguistically-Informed Self-Attention for Semantic Role Labeling <https://www.aclweb.org/anthology/D18-1548>`_ | :author:`Emma Strubell, Patrick Verga, Daniel Andor, David Weiss, Andrew McCallum` | :venue:`EMNLP 2018` `Graph Convolution over Pruned Dependency Trees Improves Relation Extraction <https://aclweb.org/anthology/D18-1244>`_ | :author:`Yuhao Zhang, Peng Qi, Christopher D. Manning` | :venue:`EMNLP 2018` `A Graph-to-Sequence Model for AMR-to-Text Generation <https://www.aclweb.org/anthology/P18-1150>`_ | :author:`Linfeng Song, Yue Zhang, Zhiguo Wang, Daniel Gildea` | :venue:`ACL 2018` `Graph-to-Sequence Learning using Gated Graph Neural Networks <https://www.aclweb.org/anthology/P18-1026>`_ | :author:`Daniel Beck, Gholamreza Haffari, Trevor Cohn` | :venue:`ACL 2018` `Graph Convolutional Networks for Text Classification <https://arxiv.org/pdf/1809.05679.pdf>`_ | :author:`Liang Yao, Chengsheng Mao, Yuan Luo` | :venue:`AAAI 2019` `Differentiable Perturb-and-Parse: Semi-Supervised Parsing with a Structured Variational Autoencoder <https://openreview.net/pdf?id=BJlgNh0qKQ>`_ | :author:`Caio Corro, Ivan Titov` | :venue:`ICLR 2019.` `Structured Neural Summarization <https://arxiv.org/pdf/1811.01824.pdf>`_ | :author:`Patrick Fernandes, Miltiadis Allamanis, Marc Brockschmid` | :venue:`ICLR 2019` `Multi-task Learning over Graph Structures <https://arxiv.org/pdf/1811.10211.pdf>`_ | :author:`Pengfei Liu, Jie Fu, Yue Dong, Xipeng Qiu, Jackie Chi Kit Cheung` | :venue:`AAAI 2019` `Imposing Label-Relational Inductive Bias for Extremely Fine-Grained Entity Typing <https://arxiv.org/pdf/1903.02591.pdf>`_ | :author:`Wenhan Xiong, Jiawei Wu, Deren Lei, Mo Yu, Shiyu Chang, Xiaoxiao Guo, William Yang Wang` | :venue:`NAACL 2019` `Single Document Summarization as Tree Induction <https://www.aclweb.org/anthology/N19-1173>`_ | :author:`Yang Liu, Ivan Titov, Mirella Lapata` | :venue:`NAACL 2019` `Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks <https://arxiv.org/pdf/1903.01306.pdf>`_ | :author:`Ningyu Zhang, Shumin Deng, Zhanlin Sun, Guanying Wang, Xi Chen, Wei Zhang, Huajun Chen` | :venue:`NAACL 2019` `Graph Neural Networks with Generated Parameters for Relation Extraction <https://arxiv.org/pdf/1902.00756.pdf>`_ | :author:`Hao Zhu, Yankai Lin, Zhiyuan Liu, Jie Fu, Tat-seng Chua, Maosong Sun` | :venue:`ACL 2019` `Dynamically Fused Graph Network for Multi-hop Reasoning <https://arxiv.org/pdf/1905.06933.pdf>`_ | :author:`Yunxuan Xiao, Yanru Qu, Lin Qiu, Hao Zhou, Lei Li, Weinan Zhang, Yong Yu` | :venue:`ACL 2019` `Encoding Social Information with Graph Convolutional Networks for Political Perspective Detection in News Media <https://www.cs.purdue.edu/homes/dgoldwas//downloads/papers/LiG_acl_2019.pdf>`_ | :author:`Chang Li, Dan Goldwasser` | :venue:`ACL 2019` `Attention Guided Graph Convolutional Networks for Relation Extraction <https://arxiv.org/pdf/1906.07510.pdf>`_ | :author:`Zhijiang Guo, Yan Zhang, Wei Lu` | :venue:`ACL 2019` `Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks <https://arxiv.org/pdf/1809.04283.pdf>`_ | :author:`Shikhar Vashishth, Manik Bhandari, Prateek Yadav, Piyush Rai, Chiranjib Bhattacharyya, Partha Talukdar` | :venue:`ACL 2019` `GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction <https://tsujuifu.github.io/pubs/acl19_graph-rel.pdf>`_ | :author:`Tsu-Jui Fu, Peng-Hsuan Li, Wei-Yun Ma` | :venue:`ACL 2019` `Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs <https://arxiv.org/pdf/1905.07374.pdf>`_ | :author:`Ming Tu, Guangtao Wang, Jing Huang, Yun Tang, Xiaodong He, Bowen Zhou` | :venue:`ACL 2019` `Cognitive Graph for Multi-Hop Reading Comprehension at Scale <https://arxiv.org/pdf/1905.05460.pdf>`_ | :author:`Ming Ding, Chang Zhou, Qibin Chen, Hongxia Yang, Jie Tang` | :venue:`ACL 2019` `Coherent Comment Generation for Chinese Articles with a Graph-to-Sequence Model <https://arxiv.org/pdf/1906.01231.pdf>`_ | :author:`Wei Li, Jingjing Xu, Yancheng He, Shengli Yan, Yunfang Wu, Xu Sun` | :venue:`ACL 2019` `Matching Article Pairs with Graphical Decomposition and Convolutions <https://arxiv.org/pdf/1802.07459.pdf>`_ | :author:`Bang Liu, Di Niu, Haojie Wei, Jinghong Lin, Yancheng He, Kunfeng Lai, Yu Xu` | :venue:`ACL 2019` `Embedding Imputation with Grounded Language Information <https://arxiv.org/pdf/1906.03753.pdf>`_ | :author:`Ziyi Yang, Chenguang Zhu, Vin Sachidananda, Eric Darve` | :venue:`ACL 2019` `Look Again at the Syntax: Relational Graph Convolutional Network for Gendered Ambiguous Pronoun Resolution <https://arxiv.org/pdf/1905.08868.pdf>`_ | :author:`Yinchuan Xu, Junlin Yang` | :venue:`ACL 2019 Workshop` | :keyword:`https://github.com/ianycxu/RGCN-with-BERT` `Learning Graph Pooling and Hybrid Convolutional Operations for Text Representations <https://arxiv.org/pdf/1901.06965.pdf>`_ | :author:`Hongyang Gao, Yongjun Chen, Shuiwang Ji` | :venue:`WWW 2019` Computer Vision --------------- `3D Graph Neural Networks for RGBD Semantic Segmentation <http://www.cs.toronto.edu/~rjliao/papers/iccv_2017_3DGNN.pdf>`_ | :author:`Xiaojuan Qi, Renjie Liao, Jiaya Jia, Sanja Fidler, Raquel Urtasun` | :venue:`ICCV 2017` `Situation Recognition With Graph Neural Networks <https://arxiv.org/abs/1708.04320>`_ | :author:`Ruiyu Li, Makarand Tapaswi, Renjie Liao, Jiaya Jia, Raquel Urtasun, Sanja Fidler` | :venue:`ICCV 2017` `Graph-Based Classification of Omnidirectional Images <https://arxiv.org/abs/1707.08301>`_ | :author:`Renata Khasanova, Pascal Frossard` | :venue:`ICCV 2017` `Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition <https://arxiv.org/abs/1801.07455>`_ | :author:`Sijie Yan, Yuanjun Xiong, Dahua Lin` | :venue:`AAAI 2018` `Image Generation from Scene Graphs <https://arxiv.org/abs/1804.01622>`_ | :author:`Justin Johnson, Agrim Gupta, Li Fei-Fei` | :venue:`CVPR 2018` `FoldingNet: Point Cloud Auto-Encoder via Deep Grid Deformation <https://arxiv.org/abs/1712.07262>`_ | :author:`Yaoqing Yang, Chen Feng, Yiru Shen, Dong Tian` | :venue:`CVPR 2018` `PPFNet: Global Context Aware Local Features for Robust 3D Point Matching <https://arxiv.org/abs/1802.02669>`_ | :author:`Haowen Deng, Tolga Birdal, Slobodan Ilic` | :venue:`CVPR 2018` `Iterative Visual Reasoning Beyond Convolutions <https://arxiv.org/abs/1803.11189>`_ | :author:`Xinlei Chen, Li-Jia Li, Li Fei-Fei, Abhinav Gupta` | :venue:`CVPR 2018` `Surface Networks <https://arxiv.org/abs/1705.10819>`_ | :author:`Ilya Kostrikov, Zhongshi Jiang, Daniele Panozzo, Denis Zorin, Joan Bruna` | :venue:`CVPR 2018` `FeaStNet: Feature-Steered Graph Convolutions for 3D Shape Analysis <https://arxiv.org/abs/1706.05206>`_ | :author:`Nitika Verma, Edmond Boyer, Jakob Verbeek` | :venue:`CVPR 2018` `Learning to Act Properly: Predicting and Explaining Affordances From Images <https://arxiv.org/abs/1712.07576>`_ | :author:`Ching-Yao Chuang, Jiaman Li, Antonio Torralba, Sanja Fidler` | :venue:`CVPR 2018` `Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling <https://arxiv.org/abs/1712.06760>`_ | :author:`Yiru Shen, Chen Feng, Yaoqing Yang, Dong Tian` | :venue:`CVPR 2018` `Deformable Shape Completion With Graph Convolutional Autoencoders <https://arxiv.org/abs/1712.00268>`_ | :author:`Or Litany, Alex Bronstein, Michael Bronstein, Ameesh Makadia` | :venue:`CVPR 2018` `Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images <https://arxiv.org/abs/1804.01654>`_ | :author:`Nanyang Wang, Yinda Zhang, Zhuwen Li, Yanwei Fu, Wei Liu, Yu-Gang Jiang` | :venue:`ECCV 2018` `Learning Human-Object Interactions by Graph Parsing Neural Networks <https://arxiv.org/abs/1808.07962>`_ | :author:`Siyuan Qi, Wenguan Wang, Baoxiong Jia, Jianbing Shen, Song-Chun Zhu` | :venue:`ECCV 2018` `Generating 3D Faces using Convolutional Mesh Autoencoders <https://arxiv.org/abs/1807.10267>`_ | :author:`Anurag Ranjan, Timo Bolkart, Soubhik Sanyal, Michael J. Black` | :venue:`ECCV 2018` `Learning SO(3) Equivariant Representations with Spherical CNNs <https://arxiv.org/abs/1711.06721>`_ | :author:`Carlos Esteves, Christine Allen-Blanchette, Ameesh Makadia, Kostas Daniilidis` | :venue:`ECCV 2018` `Neural Graph Matching Networks for Fewshot 3D Action Recognition <http://openaccess.thecvf.com/content_ECCV_2018/papers/Michelle_Guo_Neural_Graph_Matching_ECCV_2018_paper.pdf>`_ | :author:`Michelle Guo, Edward Chou, De-An Huang, Shuran Song, Serena Yeung, Li Fei-Fei` | :venue:`ECCV 2018` `Multi-Kernel Diffusion CNNs for Graph-Based Learning on Point Clouds <https://arxiv.org/abs/1809.05370>`_ | :author:`Lasse Hansen, Jasper Diesel, Mattias P. Heinrich` | :venue:`ECCV 2018` `Hierarchical Video Frame Sequence Representation with Deep Convolutional Graph Network <https://arxiv.org/abs/1906.00377>`_ | :author:`Feng Mao, Xiang Wu, Hui Xue, Rong Zhang` | :venue:`ECCV 2018` `Graph R-CNN for Scene Graph Generation <https://arxiv.org/abs/1808.00191>`_ | :author:`Jianwei Yang, Jiasen Lu, Stefan Lee, Dhruv Batra, Devi Parikh` | :venue:`ECCV 2018` `Exploring Visual Relationship for Image Captioning <https://arxiv.org/abs/1809.07041>`_ | :author:`Ting Yao, Yingwei Pan, Yehao Li, Tao Mei` | :venue:`ECCV 2018` `Beyond Grids: Learning Graph Representations for Visual Recognition <https://papers.nips.cc/paper/8135-beyond-grids-learning-graph-representations-for-visual-recognition>`_ | :author:`Yin Li, Abhinav Gupta` | :venue:`NeurIPS 2018` `Learning Conditioned Graph Structures for Interpretable Visual Question Answering <https://arxiv.org/abs/1806.07243>`_ | :author:`Will Norcliffe-Brown, Efstathios Vafeias, Sarah Parisot` | :venue:`NeurIPS 2018` `LinkNet: Relational Embedding for Scene Graph <https://arxiv.org/abs/1811.06410>`_ | :author:`Sanghyun Woo, Dahun Kim, Donghyeon Cho, In So Kweon` | :venue:`NeurIPS 2018` `Flexible Neural Representation for Physics Prediction <https://arxiv.org/abs/1806.08047>`_ | :author:`Damian Mrowca, Chengxu Zhuang, Elias Wang, Nick Haber, Li Fei-Fei, Joshua B. Tenenbaum, Daniel L. K. Yamins` | :venue:`NeurIPS 2018` `Learning Localized Generative Models for 3D Point Clouds via Graph Convolution <https://openreview.net/forum?id=SJeXSo09FQ>`_ | :author:`Diego Valsesia, Giulia Fracastoro, Enrico Magli` | :venue:`ICLR 2019` `Graph-Based Global Reasoning Networks <https://arxiv.org/abs/1811.12814>`_ | :author:`Yunpeng Chen, Marcus Rohrbach, Zhicheng Yan, Shuicheng Yan, Jiashi Feng, Yannis Kalantidis` | :venue:`CVPR 2019` `Deep Graph Laplacian Regularization for Robust Denoising of Real Images <https://arxiv.org/abs/1807.11637>`_ | :author:`Jin Zeng, Jiahao Pang, Wenxiu Sun, Gene Cheung` | :venue:`CVPR 2019` `Learning Context Graph for Person Search <https://arxiv.org/abs/1904.01830>`_ | :author:`Yichao Yan, Qiang Zhang, Bingbing Ni, Wendong Zhang, Minghao Xu, Xiaokang Yang` | :venue:`CVPR 2019` `Graphonomy: Universal Human Parsing via Graph Transfer Learning <https://arxiv.org/abs/1904.04536>`_ | :author:`Ke Gong, Yiming Gao, Xiaodan Liang, Xiaohui Shen, Meng Wang, Liang Lin` | :venue:`CVPR 2019` `Masked Graph Attention Network for Person Re-Identification <http://openaccess.thecvf.com/content_CVPRW_2019/html/TRMTMCT/Bao_Masked_Graph_Attention_Network_ for_Person_Re-Identification_CVPRW_2019_paper.html>`_ | :author:`Liqiang Bao, Bingpeng Ma, Hong Chang, Xilin Chen` | :venue:`CVPR 2019` `Learning to Cluster Faces on an Affinity Graph <https://arxiv.org/abs/1904.02749>`_ | :author:`Lei Yang, Xiaohang Zhan, Dapeng Chen, Junjie Yan, Chen Change Loy, Dahua Lin` | :venue:`CVPR 2019` `Actional-Structural Graph Convolutional Networks for Skeleton-Based Action Recognition <https://arxiv.org/abs/1904.12659>`_ | :author:`Maosen Li, Siheng Chen, Xu Chen, Ya Zhang, Yanfeng Wang, Qi Tian` | :venue:`CVPR 2019` `Adaptively Connected Neural Networks <https://arxiv.org/abs/1904.03579>`_ | :author:`Guangrun Wang, Keze Wang, Liang Lin` | :venue:`CVPR 2019` `Reasoning Visual Dialogs with Structural and Partial Observations <https://arxiv.org/abs/1904.03579>`_ | :author:`Zilong Zheng, Wenguan Wang, Siyuan Qi, Song-Chun Zhu` | :venue:`CVPR 2019` `MeshCNN: A Network with an Edge <https://arxiv.org/pdf/1809.05910.pdf>`_ | :author:`Rana Hanocka, Amir Hertz, Noa Fish, Raja Giryes, Shachar Fleishman, Daniel Cohen-Or` | :venue:`SIGGRAPH 2019` | :keyword:`https://ranahanocka.github.io/MeshCNN/` Recommender Systems ------------------- `Graph Convolutional Neural Networks for Web-Scale Recommender Systems <https://arxiv.org/pdf/1806.01973.pdf>`_ | :author:`Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, Jure Leskovec` | :venue:`KDD 2018` | :keyword:`PinSage` `SocialGCN: An Efficient Graph Convolutional Network based Model for Social Recommendation <https://arxiv.org/pdf/1811.02815.pdf>`_ | :author:`Le Wu, Peijie Sun, Richang Hong, Yanjie Fu, Xiting Wang, Meng Wang` | :venue:`AAAI 2018` | :keyword:`GCN, Social recommendation` `Session-based Social Recommendation via Dynamic Graph Attention Networks <https://arxiv.org/pdf/1902.09362.pdf>`_ | :author:`Weiping Song, Zhiping Xiao, Yifan Wang, Laurent Charlin, Ming Zhang, Jian Tang` | :venue:`WSDM 2019` | :keyword:`Social recommendation, session-based, GAT` `Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems <https://arxiv.org/pdf/1903.10433.pdf>`_ | :author:`Qitian Wu, Hengrui Zhang, Xiaofeng Gao, Peng He, Paul Weng, Han Gao, Guihai Chen` | :venue:`WWW 2019` | :keyword:`Social recommendation, GAT` `Graph Neural Networks for Social Recommendation <https://arxiv.org/pdf/1902.07243.pdf>`_ | :author:`Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, Dawei Yin` | :venue:`WWW 2019` | :keyword:`Social recommendation, GNN` `Session-based Recommendation with Graph Neural Networks <https://arxiv.org/pdf/1811.00855.pdf>`_ | :author:`Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, Tieniu Tan` | :venue:`AAAI 2019` | :keyword:`Session-based recommendation, GNN` `A Neural Influence Diffusion Model for Social Recommendation <https://arxiv.org/pdf/1904.10322.pdf>`_ | :author:`Le Wu, Peijie Sun, Yanjie Fu, Richang Hong, Xiting Wang, Meng Wang` | :venue:`SIGIR 2019` | :keyword:`Social Recommendation, diffusion` `Neural Graph Collaborative Filtering <https://arxiv.org/pdf/1905.08108.pdf>`_ | :author:`Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, Tat-Seng Chua` | :venue:`SIGIR 2019` | :keyword:`Collaborative Filtering, GNN` `Binarized Collaborative Filtering with Distilling Graph Convolutional Networks <https://arxiv.org/pdf/1906.01829.pdf>`_ | :author:`Haoyu Wang, Defu Lian, Yong Ge` | :venue:`IJCAI 2019` Link Prediction --------------- `Link Prediction Based on Graph Neural Networks <https://papers.nips.cc/paper/7763-link-prediction-based-on-graph-neural-networks.pdf>`_ | :author:`Muhan Zhang, Yixin Chen` | :venue:`NeurIPS 2018` `Link Prediction via Subgraph Embedding-Based Convex Matrix Completion <http://iiis.tsinghua.edu.cn/~weblt/papers/link-prediction-subgraphembeddings.pdf>`_ | :author:`Zhu Cao, Linlin Wang, Gerard de Melo` | :venue:`AAAI 2018` `Graph Convolutional Matrix Completion <https://www.kdd.org/kdd2018/files/deep-learning-day/DLDay18_paper_32.pdf>`_ | :author:`Rianne van den Berg, Thomas N. Kipf, Max Welling` | :venue:`KDD 2018 Workshop` Influence Prediction -------------------- `DeepInf: Social Influence Prediction with Deep Learning <https://arxiv.org/pdf/1807.05560.pdf>`_ | :author:`Jiezhong Qiu, Jian Tang, Hao Ma, Yuxiao Dong, Kuansan Wang, Jie Tang` | :venue:`KDD 2018` `Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks <https://arxiv.org/pdf/1905.08865.pdf>`_ | :author:`Namyong Park, Andrey Kan, Xin Luna Dong, Tong Zhao, Christos Faloutsos` | :venue:`KDD 2019` Neural Architecture Search -------------------------- `Graph HyperNetworks for Neural Architecture Search <https://openreview.net/pdf?id=rkgW0oA9FX>`_ | :author:`Chris Zhang, Mengye Ren, Raquel Urtasun` | :venue:`ICLR 2019` Reinforcement Learning ---------------------- `Action Schema Networks: Generalised Policies with Deep Learning <https://arxiv.org/pdf/1709.04271.pdf>`_ | :author:`Sam Toyer, Felipe Trevizan, Sylvie Thiebaux, Lexing Xie` | :venue:`AAAI 2018` `NerveNet: Learning Structured Policy with Graph Neural Networks <https://openreview.net/pdf?id=S1sqHMZCb>`_ | :author:`Tingwu Wang, Renjie Liao, Jimmy Ba, Sanja Fidler` | :venue:`ICLR 2018` `Graph Networks as Learnable Physics Engines for Inference and Control <https://arxiv.org/pdf/1806.01242.pdf>`_ | :author:`Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg, Josh Merel, Martin Riedmiller` | :venue:`ICML 2018` `Learning Policy Representations in Multiagent Systems <https://arxiv.org/pdf/1806.06464.pdf>`_ | :author:`Aditya Grover, Maruan Al-Shedivat, Jayesh K. Gupta, Yura Burda, Harrison Edwards` | :venue:`ICML 2018` `Relational recurrent neural networks <https://papers.nips.cc/paper/7960-relational-recurrent-neural-networks.pdf>`_ | :author:`Adam Santoro, Ryan Faulkner, David Raposo, Jack Rae, Mike Chrzanowski,Théophane Weber, Daan Wierstra, Oriol Vinyals, Razvan Pascanu, Timothy Lillicrap` | :venue:`NeurIPS 2018` `Transfer of Deep Reactive Policies for MDP Planning <http://www.cse.iitd.ac.in/~mausam/papers/nips18.pdf>`_ | :author:`Aniket Bajpai, Sankalp Garg, Mausam` | :venue:`NeurIPS 2018` `Neural Graph Evolution: Towards Efficient Automatic Robot Design <https://openreview.net/pdf?id=BkgWHnR5tm>`_ | :author:`Tingwu Wang, Yuhao Zhou, Sanja Fidler, Jimmy Ba` | :venue:`ICLR 2019` Combinatorial Optimization -------------------------- `Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search <https://arxiv.org/abs/1810.10659>`_ | :author:`Zhuwen Li, Qifeng Chen, Vladlen Koltun` | :venue:`NeurIPS 2018` `Reinforcement Learning for Solving the Vehicle Routing Problem <https://arxiv.org/abs/1802.04240>`_ | :author:`Mohammadreza Nazari, Afshin Oroojlooy, Lawrence V. Snyder, Martin Takáč` | :venue:`NeurIPS 2018` Adversarial Attack ------------------ `Adversarial Attack on Graph Structured Data <https://arxiv.org/abs/1806.02371>`_ | :author:`Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, Le Song` | :venue:`ICML 2018` `Adversarial Attacks on Neural Networks for Graph Data <https://arxiv.org/abs/1805.07984>`_ | :author:`Daniel Zügner, Amir Akbarnejad, Stephan Günnemann` | :venue:`KDD 2018` `Adversarial Attacks on Graph Neural Networks via Meta Learning <https://arxiv.org/abs/1902.08412>`_ | :author:`Daniel Zügner, Stephan Günnemann` | :venue:`ICLR 2019` Meta Learning ------------- `Learning Steady-States of Iterative Algorithms over Graphs <http://proceedings.mlr.press/v80/dai18a.html>`_ | :author:`Hanjun Dai, Zornitsa Kozareva, Bo Dai, Alex Smola, Le Song` | :venue:`ICML 2018` Structure Learning ------------------ `Few-Shot Learning with Graph Neural Networks <https://arxiv.org/abs/1711.04043>`_ | :author:`Victor Garcia, Joan Bruna` | :venue:`ICLR 2018` `Neural Relational Inference for Interacting Systems <https://arxiv.org/abs/1802.04687>`_ | :author:`Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, Richard Zemel` | :venue:`ICML 2018` `Brain Signal Classification via Learning Connectivity Structure <https://arxiv.org/abs/1905.11678>`_ | :author:`Soobeom Jang, Seong-Eun Moon, Jong-Seok Lee` | :venue:`arXiv 1905` `A Flexible Generative Framework for Graph-based Semi-supervised Learning <https://arxiv.org/abs/1905.10769>`_ | :author:`Jiaqi Ma, Weijing Tang, Ji Zhu, Qiaozhu Mei` | :venue:`arXiv 1905` `Joint embedding of structure and features via graph convolutional networks <https://arxiv.org/abs/1905.08636>`_ | :author:`Sébastien Lerique, Jacob Levy Abitbol, Márton Karsai` | :venue:`arXiv 1905` `Variational Spectral Graph Convolutional Networks <https://arxiv.org/abs/1906.01852>`_ | :author:`Louis Tiao, Pantelis Elinas, Harrison Nguyen, Edwin V. Bonilla` | :venue:`arXiv 1906` `Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning <https://arxiv.org/abs/1805.10002>`_ | :author:`Yanbin Liu, Juho Lee, Minseop Park, Saehoon Kim, Eunho Yang, Sung Ju Hwang, Yi Yang` | :venue:`ICLR 2019` `Graph Learning Network: A Structure Learning Algorithm <https://arxiv.org/abs/1905.12665>`_ | :author:`Darwin Saire Pilco, Adín Ramírez Rivera` | :venue:`ICML 2019 Workshop` `Learning Discrete Structures for Graph Neural Networks <https://arxiv.org/abs/1903.11960>`_ | :author:`Luca Franceschi, Mathias Niepert, Massimiliano Pontil, Xiao He` | :venue:`ICML 2019` `Graphite: Iterative Generative Modeling of Graphs <https://arxiv.org/abs/1803.10459>`_ | :author:`Aditya Grover, Aaron Zweig, Stefano Ermon` | :venue:`ICML 2019` Bioinformatics and Chemistry -------------- `Protein Interface Prediction using Graph Convolutional Networks <https://papers.nips.cc/paper/7231-protein-interface-prediction-using-graph-convolutional-networks.pdf>`_ | :author:`Alex Fout, Jonathon Byrd, Basir Shariat, Asa Ben-Hur` | :venue:`NeurIPS 2017` `Modeling Polypharmacy Side Effects with Graph Convolutional Networks <https://arxiv.org/abs/1802.00543>`_ | :author:`Marinka Zitnik, Monica Agrawal, Jure Leskovec` | :venue:`Bioinformatics 2018` `NeoDTI: Neural Integration of Neighbor Information from a Heterogeneous Network for Discovering New Drug–target Interactions <https://academic.oup.com/bioinformatics/article-abstract/35/1/104/5047760?redirectedFrom=fulltext>`_ | :author:`Fangping Wan, Lixiang Hong, An Xiao, Tao Jiang, Jianyang Zeng` | :venue:`Bioinformatics 2018` `SELFIES: a Robust Representation of Semantically Constrained Graphs with an Example Application in Chemistry <https://arxiv.org/pdf/1905.13741.pdf>`_ | :author:`Mario Krenn, Florian Häse, AkshatKumar Nigam, Pascal Friederich, Alán Aspuru-Guzik` | :venue:`arXiv 1905` `Drug-Drug Adverse Effect Prediction with Graph Co-Attention <https://arxiv.org/pdf/1905.00534.pdf>`_ | :author:`Andreea Deac, Yu-Hsiang Huang, Petar Veličković, Pietro Liò, Jian Tang` | :venue:`arXiv 1905` Theorem Proving --------------- `Premise Selection for Theorem Proving by Deep Graph Embedding <https://arxiv.org/abs/1709.09994>`_ | :author:`Mingzhe Wang, Yihe Tang, Jian Wang, Jia Deng` | :venue:`NeurIPS 2017` Graph Generation ================ `GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models <https://arxiv.org/abs/1802.08773>`_ | :author:`Jiaxuan You, Rex Ying, Xiang Ren, William L. Hamilton, Jure Leskovec` | :venue:`ICML 2018` `NetGAN: Generating Graphs via Random Walks <https://arxiv.org/abs/1803.00816>`_ | :author:`Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, Stephan Günnemann` | :venue:`ICML 2018` `Junction Tree Variational Autoencoder for Molecular Graph Generation <https://arxiv.org/abs/1802.04364>`_ | :author:`Wengong Jin, Regina Barzilay, Tommi Jaakkola` | :venue:`ICML 2018` `MolGAN: An implicit generative model for small molecular graphs <https://arxiv.org/abs/1805.11973>`_ | :author:`Nicola De Cao, Thomas Kipf` | :venue:`arXiv 1805` `Generative Modeling for Protein Structures <https://papers.nips.cc/paper/7978-generative-modeling-for-protein-structures.pdf>`_ | :author:`Namrata Anand, Po-Ssu Huang` | :venue:`NeurIPS 2018` `Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders <https://arxiv.org/abs/1809.02630>`_ | :author:`Tengfei Ma, Jie Chen, Cao Xiao` | :venue:`NeurIPS 2018` `Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation <https://arxiv.org/abs/1806.02473>`_ | :author:`Jiaxuan You, Bowen Liu, Rex Ying, Vijay Pande, Jure Leskovec` | :venue:`NeurIPS 2018` `Constrained Graph Variational Autoencoders for Molecule Design <https://arxiv.org/abs/1805.09076>`_ | :author:`Qi Liu, Miltiadis Allamanis, Marc Brockschmidt, Alexander L. Gaunt` | :venue:`NeurIPS 2018` `Learning Multimodal Graph-to-Graph Translation for Molecule Optimization <https://arxiv.org/abs/1812.01070>`_ | :author:`Wengong Jin, Kevin Yang, Regina Barzilay, Tommi Jaakkola` | :venue:`ICLR 2019` `DAG-GNN: DAG Structure Learning with Graph Neural Networks <https://arxiv.org/abs/1904.10098>`_ | :author:`Yue Yu, Jie Chen, Tian Gao, Mo Yu` | :venue:`ICML 2019` `Graph to Graph: a Topology Aware Approach for Graph Structures Learning and Generation <http://proceedings.mlr.press/v89/sun19c.html>`_ | :author:`Mingming Sun, Ping Li` | :venue:`AISTATS 2019` Graph Layout and High-dimensional Data Visualization ==================================================== `Visualizing Data using t-SNE <http://www.jmlr.org/papers/volume9/vandermaaten08a/vandermaaten08a.pdf>`_ | :author:`Laurens van der Maaten, Geoffrey Hinton` | :venue:`JMLR 2008` `Visualizing non-metric similarities in multiple maps <https://link.springer.com/content/pdf/10.1007/s10994-011-5273-4.pdf>`_ | :author:`Laurens van der Maaten, Geoffrey Hinton` | :venue:`ML 2012` `Visualizing Large-scale and High-dimensional Data <https://arxiv.org/pdf/1602.00370>`_ | :author:`Jian Tang, Jingzhou Liu, Ming Zhang, Qiaozhu Mei` | :venue:`WWW 2016` `GraphTSNE: A Visualization Technique for Graph-Structured Data <https://arxiv.org/pdf/1904.06915.pdf>`_ | :author:`Yao Yang Leow, Thomas Laurent, Xavier Bresson` | :venue:`ICLR 2019 Workshop` Graph Representation Learning Systems ===================================== `GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding <https://arxiv.org/pdf/1903.00757>`_ | :author:`Zhaocheng Zhu, Shizhen Xu, Meng Qu, Jian Tang` | :venue:`WWW 2019` `PyTorch-BigGraph: A Large-scale Graph Embedding System <https://arxiv.org/pdf/1903.12287>`_ | :author:`Adam Lerer, Ledell Wu, Jiajun Shen, Timothee Lacroix, Luca Wehrstedt, Abhijit Bose, Alex Peysakhovich` | :venue:`SysML 2019` `AliGraph: A Comprehensive Graph Neural Network Platform <https://arxiv.org/pdf/1902.08730>`_ | :author:`Rong Zhu, Kun Zhao, Hongxia Yang, Wei Lin, Chang Zhou, Baole Ai, Yong Li, Jingren Zhou` | :venue:`VLDB 2019` `Deep Graph Library <https://www.dgl.ai>`_ | :author:`DGL Team` `AmpliGraph <https://github.com/Accenture/AmpliGraph>`_ | :author:`Luca Costabello, Sumit Pai, Chan Le Van, Rory McGrath, Nicholas McCarthy, Pedro Tabacof` `Euler <https://github.com/alibaba/euler>`_ | :author:`Alimama Engineering Platform Team, Alimama Search Advertising Algorithm Team` Datasets ========