Must-read papers and continuous tracking on Graph Neural Network(GNN) progress

Many important real-world applications and questions come in the form of graphs, such as social network, protein-protein interaction network, brain network, chemical molecular graph and 3D point cloud. Therefore, driven by interdisciplinary research, the neural network model for graph data-oriented has become an emerging research hotspot. Among them, two of the three pioneers of deep learning, Professor Yann LeCun (2018 Turing Award Winner), Professor Yoshua Bengio (2018 Turing Award Winner) and famous Professor Jure Leskovec from Stanford University AI lab also participated in it.

This project focuses on GNN, lists relevant must-read papers and keeps track of progress. We look forward to promoting this direction and providing some help to researchers in this direction.

Contributed by Bentian Li(at NUAA), If there is something wrong or GNN-related issue, welcome to contact me via the email (Address: [email protected], [email protected])

Technology Keyword: Graph Neural Network, Graph convolutional network, Graph network, Graph attention network, Graph auto-encoder,...

Very hot research topic: the representative work--Graph convolutional networks (GCNs) proposed by T.N. Kipf and M. Welling (ICLR2017 [5] in conference paper list) has been cited 1,020 times in Google Scholar (by the date 09 May 2019). Update: 1, 065 times (by the date 20 May 2019); Update: 1, 106 times (by the date 27 May 2019); Update: 1, 227 times (by the date 19 June 2019); Update: 1, 377 times (by the date 8 July 2019); Update: 1, 678 times (by the date 17 Sept. 2019)

Thanks to so many developers and scientists in Github for giving me so many stars and support！！！ I will continue to make this project better.

Project Start time: 11 Dec 2018, Latest updated time: 17 Sept. 2019

More papers about GNN models and their applications will come from NeurIPS2019 .... We are waiting for the paper to be released.

Survey papers:

1. Ziwei Zhang, Peng Cui, Wenwu Zhu, Deep Learning on Graphs: A Survey, ArXiv, 2018. paper.

The categorization of deep learning methods on graphs[1] from Tsinghua University.

2. Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Maosong Sun, Graph Neural Networks: A Review of Methods and Applications, ArXiv, 2018. paper.

Some typical application of GNN[2] from Tsinghua University.

3. Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, Philip S. Yu(Fellow,IEEE), A Comprehensive Survey on Graph Neural Networks, ArXiv, 2019. paper.

Some open-source codes of the state-of-the-art methods[3].

4. Battaglia P W, Hamrick J B, Bapst V, et al. Relational inductive biases, deep learning, and graph networks, arXiv 2018. paper

Journal papers:

1. F. Scarselli, M. Gori, A.C. Tsoi, M. Hagenbuchner, G. Monfardini, The graph neural network model, IEEE Transactions on Neural Networks(IEEE Transactions on Neural Networks and Learning Systems), 2009. paper.

2. Scarselli F, Gori M, Tsoi A C, et al. Computational capabilities of graph neural networks, IEEE Transactions on Neural Networks, 2009. paper.

3. Micheli A . Neural Network for Graphs: A Contextual Constructive Approach. IEEE Transactions on Neural Networks, 2009. paper.

4. Goles, Eric, and Gonzalo A. Ruz. Dynamics of Neural Networks over Undirected Graphs. Neural Networks, 2015. paper.

5. Z. Luo, L. Liu, J. Yin, Y. Li, Z. Wu, Deep Learning of Graphs with Ngram Convolutional Neural Networks, IEEE Transactions on Knowledge & Data Engineering, 2017. paper. code.

6. Petroski Such F , Sah S , Dominguez M A , et al. Robust Spatial Filtering with Graph Convolutional Neural Networks. IEEE Journal of Selected Topics in Signal Processing, 2017. paper.

7. Kawahara J, Brown C J, Miller S P, et al. BrainNetCNN: convolutional neural networks for brain networks; towards predicting neurodevelopment. NeuroImage, 2017. paper.

8. Muscoloni A , Thomas J M , Ciucci S , et al. Machine learning meets complex networks via coalescent embedding in the hyperbolic space. Nature Communications, 2017. paper.

9. D.M. Camacho, K.M. Collins, R.K. Powers, J.C. Costello, J.J. Collins, Next-Generation Machine Learning for Biological Networks, Cell, 2018. paper.

10. Marinka Z , Monica A , Jure L . Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics, 2018. paper.

11. Sarah P , Ira K S , Enzo F , et al. Disease Prediction using Graph Convolutional Networks: Application to Autism Spectrum Disorder and Alzheimer’s Disease. Medical Image Analysis, 2018. paper.

12. Sofia Ira Ktena, Sarah Parisot, Enzo Ferrante, Martin Rajchl, Matthew Lee, Ben Glocker, Daniel Rueckert, Metric learning with spectral graph convolutions on brain connectivity networks, NeuroImage, 2018. paper.

13. Xie T , Grossman J C . Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties. Physical Review Letters, 2018. paper.

14. Phan, Anh Viet, Minh Le Nguyen, Yen Lam Hoang Nguyen, and Lam Thu Bui. DGCNN: A Convolutional Neural Network over Large-Scale Labeled Graphs. Neural Networks, 2018. paper

15. Song T, Zheng W, Song P, et al. Eeg emotion recognition using dynamical graph convolutional neural networks. IEEE Transactions on Affective Computing, 2018. paper

16. Levie R, Monti F, Bresson X, et al. Cayleynets: Graph convolutional neural networks with complex rational spectral filters. IEEE Transactions on Signal Processing 2019. paper

17. Zhang, Zhihong, Dongdong Chen, Jianjia Wang, Lu Bai, and Edwin R. Hancock. Quantum-Based Subgraph Convolutional Neural Networks. Pattern Recognition, 2019. paper

18. Qin A, Shang Z, Tian J, et al. Spectral–Spatial Graph Convolutional Networks for Semisupervised Hyperspectral Image Classification. IEEE Geoscience and Remote Sensing Letters, 2019. paper

19. Coley C W, Jin W, Rogers L, et al. A graph-convolutional neural network model for the prediction of chemical reactivity. Chemical Science, 2019. paper

20. Zhang Z, Chen D, Wang Z, et al. Depth-based Subgraph Convolutional Auto-Encoder for Network Representation Learning. Pattern Recognition, 2019. paper

21. Hong Y, Kim J, Chen G, et al. Longitudinal Prediction of Infant Diffusion MRI Data via Graph Convolutional Adversarial Networks. IEEE transactions on medical imaging, 2019. paper

22. Khodayar M, Mohammadi S, Khodayar M E, et al. Convolutional Graph Autoencoder: A Generative Deep Neural Network for Probabilistic Spatio-temporal Solar Irradiance Forecasting. IEEE Transactions on Sustainable Energy, 2019. paper

23. Zhang Q, Chang J, Meng G, et al. Learning graph structure via graph convolutional networks. Pattern Recognition, 2019. paper

24. Xuan P, Pan S, Zhang T, et al. Graph Convolutional Network and Convolutional Neural Network Based Method for Predicting lncRNA-Disease Associations. Cells, 2019. paper

25. Sun M, Zhao S, Gilvary C, et al. Graph convolutional networks for computational drug development and discovery. Briefings in bioinformatics, 2019. paper

26. Spier N, Nekolla S, Rupprecht C, et al. Classification of Polar Maps from Cardiac Perfusion Imaging with Graph-Convolutional Neural Networks. Scientific reports, 2019. paper

Conference papers:

1. Duvenaud D, Maclaurin D, Aguilera-Iparraguirre J, et al. Convolutional networks on graphs for learning molecular fingerprints, NeurIPS(NIPS) 2015. paper. code.

2. M. Niepert, M. Ahmed, K. Kutzkov, Learning Convolutional Neural Networks for Graphs, ICML 2016. paper.

3. S. Cao, W. Lu, Q. Xu, Deep neural networks for learning graph representations, AAAI 2016. paper.

4. M. Defferrard, X. Bresson, P. Vandergheynst, Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering, NeurIPS(NIPS) 2016. paper. code.

5. T.N. Kipf, M. Welling, Semi-Supervised Classification with Graph Convolutional Networks, ICLR 2017. paper. code.

6. A. Fout, B. Shariat, J. Byrd, A. Benhur, Protein Interface Prediction using Graph Convolutional Networks, NeurIPS(NIPS) 2017. paper.

7. Monti F, Bronstein M, Bresson X. Geometric matrix completion with recurrent multi-graph neural networks, NeurIPS(NIPS) 2017. paper.

8. Simonovsky M, Komodakis N. Dynamic edgeconditioned filters in convolutional neural networks on graphs, CVPR. 2017. paper

9. R. Li, S. Wang, F. Zhu, J. Huang, Adaptive Graph Convolutional Neural Networks, AAAI 2018. paper

10. J. You, B. Liu, R. Ying, V. Pande, J. Leskovec, Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation, NeurIPS(NIPS) 2018. paper.

11. C. Zhuang, Q. Ma, Dual Graph Convolutional Networks for Graph-Based Semi-Supervised Classification, WWW 2018. paper

12. H. Gao, Z. Wang, S. Ji, Large-Scale Learnable Graph Convolutional Networks, KDD 2018. paper

13. D. Zügner, A. Akbarnejad, S. Günnemann, Adversarial Attacks on Neural Networks for Graph Data, KDD 2018. paper

14. Ying R , He R , Chen K , et al. Graph Convolutional Neural Networks for Web-Scale Recommender Systems. KDD 2018. paper

15. P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò, Y. Bengio, Graph Attention Networks, ICLR, 2018. paper

16. Beck, Daniel Edward Robert, Gholamreza Haffari and Trevor Cohn. Graph-to-Sequence Learning using Gated Graph Neural Networks. ACL 2018. paper

17. Marcheggiani D , Bastings J , Titov I . Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks. NAACL 2018. paper

18. Chen J , Zhu J , Song L . Stochastic Training of Graph Convolutional Networks with Variance Reduction. ICML 2018. paper

19. Gusi Te, Wei Hu, Amin Zheng, Zongming Guo, RGCNN: Regularized Graph CNN for Point Cloud Segmentation. ACM Multimedia 2018. paper, code,

20. Talukdar, Partha, Shikhar Vashishth, Shib Sankar Dasgupta and Swayambhu Nath Ray. Dating Documents using Graph Convolution Networks. ACL 2018. paper, code

21. Sanchez-Gonzalez A , Heess N , Springenberg J T , et al. Graph networks as learnable physics engines for inference and control. ICML 2018. paper

22. Muhan Zhang, Yixin Chen. Link Prediction Based on Graph Neural Networks. NeurIPS(NIPS) 2018. paper

23. Chen, Jie, Tengfei Ma, and Cao Xiao. FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling. ICLR 2018. paper

24. Zhang, Zhen, Hongxia Yang, Jiajun Bu, Sheng Zhou, Pinggang Yu, Jianwei Zhang, Martin Ester, and Can Wang. ANRL: Attributed Network Representation Learning via Deep Neural Networks.. IJCAI 2018. paper

25. Rahimi A , Cohn T , Baldwin T . Semi-supervised User Geolocation via Graph Convolutional Networks. ACL 2018. paper

26. Morris C , Ritzert M , Fey M , et al.Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks.. AAAI 2019. paper

27. Xu K, Hu W, Leskovec J, et al. How Powerful are Graph Neural Networks?, ICLR 2019. paper

28. Johannes Klicpera, Aleksandar Bojchevski, Stephan Günnemann. Combining Neural Networks with Personalized PageRank for Classification on Graphs, ICLR 2019. paper

29. Daniel Zügner, Stephan Günnemann. Adversarial Attacks on Graph Neural Networks via Meta Learning, ICLR 2019. paper

30. Zhang Xinyi, Lihui Chen. Capsule Graph Neural Network, ICLR 2019. paper

31. Liao, R., Zhao, Z., Urtasun, R., and Zemel, R. LanczosNet: Multi-Scale Deep Graph Convolutional Networks, ICLR 2019, paper

32. Bingbing Xu, Huawei Shen, Qi Cao, Yunqi Qiu, Xueqi Cheng. Graph Wavelet Neural Network, ICLR 2019, paper

33. Hu J, Guo C, Yang B, et al. Stochastic Weight Completion for Road Networks using Graph Convolutional Networks ICDE. 2019. paper

34. Yao L, Mao C, Luo Y . Graph Convolutional Networks for Text Classification. AAAI 2019. paper

35. Landrieu L , Boussaha M . Point Cloud Oversegmentation with Graph-Structured Deep Metric Learning. CVPR 2019. paper

36. Si C , Chen W , Wang W , et al. An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition. CVPR 2019. paper

37. Cucurull G , Taslakian P , Vazquez D . Context-Aware Visual Compatibility Prediction. CVPR 2019. paper

38. Jia-Xing Zhong, Nannan Li, Weijie Kong, Shan Liu, Thomas H. Li, Ge Li. Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detection. CVPR 2019. paper

39. Michael Kampffmeyer, Yinbo Chen, Xiaodan Liang, Hao Wang, Yujia Zhang, Eric P. Xing. Rethinking Knowledge Graph Propagation for Zero-Shot Learning. CVPR 2019. paper

40. Arushi Goel, Keng Teck Ma, Cheston Tan. An End-to-End Network for Generating Social Relationship Graphs. CVPR 2019. paper

41. Yichao Yan, Qiang Zhang, Bingbing Ni, Wendong Zhang, Minghao Xu, Xiaokang Yang. Learning Context Graph for Person Search. CVPR 2019 paper

42. Zhongdao Wang, Liang Zheng, Yali Li, Shengjin Wang. Linkage Based Face Clustering via Graph Convolution Network. CVPR 2019 paper

43. Lei Yang, Xiaohang Zhan, Dapeng Chen, Junjie Yan, Chen Change Loy, Dahua Lin. Learning to Cluster Faces on an Affinity Graph. CVPR 2019 paper

44. Yao Ma, Suhang Wang, Charu C. Aggarwal, Jiliang Tang. Graph Convolutional Networks with EigenPooling. KDD2019, paper

45. Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, Dawei Yin. Graph Neural Networks for Social Recommendation. WWW2019, paper

46. Kim J, Kim T, Kim S, et al. Edge-labeling Graph Neural Network for Few-shot Learning. CVPR 2019. paper

47. Jessica V. Schrouff, Kai Wohlfahrt, Bruno Marnette, Liam Atkinson. INFERRING JAVASCRIPT TYPES USING GRAPH NEURAL NETWORKS. ICLR 2019. paper

48. Emanuele Rossi, Federico Monti, Michael Bronstein, Pietro liò. ncRNA Classification with Graph Convolutional Networks. SIGKDD 2019. paper

49. Wu F, Zhang T, Souza Jr A H, et al. Simplifying Graph Convolutional Networks. ICML 2019. paper.

50. Junhyun Lee, Inyeop Lee, Jaewoo Kang. Self-Attention Graph Pooling. ICML 2019. paper.

51. Chiang W L, Liu X, Si S, et al. Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks. SIGKDD 2019. paper.

52. Namyong Park, Andrey Kan, Xin Luna Dong, Tong Zhao, Christos Faloutsos, Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks. SIGKDD 2019. paper.

53. Wu S, Tang Y, Zhu Y, et al. Session-based Recommendation with Graph Neural Networks. AAAI 2019. paper.

54. Qu M, Bengio Y, Tang J. GMNN: Graph Markov Neural Networks. ICML 2019. papercoder.

55. Li Y, Gu C, Dullien T, et al. Graph Matching Networks for Learning the Similarity of Graph Structured Objects, ICML 2019.paper.

56. Gao H, Ji S. Graph U-Nets, ICML 2019. paper.

57. Bojchevski A, Günnemann S. Adversarial Attacks on Node Embeddings via Graph Poisoning, ICML 2019. paper.

58. Jeong D, Kwon T, Kim Y, et al. Graph Neural Network for Music Score Data and Modeling Expressive Piano Performance. ICML 2019. paper.

59. Zhang G, He H, Katabi D. Circuit-GNN: Graph Neural Networks for Distributed Circuit Design. ICML 2019. paper.

60. Alet F, Jeewajee A K, Bauza M, et al. Graph Element Networks: adaptive, structured computation and memory, ICML 2019. paper.

61. Rieck B, Bock C, Borgwardt K. A Persistent Weisfeiler-Lehman Procedure for Graph Classification, ICML 2019. paper.

62. Walker I, Glocker B. Graph Convolutional Gaussian Processes，ICML 2019. paper.

63. Yu Y, Chen J, Gao T, et al. DAG-GNN: DAG Structure Learning with Graph Neural Networks, ICML 2019. paper.

64. Zhijiang Guo, Yan Zhang and Wei Lu, Attention Guided Graph Convolutional Networks for Relation Extraction ACL 2019. paper. coder.

65. Chang Li, Dan Goldwasser. Encoding Social Information with Graph Convolutional Networks for Political Perspective Detection in News Media ACL 2019. paper.

66. Hao Zhu, Yankai Lin, Zhiyuan Liu, Jie Fu, Tat-seng Chua, Maosong Sun. Graph Neural Networks with Generated Parameters for Relation Extraction ACL 2019. paper.

67. Shikhar Vashishth, Manik Bhandari, Prateek Yadav, Piyush Rai, Chiranjib Bhattacharyya, Partha Talukdar. Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks ACL 2019. paper.

68. Cui Z, Li Z, Wu S, et al. Dressing as a Whole: Outfit Compatibility Learning Based on Node-wise Graph Neural Networks WWW 2019. paper.

69. Zhang, Chris, et al. Graph HyperNetworks for Neural Architecture Search. ICLR 2019. paper.

70. Chen, Zhengdao, et al. Supervised Community Detection with Line Graph Neural Networks. ICLR 2019. paper.

71. Maron, Haggai, et al. Invariant and Equivariant Graph Networks. ICLR 2019. paper.

72. Gulcehre, Caglar, et al. Hyperbolic Attention Networks. ICLR, 2019. paper.

73. Prates, Marcelo O. R., et al. Learning to Solve NP-Complete Problems -- A Graph Neural Network for the Decision TSP. AAAI, 2019. paper.

74. Liu, Ziqi, et al. GeniePath: Graph Neural Networks with Adaptive Receptive Paths. AAAI, 2019. paper.

75. Keriven N, Peyré G. Universal invariant and equivariant graph neural networks. NeurIPS, 2019. paper.

76. Qi Liu, et al. Hyperbolic Graph Neural Networks. NeurIPS, 2019. The paper is not yet available.

77. Zhitao Ying, et al. GNNExplainer: Generating Explanations for Graph Neural Networks. NeurIPS, 2019. The paper is not yet available.

78. Yaqin Zhou, et al. Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks. NeurIPS, 2019. The paper is not yet available.

79. Ehsan Hajiramezanali, et al. Variational Graph Recurrent Neural Networks. NeurIPS, 2019. The paper is not yet available.

80. Sitao Luan, et al. Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks. NeurIPS, 2019. The paper is not yet available.

81. Difan Zou, et al. Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks. NeurIPS, 2019. The paper is not yet available.

82. Seongjun Yun, et al. Graph Transformer Networks. NeurIPS, 2019. The paper is not yet available.

83. Andrei Nicolicioiu, et al. Recurrent Space-time Graph Neural Networks. NeurIPS, 2019. The paper is not yet available.

84. Nima Dehmamy, et al. Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology. NeurIPS, 2019. The paper is not yet available.

85. Maxime Gasse, et al. Exact Combinatorial Optimization with Graph Convolutional Neural Networks. NeurIPS, 2019. The paper is not yet available.

86. Zhengdao Chen, et al. On the equivalence between graph isomorphism testing and function approximation with GNNs. NeurIPS, 2019. The paper is not yet available.

87. Vineet Kosaraju, et al. Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks. NeurIPS, 2019. The paper is not yet available.

88. Carl Yang, et al.Conditional Structure Generation through Graph Variational Generative Adversarial Nets. NeurIPS, 2019. The paper is not yet available.

89. Naganand Yadati, et al.HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs. NeurIPS, 2019. The paper is not yet available.

90. Haggai Maron, et al.Provably Powerful Graph Networks. NeurIPS, 2019. The paper is not yet available.

91. Eliya Nachmani, et al.Hyper-Graph-Network Decoders for Block Codes. NeurIPS, 2019. The paper is not yet available.

92. Hanjun Dai, et al.Learning Transferable Graph Exploration. NeurIPS, 2019. The paper is not yet available.

93. Ryoma Sato, et al.Approximation Ratios of Graph Neural Networks for Combinatorial Problems. NeurIPS, 2019. The paper is not yet available.

94. Boris Knyazev, et al.Understanding attention in graph neural networks. NeurIPS, 2019. The paper is not yet available.

95. Renjie Liao, et al.Efficient Graph Generation with Graph Recurrent Attention Networks. NeurIPS, 2019. The paper is not yet available.

96. Bryan Wilder, et al.End to end learning and optimization on graphs. NeurIPS, 2019. The paper is not yet available.

97. Simon Du, et al.Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels. NeurIPS, 2019. The paper is not yet available.

98. W. O. K. Asiri Suranga Wijesinghe, et al. DFNets: Spectral CNNs for Graphs with Feedback-looped Filters. NeurIPS, 2019. The paper is not yet available.

ArXiv papers:

1. Li Y, Tarlow D, Brockschmidt M, et al. Gated graph sequence neural networks. arXiv 2015. paper

2. Henaff M, Bruna J, LeCun Y. Deep convolutional networks on graph-structured data, arXiv 2015. paper

3. Hechtlinger Y, Chakravarti P, Qin J. A generalization of convolutional neural networks to graph-structured data. arXiv 2017. paper

4. Marcheggiani D, Titov I. Encoding sentences with graph convolutional networks for semantic role labeling. arXiv 2017. paper

5. Battaglia P W, Hamrick J B, Bapst V, et al. Relational inductive biases, deep learning, and graph networks, arXiv 2018. paper

6. Verma S, Zhang Z L. Graph Capsule Convolutional Neural Networks. arXiv 2018. paper

7. Zhang T , Zheng W , Cui Z , et al. Tensor graph convolutional neural network. arXiv 2018. paper

8. Zou D, Lerman G. Graph Convolutional Neural Networks via Scattering. arXiv 2018. paper

9. Du J , Zhang S , Wu G , et al. Topology Adaptive Graph Convolutional Networks. arXiv 2018. paper.

10. Shang C , Liu Q , Chen K S , et al. Edge Attention-based Multi-Relational Graph Convolutional Networks. arXiv 2018. paper.

11. Scardapane S , Vaerenbergh S V , Comminiello D , et al. Improving Graph Convolutional Networks with Non-Parametric Activation Functions. arXiv 2018. paper.

12. Wang Y , Sun Y , Liu Z , et al. Dynamic Graph CNN for Learning on Point Clouds. arXiv 2018. paper.

13. Ryu S , Lim J , Hong S H , et al. Deeply learning molecular structure-property relationships using attention- and gate-augmented graph convolutional network. arXiv 2018. paper.

14. Cui Z , Henrickson K , Ke R , et al. High-Order Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting. arXiv 2018. paper.

15. Shchur O , Mumme M , Bojchevski A , et al. Pitfalls of Graph Neural Network Evaluation. arXiv 2018. paper.

16. Bai Y , Ding H , Bian S , et al. Graph Edit Distance Computation via Graph Neural Networks. arXiv 2018. paper.

17. Pedro H. C. Avelar, Henrique Lemos, Marcelo O. R. Prates, Luis Lamb, Multitask Learning on Graph Neural Networks - Learning Multiple Graph Centrality Measures with a Unified Network. arXiv 2018. paper.

18. Matthew Baron, Topology and Prediction Focused Research on Graph Convolutional Neural Networks. arXiv 2018. paper.

19. Wenting Zhao, Chunyan Xu, Zhen Cui, Tong Zhang, Jiatao Jiang, Zhenyu Zhang, Jian Yang, When Work Matters: Transforming Classical Network Structures to Graph CNN. arXiv 2018. paper.

20. Xavier Bresson, Thomas Laurent, Residual Gated Graph ConvNets. arXiv 2018. paper.

21. Kun XuLingfei WuZhiguo WangYansong FengVadim Sheinin, Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks. arXiv 2018. paper.

22. Xiaojie GuoLingfei WuLiang Zhao. Deep Graph Translation. arXiv 2018. paper.

23. Choma, Nicholas, et al. Graph Neural Networks for IceCube Signal Classification. ArXiv 2018. paper.

24. Tyler Derr, Yao Ma, Jiliang Tang. Signed Graph Convolutional Network ArXiv 2018. paper.

25. Yawei Luo, Tao Guan, Junqing Yu, Ping Liu, Yi Yang. Every Node Counts: Self-Ensembling Graph Convolutional Networks for Semi-Supervised Learning ArXiv 2018. paper.

26. Sun K, Koniusz P, Wang J. Fisher-Bures Adversary Graph Convolutional Networks. arXiv 2019. paper.

27. Kazi A, Burwinkel H, Vivar G, et al. InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction. arXiv 2019. paper.

28. Lemos H, Prates M, Avelar P, et al. Graph Colouring Meets Deep Learning: Effective Graph Neural Network Models for Combinatorial Problems. arXiv 2019. paper.

29. Diehl F, Brunner T, Le M T, et al. Graph Neural Networks for Modelling Traffic Participant Interaction. arXiv 2019. paper.

30. Murphy R L, Srinivasan B, Rao V, et al. Relational Pooling for Graph Representations. arXiv 2019. paper.

31. Zhang W, Shu K, Liu H, et al. Graph Neural Networks for User Identity Linkage. arXiv 2019. paper.

32. Ruiz L, Gama F, Ribeiro A. Gated Graph Convolutional Recurrent Neural Networks. arXiv 2019. paper.

33. Phillips S, Daniilidis K. All Graphs Lead to Rome: Learning Geometric and Cycle-Consistent Representations with Graph Convolutional Networks. arXiv 2019. paper.

34. Hu F, Zhu Y, Wu S, et al. Semi-supervised Node Classification via Hierarchical Graph Convolutional Networks. arXiv 2019. paper.

35. Deng Z, Dong Y, Zhu J. Batch Virtual Adversarial Training for Graph Convolutional Networks. arXiv 2019. paper.

36. Chen Z M, Wei X S, Wang P, et al.Multi-Label Image Recognition with Graph Convolutional Networks. arXiv 2019. paper.

37. Mallea M D G, Meltzer P, Bentley P J. Capsule Neural Networks for Graph Classification using Explicit Tensorial Graph Representations. arXiv 2019. paper.

38. Peter Meltzer, Marcelo Daniel Gutierrez Mallea and Peter J. Bentley. PiNet: A Permutation Invariant Graph Neural Network for Graph Classification. arXiv 2019. paper.

39. Padraig Corcoran. Function Space Pooling For Graph Convolutional Networks. arXiv 2019. paper.

40. Sbastien Lerique, Jacob Levy Abitbol, and Mrton Karsai. Joint embedding of structure and features via graph convolutional networks. arXiv 2019. paper.

41. Li G, Müller M, Thabet A, et al. Can GCNs Go as Deep as CNNs?. arXiv 2019. paper.

42. Ohue M, Ii R, Yanagisawa K, et al. Molecular activity prediction using graph convolutional deep neural network considering distance on a molecular graph. arXiv 2019. paper.

43. Gao X, Xiong H, Frossard P. iPool--Information-based Pooling in Hierarchical Graph Neural Networks. arXiv 2019. paper.

Open source platform on GNN

1. Deep Graph Library（DGL)

DGL is developed and maintained by New York University, New York University Shanghai, AWS Shanghai Research Institute and AWS MXNet Science Team.

Initiation time: 2018.

Source: URL, github

2. NGra

NGra is developed and maintained by Peking University and Microsoft Asia Research Institute.

Initiation time:2018

Source: pdf

3. Graph_nets

Graph_nets is developed and maintained by DeepMind, Google Corp.

Initiation time:2018

Source: github

4. Euler

Euler is developed and maintained by Alimama, which belongs to Alibaba Group.

Initiation time:2019

Source: github

5. PyTorch Geometric

PyTorch Geometric is developed and maintained by TU Dortmund University, Germany.

Initiation time:2019

Source: github paper

6. PyTorch-BigGraph（PBG）

PBG is developed and maintained by Facebook AI Research.

Initiation time:2019

Source: github paper

Appetizer for you：Art Exhibition in the Ultra-High Dimensional Network/Graph Structured Space

1. The interesting Social Network.

2. The beauty of the Biological Network.