【论文整理】GNN 论文持续追踪 Must-read papers and continuous track 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 odel 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 Allen Bluce (Bentian Li) and Anne Bluce (Yunxia Lin), If there is something wrong or GNN-related issue, welcome to send email (Address: jdlc105@qq.com, lbtjackbluce@gmail.com). Technology Keyword: Graph Neural Network, Graph convolutional network, Graph network, Graph attention network, Graph auto-encoder,… GNN and its variants are an emerging and powerful neural network approach. Its application is no longer limited to the original field. It has flourished in many other areas, such as Data Visualization, Image Processing, NLP, Recommendation System, Computer Vision, Bioinformatics, Chemical informatics, Drug Development and Discovery, Smart Transportation. 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 beecited 1,020 times in Google Scholar (on 09 May 2019). Update: 1, 065 times (on 20 May 2019); Update: 1, 106 times (on 27 May 2019); Update: 1, 227 times (on 19 June 2019); Update: 1, 377 times (on 8 July 2019); Update: 1, 678 times 17 Sept. 2019); Update: 1, 944 times (on 29 Oct. 2019); Update: 2,232 times (on 9 Dec. 2019) Thanks for giving us so many stars and supports from many developers and scientists on Github ！！！
We will continue to make this project better. Project Start time: 11 Dec 2018, Latest updated time: 9 Dec. 2019
More papers about GNN models and their applications will come from AAAI2020 … 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 DiffusMRI 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 27. Heyuan Shi, et al. Hypergraph-Induced Convolutional Networks for Visual Classification. IEEE Transactions on Neural Networks and Learning Systems, 2019. paper 28. S.Pan, et al. Learning Graph Embedding With Adversarial Training Methods. IEEE Transactions on Cybernetics, 2019. paper 29. D. Grattarola, et al. Change Detection in Graph Streams by Learning Graph Embeddings on Constant-Curvature Manifolds. IEEE Transactions on Neural Networks and Learning Systems. 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. Yu B, Yin H, Zhu Z. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. IJCAI 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. i 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. AggarwalJiliang 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 Imptance 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. 99. Dong Wook Shu, et al.3D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutions. ICCV 2019. paper 100. Yujun Cai, et al. Exploiting Spatial-temporal Relationships for 3D Pose Estimation via Graph Convolutional Networks. ICCV 2019. paper 101. Runhao Zeng, et al. Graph Convolutional Networks for Temporal Action Localization. ICCV 2019. paper 102. Yin Bi, et al. Graph-Based Object Classification for Neuromorphic Vision Sensing. ICCV 2019. paper 103.Tianshui Chen, et al. Learning Semantic-Specific Graph Representation for Multi-Label Image Recognition. ICCV 2019. paper 104. Linjie Li, et al. Relation-Aware Graph Attention Network for Visual Question Answering. ICCV 2019. paper 105. Jiwoong Park, et al. Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learning. ICCV 2019. paper 106. Runzhong Wang, et al. Learning Combinatorial Embedding Networks for Deep Graph Matching. ICCV 2019. paper 107. Zhiqiang Tao, et al. Adversarial Graph Embedding for Ensemble Clustering. IJCAI 2019. paper 108. Xiaotong Zhang, et al. Attributed Graph Clustering via Adaptive Graph Convolution. IJCAI 2019. paper 109. Jianwen Jiang, et al. Dynamic Hypergraph Neural Networks. IJCAI 2019. paper 110. Hogun Park, et al. Exploiting Interaction Links for Node Classification with Deep Graph Neural Networks. IJCAI 2019. paper 111. Hao Peng, et al. Fine-grained Event Categorization with Heterogeneous Graph Convolutional Networks. IJCAI 2019. paper 112. Chengfeng Xu, et al. Graph Contextualized Self-Attention Network for Session-based Recommendation. IJCAI 2019. paper 113. Ruiqing Xu, et al. Graph Convolutional Network Hashing for Cross-Modal Retrieval. IJCAI 2019. paper 114. Bingbing Xu, et al. Graph Convolutional Networks using Heat Kernel for Semi-supervised Learning. IJCAI 2019. paper
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DGL is developed d 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）
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PBG is developed and maintained by Facebook AI Research. Initiation time:2019 Source: github paper 7. Angel Angel is developed and maintained by Tencent Inc. Initiation time:2019
Source: github 8. Plato –NEW! Plato is developed and maintained by Tencent Inc. Initiation time:2019 Source: github 9. PGL –NEW! PGL is developed and maintained by Baidu Inc. Initiation time:2019 Source: github 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.