【資源推薦】圖神經網絡（GNN）近年論文分類集錦

Survey Papers

1. Graph Neural Networks: A Review of Methods and Applications. arxiv 2018.

   Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Maosong Sun.

2. Adversarial Attack and Defense on Graph Data: A Survey. arxiv 2018.

   Lichao Sun, Yingtong Dou, Carl Yang, Ji Wang, Philip S. Yu, Bo Li.

3. Deep Learning on Graphs: A Survey. arxiv 2018.

   Ziwei Zhang, Peng Cui, Wenwu Zhu.

4. Relational Inductive Biases, Deep Learning, and Graph Networks. arxiv 2018.

   Battaglia, Peter W and Hamrick, Jessica B and Bapst, Victor and Sanchez-Gonzalez, Alvaro and Zambaldi, Vinicius and Malinowski, Mateusz and Tacchetti, Andrea and Raposo, David and Santoro, Adam and Faulkner, Ryan and others.

5. A Comprehensive Survey on Graph Neural Networks. arxiv 2019.

   Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, Philip S. Yu.

6. Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey. arxiv 2020.

   Skarding, Joakim and Gabrys, Bogdan and Musial, Katarzyna.

7. Introduction to Graph Neural Networks. Synthesis Lectures on Artificial Intelligence and Machine Learning, Morgan & Claypool Publishers, 2020.

   Zhiyuan Liu, Jie Zhou.

8. Deep Learning on Graphs. 2020

   Yao Ma, Jiliang Tang

GNN Models

1. Basic Models

1. Supervised Neural Networks for the Classification of Structures. IEEE TNN 1997.

   Alessandro Sperduti and Antonina Starita.

2. Graphical-Based Learning Environments for Pattern Recognition. SSPR/SPR 2004.

   Franco Scarselli, Ah Chung Tsoi, Marco Gori, Markus Hagenbuchner.

3. A new model for learning in graph domains. IJCNN 2005.

   Marco Gori, Gabriele Monfardini, Franco Scarselli.

4. Graph Neural Networks for Ranking Web Pages. WI 2005.

   Franco Scarselli, Sweah Liang Yong, Marco Gori, Markus Hagenbuchner, Ah Chung Tsoi, Marco Maggini.

5. Neural Network for Graphs: A Contextual Constructive Approach. IEEE TNN 2009.

   Alessio Micheli.

6. Spectral Networks and Locally Connected Networks on Graphs. ICLR 2014.

   Joan Bruna, Wojciech Zaremba, Arthur Szlam, Yann LeCun.

7. Deep Convolutional Networks on Graph-Structured Data. arxiv 2015.

   Mikael Henaff, Joan Bruna, Yann LeCun.

8. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. NIPS 2016.

   Michaël Defferrard, Xavier Bresson, Pierre Vandergheynst.

9. Diffusion-Convolutional Neural Networks. NIPS 2016.

   James Atwood, Don Towsley.

10. Gated Graph Sequence Neural Networks. ICLR 2016.

    Yujia Li, Daniel Tarlow, Marc Brockschmidt, Richard Zemel.

11. Learning Convolutional Neural Networks for Graphs. ICML 2016.

    Mathias Niepert, Mohamed Ahmed, Konstantin Kutzkov.

12. Semantic Object Parsing with Graph LSTM. ECCV 2016.

    Xiaodan Liang, Xiaohui Shen, Jiashi Feng, Liang Lin, Shuicheng Yan.

13. Semi-Supervised Classification with Graph Convolutional Networks. ICLR 2017.

    Thomas N. Kipf, Max Welling.

14. Inductive Representation Learning on Large Graphs. NIPS 2017.

    William L. Hamilton, Rex Ying, Jure Leskovec.

15. Geometric deep learning on graphs and manifolds using mixture model cnns. CVPR 2017.

    Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodolà, Jan Svoboda, Michael M. Bronstein.

16. Graph Attention Networks. ICLR 2018.

    Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, Yoshua Bengio.

17. Covariant Compositional Networks For Learning Graphs. ICLR 2018.

    Risi Kondor, Hy Truong Son, Horace Pan, Brandon Anderson, Shubhendu Trivedi.

18. Graph Partition Neural Networks for Semi-Supervised Classification. ICLR 2018.

    Renjie Liao, Marc Brockschmidt, Daniel Tarlow, Alexander L. Gaunt, Raquel Urtasun, Richard Zemel.

19. Inference in Probabilistic Graphical Models by Graph Neural Networks. ICLR Workshop 2018.

    KiJung Yoon, Renjie Liao, Yuwen Xiong, Lisa Zhang, Ethan Fetaya, Raquel Urtasun, Richard Zemel, Xaq Pitkow.

20. Structure-Aware Convolutional Neural Networks. NeurIPS 2018.

    Jianlong Chang, Jie Gu, Lingfeng Wang, Gaofeng Meng, Shiming Xiang, Chunhong Pan.

21. Bayesian Semi-supervised Learning with Graph Gaussian Processes. NeurIPS 2018.

    Yin Cheng Ng, Nicolò Colombo, Ricardo Silva.

22. Adaptive Graph Convolutional Neural Networks. AAAI 2018.

    Ruoyu Li, Sheng Wang, Feiyun Zhu, Junzhou Huang.

23. Dual Graph Convolutional Networks for Graph-Based Semi-Supervised Classification. WWW 2018.

    Chenyi Zhuang, Qiang Ma.

24. Learning Steady-States of Iterative Algorithms over Graphs. ICML 2018.

    Hanjun Dai, Zornitsa Kozareva, Bo Dai, Alex Smola, Le Song.

25. Graph Capsule Convolutional Neural Networks. ICML 2018 Workshop.

    Saurabh Verma, Zhi-Li Zhang.

26. Capsule Graph Neural Network. ICLR 2019.

    Zhang Xinyi, Lihui Chen.

27. Graph Wavelet Neural Network. ICLR 2019.

    Bingbing Xu, Huawei Shen, Qi Cao, Yunqi Qiu, Xueqi Cheng.

28. Deep Graph Infomax. ICLR 2019.

    Petar Veličković, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, R Devon Hjelm.

29. Predict then Propagate: Graph Neural Networks meet Personalized PageRank. ICLR 2019.

    Johannes Klicpera, Aleksandar Bojchevski, Stephan Günnemann.

30. LanczosNet: Multi-Scale Deep Graph Convolutional Networks. ICLR 2019.

    Renjie Liao, Zhizhen Zhao, Raquel Urtasun, Richard Zemel.

31. Invariant and Equivariant Graph Networks. ICLR 2019.

    Haggai Maron, Heli Ben-Hamu, Nadav Shamir, Yaron Lipman.

32. GMNN: Graph Markov Neural Networks. ICML 2019.

    Meng Qu, Yoshua Bengio, Jian Tang.

33. Position-aware Graph Neural Networks. ICML 2019.

    Jiaxuan You, Rex Ying, Jure Leskovec.

34. Disentangled Graph Convolutional Networks. ICML 2019.

    Jianxin Ma, Peng Cui, Kun Kuang, Xin Wang, Wenwu Zhu.

35. Stochastic Blockmodels meet Graph Neural Networks. ICML 2019.

    Nikhil Mehta, Lawrence Carin, Piyush Rai.

36. Learning Discrete Structures for Graph Neural Networks. ICML 2019.

    Luca Franceschi, Mathias Niepert, Massimiliano Pontil, Xiao He.

37. MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing. ICML 2019.

    Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, Aram Galstyan.

38. DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification. KDD 2019.

    Jun Wu, Jingrui He, Jiejun Xu.

39. Graph Representation Learning via Hard and Channel-Wise Attention Networks. KDD 2019.

    Hongyang Gao, Shuiwang Ji.

40. Graph Learning-Convolutional Networks. CVPR 2019.

    Bo Jiang, Ziyan Zhang, Doudou Lin, Jin Tang.

41. Data Representation and Learning with Graph Diffusion-Embedding Networks. CVPR 2019.

    Bo Jiang, Doudou Lin, Jin Tang, Bin Luo.

42. Label Efficient Semi-Supervised Learning via Graph Filtering. CVPR 2019.

    Qimai Li, Xiao-Ming Wu, Han Liu, Xiaotong Zhang, Zhichao Guan.

43. SPAGAN: Shortest Path Graph Attention Network. IJCAI 2019.

    Yiding Yang, Xinchao Wang, Mingli Song, Junsong Yuan, Dacheng Tao.

44. Topology Optimization based Graph Convolutional Network. IJCAI 2019.

    Liang Yang, Zesheng Kang, Xiaochun Cao, Di Jin, Bo Yang, Yuanfang Guo.

45. Hierarchical Graph Convolutional Networks for Semi-supervised Node Classification. IJCAI 2019.

    Fenyu Hu, Yanqiao Zhu, Shu Wu, Liang Wang, Tieniu Tan.

46. Masked Graph Convolutional Network. IJCAI 2019.

    Liang Yang, Fan Wu, Yingkui Wang, Junhua Gu, Yuanfang Guo.

47. Dual Self-Paced Graph Convolutional Network: Towards Reducing Attribute Distortions Induced by Topology. IJCAI 2019.

    Liang Yang, Zhiyang Chen, Junhua Gu, Yuanfang Guo.

48. Bayesian graph convolutional neural networks for semi-supervised classification. AAAI 2019.

    Yingxue Zhang, Soumyasundar Pal, Mark Coates, Deniz Üstebay.

49. GeniePath: Graph Neural Networks with Adaptive Receptive Paths. AAAI 2019.

    Ziqi Liu, Chaochao Chen, Longfei Li, Jun Zhou, Xiaolong Li, Le Song, Yuan Qi.

50. Gaussian-Induced Convolution for Graphs. AAAI 2019.

    Jiatao Jiang, Zhen Cui, Chunyan Xu, Jian Yang.

51. Fisher-Bures Adversary Graph Convolutional Networks. UAI 2019.

    Ke Sun, Piotr Koniusz, Zhen Wang.

52. N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification. UAI 2019.

    Sami Abu-El-Haija, Amol Kapoor, Bryan Perozzi, Joonseok Lee.

53. Confidence-based Graph Convolutional Networks for Semi-Supervised Learning. AISTATS 2019.

    Shikhar Vashishth, Prateek Yadav, Manik Bhandari, Partha Talukdar.

54. Lovasz Convolutional Networks. AISTATS 2019.

    Prateek Yadav, Madhav Nimishakavi, Naganand Yadati, Shikhar Vashishth, Arun Rajkumar, Partha Talukdar.

55. Provably Powerful Graph Networks. NeurIPS 2019.

    Haggai Maron, Heli Ben-Hamu, Hadar Serviansky, Yaron Lipman.

56. Graph Agreement Models for Semi-Supervised Learning. NeurIPS 2019.

    Otilia Stretcu, Krishnamurthy Viswanathan, Dana Movshovitz-Attias, Emmanouil Platanios. Sujith Ravi, Andrew Tomkins.

57. Graph-Based Semi-Supervised Learning with Non-ignorable Non-response. NeurIPS 2019.

    Fan Zhou, Tengfei Li, Haibo Zhou, Hongtu Zhu, Ye Jieping.

58. A Flexible Generative Framework for Graph-based Semi-supervised Learning. NeurIPS 2019.

    Jiaqi Ma, Weijing Tang, Ji Zhu, Qiaozhu Mei.

59. Semi-Implicit Graph Variational Auto-Encoders. NeurIPS 2019.

    Arman Hasanzadeh, Ehsan Hajiramezanali, Krishna Narayanan, Nick Duffield, Mingyuan Zhou, Xiaoning Qian.

60. Hyperbolic Graph Neural Networks. NeurIPS 2019.

    Qi Liu, Maximilian Nickel, Douwe Kiela.

61. Hyperbolic Graph Convolutional Neural Networks. NeurIPS 2019.

    Ines Chami, Zhitao Ying, Christopher Ré, Jure Leskovec.

62. Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels. NeurIPS 2019.

    Simon Du, Kangcheng Hou, Russ Salakhutdinov, Barnabas Poczos, Ruosong Wang, Keyulu Xu.

63. SNEQ: Semi-supervised Attributed Network Embedding with Attention-based Quantisation. AAAI 2020.

    Tao He, Lianli Gao, Jingkuan Song, Xin Wang, Kejie Huang, Yuan-­‐Fang Li.

64. Going Deep: Graph Convolutional Ladder-Shape Networks. AAAI 2020.

    Ruiqi Hu, Shirui Pan, Guodong Long, Qinghua Lu, Liming Zhu, Jing Jiang.

65. Co-GCN for Multi-View Semi-Supervised Learning. AAAI 2020.

    Shu Li, Wen-­‐Tao Li, Wei Wang.

66. Graph Representation Learning via Ladder Gamma Variational Autoencoders. AAAI 2020.

    Arindam Sarkar, Nikhil Mehta, Piyush Rai.

67. GSSNN: Graph Smoothing Splines Neural Networks. AAAI 2020.

    Shichao Zhu, Lewei Zhou, Shirui Pan, Chuan Zhou, Guiying Yan, Bin Wang.

68. Effective Decoding in Graph Auto-Encoder using Triadic Closure. AAAI 2020.

    Han Shi, Haozheng Fan, James T. Kwok.

69. An Attention-based Graph Neural Network for Heterogeneous Structural Learning. AAAI 2020.

    Huiting Hong, Hantao Guo, Yucheng Lin, Xiaoqing Yang, Zang Li, Jieping Ye.

70. Fast and Deep Graph Neural Networks. AAAI 2020.

    Claudio Gallicchio, Alessio Micheli.

71. Learning Signed Network Embedding via Graph Attention. AAAI 2020.

    Yu Li, Yuan Tian, Jiawei Zhang, Yi Chang.

72. GraLSP: Graph Neural Networks with Local Structural Patterns. AAAI 2020.

    Yilun Jin, Guojie Song, Chuan Shi.

73. Multi‐Stage Self­‐Supervised Learning for Graph Convolutional Networks on Graphs with Few Labeled Nodes. AAAI 2020.

    Ke Sun, Zhouchen Lin, Zhanxing Zhu.

74. Collaborative Graph Convolutional Networks: Unsupervised Learning Meets Semi-­‐Supervised Learning. AAAI 2020.

    Binyuan Hui, Pengfei Zhu, Qinghua, Hu.

75. A Multi­‐Scale Approach for Graph Link Prediction. AAAI 2020.

    Lei Cai, Shuiwang Ji.

76. Adaptive Structural Fingerprints for Graph Attention Networks. ICLR 2020.

    Kai Zhang, Yaokang Zhu, Jun Wang, Jie Zhang.

77. Strategies for Pre-training Graph Neural Networks. ICLR 2020.

    Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, Jure Leskovec.

78. DropEdge: Towards Deep Graph Convolutional Networks on Node Classification. ICLR 2020.

    Yu Rong, Wenbing Huang, Tingyang Xu, Junzhou Huang.

79. Directional Message Passing for Molecular Graphs. ICLR 2020.

    Johannes Klicpera, Janek Groß, Stephan Günnemann.

80. DeepSphere: a graph-based spherical CNN. ICLR 2020.

    Michaël Defferrard, Martino Milani, Frédérick Gusset, Nathanaël Perraudin.

81. Geom-GCN: Geometric Graph Convolutional Networks. ICLR 2020.

    Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, Bo Yang.

82. Curvature Graph Network. ICLR 2020.

    Ze Ye, Kin Sum Liu, Tengfei Ma, Jie Gao, Chao Chen.

83. Measuring and Improving the Use of Graph Information in Graph Neural Networks. ICLR 2020.

    Yifan Hou, Jian Zhang, James Cheng, Kaili Ma, Richard T. B. Ma, Hongzhi Chen, Ming-Chang Yang.

84. Pruned Graph Scattering Transforms. ICLR 2020.

    Vassilis N. Ioannidis, Siheng Chen, Georgios B. Giannakis.

85. Neural Execution of Graph Algorithms. ICLR 2020.

    Petar Veličković, Rex Ying, Matilde Padovano, Raia Hadsell, Charles Blundell.

86. Graph inference learning for semi-supervised classification. ICLR 2020.

    Chunyan Xu, Zhen Cui, Xiaobin Hong, Tong Zhang, Jian Yang, Wei Liu.

87. SGAS: Sequential Greedy Architecture Search. CVPR 2020.

    Guohao Li, Guocheng Qian, Itzel C. Delgadillo, Matthias Müller, Ali Thabet, Bernard Ghanem.

88. Streaming Graph Neural Networks SIGIR2020.

    Yao Ma, Ziyi Guo, Zhaochun Ren, Eric Zhao, Jiliang Tang, Dawei Yin.

89. Graph Structural-topic Neural Network. KDD 2020.

    Long, Qingqing and Jin, Yilun and Song, Guojie and Li, Yi and Lin, Wei.

90. Random Walk Graph Neural Networks. NeurIPS 2020.

    Giannis Nikolentzos and Michalis Vazirgiannis.

91. Learning Graph Structure With A Finite-State Automaton Layer. NeurIPS 2020.

    Daniel D. Johnson and Hugo Larochelle and Daniel Tarlow.

92. Improving SAT Solver Heuristics with Graph Networks and Reinforcement Learning. NeurIPS 2020.

    Vitaly Kurin and Saad Godil and Shimon Whiteson and Bryan Catanzaro.

2. Graph Pooling Methods

1. An End-to-End Deep Learning Architecture for Graph Classification. AAAI 2018.

   Muhan Zhang, Zhicheng Cui, Marion Neumann, Yixin Chen.

2. Hierarchical Graph Representation Learning with Differentiable Pooling. NeurIPS 2018.

   Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, Will Hamilton, Jure Leskovec.

3. Self-Attention Graph Pooling. ICML 2019.

   Junhyun Lee, Inyeop Lee, Jaewoo Kang.

4. Graph U-Nets. ICML 2019.

   Hongyang Gao, Shuiwang Ji.

5. Graph Convolutional Networks with EigenPooling. KDD 2019.

   Yao Ma, Suhang Wang, Charu C. Aggarwal, Jiliang Tang.

6. Relational Pooling for Graph Representations. ICML 2019.

   Ryan L. Murphy, Balasubramaniam Srinivasan, Vinayak Rao, Bruno Ribeiro.

7. Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks. NeurIPS 2019.

   Sitao Luan, Mingde Zhao, Xiao-Wen Chang, Doina Precup.

8. Diffusion Improves Graph Learning. NeurIPS 2019.

   Johannes Klicpera, Stefan Weißenberger, Stephan Günnemann.

9. ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations. AAAI 2020.

   Ekagra Ranjan, Soumya Sanyal, Partha Pratim Talukdar.

10. Hierarchical Graph Pooling with Structure Learning. AAAI 2020.

    Zhen Zhang, Jiajun Bu, Martin Ester, Jianfeng Zhang, Chengwei Yao, Zhi Yu, Can Wang.

11. StructPool: Structured Graph Pooling via Conditional Random Fields. ICLR 2020.

    Hao Yuan, Shuiwang Ji.

12. Memory-Based Graph Networks. ICLR 2020.

    Khasahmadi, Amir Hosein，Hassani, Kaveh，Moradi, Parsa，Lee, Leo，Morris, Quaid.

13. Spectral Clustering with Graph Neural Networks for Graph Pooling. ICML 2020.

    Filippo Maria Bianchi, Daniele Grattarola, Cesare Alippi.

14. Path Integral Based Convolution and Pooling for Graph Neural Networks. NeurIPS 2020.

    Zheng Ma and Junyu Xuan and Yu Guang Wang and Ming Li and Pietro Lio.

3. Efficiency and Scalability

1. Stochastic Training of Graph Convolutional Networks with Variance Reduction. ICML 2018.

   Jianfei Chen, Jun Zhu, Le Song.

2. FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling. ICLR 2018.

   Jie Chen, Tengfei Ma, Cao Xiao.

3. Adaptive Sampling Towards Fast Graph Representation Learning. NeurIPS 2018.

   Wenbing Huang, Tong Zhang, Yu Rong, Junzhou Huang.

4. Large-Scale Learnable Graph Convolutional Networks. KDD 2018.

   Hongyang Gao, Zhengyang Wang, Shuiwang Ji.

5. Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks. KDD 2019.

   Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, Cho-Jui Hsieh.

6. A Degeneracy Framework for Scalable Graph Autoencoders. IJCAI 2019.

   Guillaume Salha, Romain Hennequin, Viet Anh Tran, Michalis Vazirgiannis.

7. Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks. NeurIPS 2019.

   Difan Zou, Ziniu Hu, Yewen Wang, Song Jiang, Yizhou Sun, Quanquan Gu.

8. GraphSAINT: Graph Sampling Based Inductive Learning Method. ICLR 2020.

   Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, Viktor Prasanna.

9. TinyGNN: Learning Efficient Graph Neural Networks . KDD 2020.

   Bencheng Yan, Chaokun Wang, Gaoyang Guo, and Yunkai Lou.

10. Convergence and Stability of Graph Convolutional Networks on Large Random Graphs. NeurIPS 2020.

    Nicolas Keriven and Alberto Bietti and Samuel Vaiter.

4. Complex Graph

Dynamic Graph

1. DyRep: Learning Representations over Dynamic Graphs. ICLR 2019.

   Rakshit Trivedi, Mehrdad Farajtabar, Prasenjeet Biswal, Hongyuan Zha.

2. Dynamic Hypergraph Neural Networks. IJCAI 2019.

   Jianwen Jiang, Yuxuan Wei, Yifan Feng, Jingxuan Cao, Yue Gao.

3. Recurrent Space-time Graph Neural Networks. NeurIPS 2019.

   Andrei Nicolicioiu, Iulia Duta, Marius Leordeanu.

4. EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs. AAAI 2020.

   Aldo Pareja, Giacomo Domeniconi, Jie Chen, Tengfei Ma, Toyotaro Suzumura, Hiroki Kanezashi, Tim Kaler, Tao B. Schardl, Charles E. Leiserson.

5. Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting. AAAI 2020.

   Chao Song, Youfang Lin, Shengnan Guo, Huaiyu Wan.

6. Inductive representation learning on temporal graphs. ICLR 2020.

   da Xu, chuanwei ruan, evren korpeoglu, sushant kumar, kannan achan.

Heterogeneous Graph

1. Deep Collective Classification in Heterogeneous Information Networks. WWW 2018.

   Yizhou Zhang, Yun Xiong, Xiangnan Kong, Shanshan Li, Jinhong Mi, Yangyong Zhu.

2. Heterogeneous Graph Neural Networks for Malicious Account Detection. CIKM 2018.

   Ziqi Liu, Chaochao Chen, Xinxing Yang, Jun Zhou, Xiaolong Li, Le Song.

3. Modeling polypharmacy side effects with graph convolutional networks ISMB 2018

   Marinka Zitnik, Monica Agrawal, Jure Leskovec.

4. Non-local Attention Learning on Large Heterogeneous Information Networks. IEEE Big Data 2019.

   Yuxin Xiao, Zecheng Zhang, Carl Yang, and Chengxiang Zhai.

5. Fine-grained Event Categorization with Heterogeneous Graph Convolutional. IJCAI 2019.

   Hao Peng, Jianxin Li, Qiran Gong, Yangqiu Song, Yuanxing Ning, Kunfeng Lai and Philip S. Yu.

6. Heterogeneous Graph Attention Network. WWW 2019.

   Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Peng Cui, Philip S. Yu, Yanfang Ye.

7. Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendation. KDD 2019.

   Shaohua Fan, Junxiong Zhu, Xiaotian Han, Chuan Shi, Linmei Hu, Biyu Ma, Yongliang Li.

8. Heterogeneous Graph Neural Network. KDD 2019.

   Chuxu Zhang, Dongjin Song, Chao Huang, Ananthram Swami, Nitesh V. Chawla.

9. Graph Transformer Networks. NIPS 2019.

   Seongjun Yun, Minbyul Jeong, Raehyun Kim, Jaewoo Kang, Hyunwoo J. Kim.

10. Heterogeneous Deep Graph Infomax. AAAI 2020.

    Yuxiang Ren and Bo Liu and Chao Huang and Peng Dai and Liefeng Bo and Jiawei Zhang.

11. Heterogeneous Graph Transformer. WWW 2020.

    Ziniu Hu, Yuxiao Dong, Kuansan Wang, Yizhou Sun.

12. Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding. WWW2020.

    Xingyu Fu, Jiani Zhang, Ziqiao Meng, Irwin King.

Hypergraph

1. Hypergraph Neural Networks. AAAI 2019.

   Yifan Feng, Haoxuan You, Zizhao Zhang, Rongrong Ji, Yue Gao.

2. HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs. NeurIPS 2019.

   Naganand Yadati, Madhav Nimishakavi, Prateek Yadav, Vikram Nitin, Anand Louis, Partha Talukdar.

3. Hypergraph Label Propagation Network. AAAI 2020.

   Yubo Zhang, Nan Wang, Yufeng Chen, Changqing Zou, Hai Wan, Xibin Zhao, Yue Gao.

4. Hyper-SAGNN: a self-attention based graph neural network for hypergraphs. ICLR 2020.

   Ruochi Zhang, Yuesong Zou, Jian Ma.

5. Hypergraph Convolutional Recurrent Neural Network. KDD 2020.

   Jaehyuk Yi, Jinkyoo Park.

5. Analysis

1. A Comparison between Recursive Neural Networks and Graph Neural Networks. IJCNN 2006.

   Vincenzo Di Massa, Gabriele Monfardini, Lorenzo Sarti, Franco Scarselli, Marco Maggini, Marco Gori.

2. Neural networks for relational learning: an experimental comparison. Machine Learning 2011.

   Werner Uwents, Gabriele Monfardini, Hendrik Blockeel, Marco Gori, Franco Scarselli.

3. Mean-field theory of graph neural networks in graph partitioning. NeurIPS 2018.

   Tatsuro Kawamoto, Masashi Tsubaki, Tomoyuki Obuchi.

4. Representation Learning on Graphs with Jumping Knowledge Networks. ICML 2018.

   Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, Stefanie Jegelka.

5. Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning. AAAI 2018.

   Qimai Li, Zhichao Han, Xiao-Ming Wu.

6. How Powerful are Graph Neural Networks? ICLR 2019.

   Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka.

7. Stability and Generalization of Graph Convolutional Neural Networks. KDD 2019.

   Saurabh Verma, Zhi-Li Zhang.

8. Simplifying Graph Convolutional Networks. ICML 2019.

   Felix Wu, Tianyi Zhang, Amauri Holanda de Souza Jr., Christopher Fifty, Tao Yu, Kilian Q. Weinberger.

9. Explainability Methods for Graph Convolutional Neural Networks. CVPR 2019.

   Phillip E. Pope, Soheil Kolouri, Mohammad Rostami, Charles E. Martin, Heiko Hoffmann.

10. Can GCNs Go as Deep as CNNs? ICCV 2019.

    Guohao Li, Matthias Müller, Ali Thabet, Bernard Ghanem.

11. Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks. AAAI 2019.

    Christopher Morris, Martin Ritzert, Matthias Fey, William L. Hamilton, Jan Eric Lenssen, Gaurav Rattan, Martin Grohe.

12. Understanding Attention and Generalization in Graph Neural Networks. NeurIPS 2019.

    Boris Knyazev, Graham W. Taylor, Mohamed R. Amer.

13. GNNExplainer: Generating Explanations for Graph Neural Networks. NeurIPS 2019.

    Zhitao Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, Jure Leskovec.

14. Universal Invariant and Equivariant Graph Neural Networks. NeurIPS 2019.

    Nicolas Keriven, Gabriel Peyré.

15. Understanding Attention and Generalization in Graph Neural Networks. NeurIPS 2019.

    Boris Knyazev, Graham W Taylor, Mohamed Amer.

16. On the equivalence between graph isomorphism testing and function approximation with GNNs. NeurIPS 2019.

    Zhengdao Chen, Soledad Villar, Lei Chen, Joan Bruna.

17. Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology. NeurIPS 2019.

    Nima Dehmamy, Albert-Laszlo Barabasi, Rose Yu.

18. Graph Neural Networks Exponentially Lose Expressive Power for Node Classification. ICLR 2020.

    Kenta Oono, Taiji Suzuki.

19. What graph neural networks cannot learn: depth vs width. ICLR 2020.

    Andreas Loukas.

20. The Logical Expressiveness of Graph Neural Networks. ICLR 2020.

    Pablo Barceló, Egor V. Kostylev, Mikael Monet, Jorge Pérez, Juan Reutter, Juan Pablo Silva.

21. On the Equivalence between Positional Node Embeddings and Structural Graph Representations. ICLR 2020.

    Balasubramaniam Srinivasan, Bruno Ribeiro

22. Simple and Deep Graph Convolutional Networks. ICML 2020.

    Ming Chen，Zhewei Wei，Zengfeng Huang，Bolin Ding，Yaliang Li

23. XGNN: Towards Model-Level Explanations of Graph Neural Networks. KDD 2020.

    Hao Yuan, Jiliang Tang, Xia Hu, Shuiwang Ji.

GNN based Recommendation

1. Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks. NIPS 2017.

   Federico Monti, Michael M. Bronstein, Xavier Bresson.

2. Graph Convolutional Neural Networks for Web-Scale Recommender Systems. KDD 2018.

   Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, Jure Leskovec.

3. Graph Convolutional Matrix Completion. KDD 2018.

   Rianne van den Berg, Thomas N. Kipf, Max Welling.

4. STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems. IJCAI 2019.

   Jiani Zhang, Xingjian Shi, Shenglin Zhao, Irwin King.

5. Binarized Collaborative Filtering with Distilling Graph Convolutional Networks. IJCAI 2019.

   Haoyu Wang, Defu Lian, Yong Ge.

6. Graph Contextualized Self-Attention Network for Session-based Recommendation. IJCAI 2019.

   Chengfeng Xu, Pengpeng Zhao, Yanchi Liu, Victor S. Sheng, Jiajie Xu, Fuzhen Zhuang, Junhua Fang, Xiaofang Zhou.

7. Session-based Recommendation with Graph Neural Networks. AAAI 2019.

   Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, Tieniu Tan.

8. Geometric Hawkes Processes with Graph Convolutional Recurrent Neural Networks. AAAI 2019.

   Jin Shang, Mingxuan Sun.

9. Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems. KDD 2019.

   Hongwei Wang, Fuzheng Zhang, Mengdi Zhang, Jure Leskovec, Miao Zhao, Wenjie Li, Zhongyuan Wang.

10. Exact-K Recommendation via Maximal Clique Optimization. KDD 2019.

    Yu Gong, Yu Zhu, Lu Duan, Qingwen Liu, Ziyu Guan, Fei Sun, Wenwu Ou, Kenny Q. Zhu.

11. KGAT: Knowledge Graph Attention Network for Recommendation. KDD 2019.

    Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, Tat-Seng Chua.

12. Knowledge Graph Convolutional Networks for Recommender Systems. WWW 2019.

    Hongwei Wang, Miao Zhao, Xing Xie, Wenjie Li, Minyi Guo.

13. Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems. WWW 2019.

    Qitian Wu, Hengrui Zhang, Xiaofeng Gao, Peng He, Paul Weng, Han Gao, Guihai Chen.

14. Graph Neural Networks for Social Recommendation. WWW 2019.

    Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, Dawei Yin.

15. Memory Augmented Graph Neural Networks for Sequential Recommendation. AAAI 2020.

    Chen Ma, Liheng Ma, Yingxue Zhang, Jianing Sun, Xue Liu, Mark Coates.

16. Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach. AAAI 2020.

    Lei Chen, Le Wu, Richang Hong, Kun Zhang, Meng Wang.

17. Inductive Matrix Completion Based on Graph Neural Networks. ICLR 2020.

    Muhan Zhang, Yixin Chen.

18. Graph Wasserstein Correlation Analysis for Movie Retrieval. ECCV 2020.

    Xueya Zhang, Tong Zhang, Xiaobin Hong, Zhen Cui, and Jian Yang.

19. xBundle Recommendation with Graph Convolutional Networks. SIGIR2020.

    Chang-You Tai, Meng-Ru Wu, Yun-Wei Chu, Shao-Yu Chu, Lun-Wei Ku.

20. MVIN: Learning Multiview Items for Recommendation. SIGIR2020.

    Chang-You Tai, Meng-Ru Wu, Yun-Wei Chu, Shao-Yu Chu, Lun-Wei Ku.

21. Hierarchical Fashion Graph Network for Personalized Outfit Recommendation. SIGIR2020.

    Xingchen Li, Xiang Wang, Xiangnan He, Long Chen, Jun Xiao, Tat-Seng Chua.

22. Item Tagging for Information Retrieval: A Tripartite Graph Neural Network based Approach. SIGIR2020.

    Kelong Mao, Xi Xiao, Jieming Zhu, Biao Lu, Ruiming Tang, Xiuqiang He.

23. Learning to Transfer Graph Embeddings for Inductive Graph based Recommendation. SIGIR2020.

    Le Wu, Yonghui Yang, Lei Chen, Defu Lian, Richang Hong, Meng Wang.

24. GCN-Based User Representation Learning for Unifying Robust Recommendation and Fraudster Detection. SIGIR2020.

    Shijie Zhang, Hongzhi Yin, Tong Chen, Quoc Viet Nguyen Hung, Zi Huang, Lizhen Cui.

25. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. SIGIR2020.

    Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, Meng Wang.

26. Attentional Graph Convolutional Networks for Knowledge Concept Recommendation in MOOCs in a Heterogeneous View. SIGIR2020.

    Shen Wang, Jibing Gong, Jinlong Wang, Wenzheng Feng, Hao Peng, Jie Tang, Philip S. Yu.

27. Personalized Image Retrieval with Sparse Graph Representation Learning. KDD2020.

    Xiaowei Jia , Handong Zhao , Zhe Lin , Ajinkya Kale , Vipin Kumar.

28. Handling Information Loss of Graph Neural Networks for Session-based Recommendation. KDD 2020.

    Tianwen Chen, Raymond Chi-Wing Wong

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