https://azure.microsoft.com/en-us/blog/announcing-microsoft-s-coco-framework-for-enterprise-blockchain-networks/ https://opensourcelibs.com/lib/diffnet https://github.com/Messi-Q/GraphDeeSmartContract https://xscode.com/jdlc105/Must-read-papers-and-continuous-tracking-on-Graph-Neural-Network-GNN-progress 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 the above 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 Allen Bluce (Dr. Bentian Li) and Anne Bluce (Dr. 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, Graph convolutional reinforcement learning, Graph capsule neural network.... 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 been cited 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 (on 17 Sept. 2019); Update: 1, 944 times (on 29 Oct. 2019); Update: 2, 232 times (on 9 Dec. 2019); Update: 2, 677 times (on 2 Feb. 2020).Update: 3, 018 times (on 17 March. 2020); Update: 3,560 times (on 27 May. 2020); Update: 4,060 times (on 3 July. 2020); Update: 5,371 times (on 25 Oct. 2020). Update: 6,258 times (on 01 Jan. 2021). Thanks for giving us so many stars and supports from the developers and scientists on Github around the world！！！ We will continue to make this project better. Project Start time: 11 Dec 2018, Latest updated time: 01 Jan. 2021 New papers about GNN models and their applications have come from NeurIPS2020, AAAI2021 .... We are waiting for more paper to be released. Survey papers: Bronstein M M, Bruna J, LeCun Y, et al. Geometric deep learning: going beyond euclidean data. IEEE Signal Processing Magazine, 2017, 34(4): 18-42. paper Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Maosong Sun, Graph Neural Networks: A Review of Methods and Applications, ArXiv, 2018. paper. Battaglia P W, Hamrick J B, Bapst V, et al. Relational inductive biases, deep learning, and graph networks, arXiv 2018. paper Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, Philip S. Yu(Fellow,IEEE), A Comprehensive Survey on Graph Neural Networks, IEEE Transactions on Neural Networks and Learning Systems, 2020. paper. Ziwei Zhang, Peng Cui, Wenwu Zhu, Deep Learning on Graphs: A Survey, IEEE Transactions on Knowledge and Data Engineering, 2020. paper. Chen Z, Chen F, Zhang L, et al. Bridging the Gap between Spatial and Spectral Domains: A Survey on Graph Neural Networks. arXiv preprint. 2020. paper Abadal S, Jain A, Guirado R, et al. Computing Graph Neural Networks: A Survey from Algorithms to Accelerators. arXiv preprint. 2020. paper Lamb L, Garcez A, Gori M, et al. Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective. arXiv preprint. 2020. paper Journal papers: 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. Scarselli F, Gori M, Tsoi A C, et al. Computational capabilities of graph neural networks, IEEE Transactions on Neural Networks, 2009. paper. Micheli A . Neural Network for Graphs: A Contextual Constructive Approach. IEEE Transactions on Neural Networks, 2009. paper. Goles, Eric, and Gonzalo A. Ruz. Dynamics of Neural Networks over Undirected Graphs. Neural Networks, 2015. paper. 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. 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. Kawahara J, Brown C J, Miller S P, et al. BrainNetCNN: convolutional neural networks for brain networks; towards predicting neurodevelopment. NeuroImage, 2017. paper. 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. 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. Marinka Z , Monica A , Jure L . Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics, 2018. paper. 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. 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. Xie T , Grossman J C . Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties. Physical Review Letters, 2018. paper. 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 Song T, Zheng W, Song P, et al. Eeg emotion recognition using dynamical graph convolutional neural networks. IEEE Transactions on Affective Computing, 2018. paper 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 Zhang, Zhihong, Dongdong Chen, Jianjia Wang, Lu Bai, and Edwin R. Hancock. Quantum-Based Subgraph Convolutional Neural Networks. Pattern Recognition, 2019. paper 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 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 Zhang Z, Chen D, Wang Z, et al. Depth-based Subgraph Convolutional Auto-Encoder for Network Representation Learning. Pattern Recognition, 2019. paper 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 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 Zhang Q, Chang J, Meng G, et al. Learning graph structure via graph convolutional networks. Pattern Recognition, 2019. paper 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 Sun M, Zhao S, Gilvary C, et al. Graph convolutional networks for computational drug development and discovery. Briefings in bioinformatics, 2019. paper 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 Heyuan Shi, et al. Hypergraph-Induced Convolutional Networks for Visual Classification. IEEE Transactions on Neural Networks and Learning Systems, 2019. paper S.Pan, et al. Learning Graph Embedding With Adversarial Training Methods. IEEE Transactions on Cybernetics, 2019. paper 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. 2019. paper Kan Guo, et al. Optimized Graph Convolution Recurrent Neural Network for Traffic Prediction. IEEE Transactions on Intelligent Transportation Systems. 2020. paper Ruiz L, et al. Invariance-preserving localized activation functions for graph neural networks. IEEE Transactions on Signal Processing, 2020. paper Li J, et al. Neural Inductive Matrix Completion with Graph Convolutional Networks for miRNA-disease Association Prediction. Bioinformatics, 2020. paper Bingzhi Chen, et al. Label Co-occurrence Learning with Graph Convolutional Networks for Multi-label Chest X-ray Image Classification. IEEE Journal of Biomedical and Health Informatics, 2020. paper Kunjin Chen, et al. Fault Location in Power Distribution Systems via Deep Graph Convolutional Networks. IEEE Journal on Selected Areas in Communications, 2020. paper Manessi, Franco, et al. Dynamic graph convolutional networks. Pattern Recognition, 2020. paper Jiang X, Zhu R, Li S, et al. Co-embedding of Nodes and Edges with Graph Neural Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence. paper Wang Z, Ji S. Second-order pooling for graph neural networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020. paper Indro Spinelli, et al. Adaptive Propagation Graph Convolutional Network. IEEE Transactions on Neural Networks and Learning Systems, 2020. paper Zhou Fan, et al. Reinforced Spatiotemporal Attentive Graph Neural Networks for Traffic Forecasting. IEEE Internet of Things Journal, 2020. paper Wang S H, et al. Covid-19 classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network. Information Fusion, 2020. paper Ruiz, Luana et al. Gated Graph Recurrent Neural Networks, IEEE Transactions on Signal Processing. paper Gama, Fernando et al. Stability Properties of Graph Neural Networks, IEEE Transactions on Signal Processing. paper He, Xin et al. MV-GNN: Multi-View Graph Neural Network for Compression Artifacts Reduction, IEEE Transactions on Image Processing. paper Conference papers: Duvenaud D, Maclaurin D, Aguilera-Iparraguirre J, et al. Convolutional networks on graphs for learning molecular fingerprints, NeurIPS(NIPS) 2015. paper. code. M. Niepert, M. Ahmed, K. Kutzkov, Learning Convolutional Neural Networks for Graphs, ICML 2016. paper. S. Cao, W. Lu, Q. Xu, Deep neural networks for learning graph representations, AAAI 2016. paper. M. Defferrard, X. Bresson, P. Vandergheynst, Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering, NeurIPS(NIPS) 2016. paper. code. T.N. Kipf, M. Welling, Semi-Supervised Classification with Graph Convolutional Networks, ICLR 2017. paper. code. A. Fout, B. Shariat, J. Byrd, A. Benhur, Protein Interface Prediction using Graph Convolutional Networks, NeurIPS(NIPS) 2017. paper. Monti F, Bronstein M, Bresson X. Geometric matrix completion with recurrent multi-graph neural networks, NeurIPS(NIPS) 2017. paper. Simonovsky M, Komodakis N. Dynamic edgeconditioned filters in convolutional neural networks on graphs, CVPR. 2017. paper R. Li, S. Wang, F. Zhu, J. Huang, Adaptive Graph Convolutional Neural Networks, AAAI 2018. paper J. You, B. Liu, R. Ying, V. Pande, J. Leskovec, Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation, NeurIPS(NIPS) 2018. paper. C. Zhuang, Q. Ma, Dual Graph Convolutional Networks for Graph-Based Semi-Supervised Classification, WWW 2018. paper H. Gao, Z. Wang, S. Ji, Large-Scale Learnable Graph Convolutional Networks, KDD 2018. paper D. Zügner, A. Akbarnejad, S. Günnemann, Adversarial Attacks on Neural Networks for Graph Data, KDD 2018. paper Ying R , He R , Chen K , et al. Graph Convolutional Neural Networks for Web-Scale Recommender Systems. KDD 2018. paper P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò, Y. Bengio, Graph Attention Networks, ICLR, 2018. paper Beck, Daniel Edward Robert, Gholamreza Haffari and Trevor Cohn. Graph-to-Sequence Learning using Gated Graph Neural Networks. ACL 2018. paper Yu B, Yin H, Zhu Z. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. IJCAI 2018. paper Chen J , Zhu J , Song L . Stochastic Training of Graph Convolutional Networks with Variance Reduction. ICML 2018. paper Gusi Te, Wei Hu, Amin Zheng, Zongming Guo, RGCNN: Regularized Graph CNN for Point Cloud Segmentation. ACM Multimedia 2018. paper, code, Talukdar, Partha, Shikhar Vashishth, Shib Sankar Dasgupta and Swayambhu Nath Ray. Dating Documents using Graph Convolution Networks. ACL 2018. paper, code Sanchez-Gonzalez A , Heess N , Springenberg J T , et al. Graph networks as learnable physics engines for inference and control. ICML 2018. paper Muhan Zhang, Yixin Chen. Link Prediction Based on Graph Neural Networks. NeurIPS(NIPS) 2018. paper Chen, Jie, Tengfei Ma, and Cao Xiao. FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling. ICLR 2018. paper 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 Rahimi A , Cohn T , Baldwin T . Semi-supervised User Geolocation via Graph Convolutional Networks. ACL 2018. paper Morris C , Ritzert M , Fey M , et al.Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks.. AAAI 2019. paper Xu K, Hu W, Leskovec J, et al. How Powerful are Graph Neural Networks?, ICLR 2019. paper Johannes Klicpera, Aleksandar Bojchevski, Stephan Günnemann. Combining Neural Networks with Personalized PageRank for Classification on Graphs, ICLR 2019. paper Daniel Zügner, Stephan Günnemann. Adversarial Attacks on Graph Neural Networks via Meta Learning, ICLR 2019. paper Zhang Xinyi, Lihui Chen. Capsule Graph Neural Network, ICLR 2019. paper Liao, R., Zhao, Z., Urtasun, R., and Zemel, R. LanczosNet: Multi-Scale Deep Graph Convolutional Networks, ICLR 2019, paper Bingbing Xu, Huawei Shen, Qi Cao, Yunqi Qiu, Xueqi Cheng. Graph Wavelet Neural Network, ICLR 2019, paper Hu J, Guo C, Yang B, et al. Stochastic Weight Completion for Road Networks using Graph Convolutional Networks ICDE. 2019. paper Yao L, Mao C, Luo Y . Graph Convolutional Networks for Text Classification. AAAI 2019. paper Landrieu L , Boussaha M . Point Cloud Oversegmentation with Graph-Structured Deep Metric Learning. CVPR 2019. paper Si C , Chen W , Wang W , et al. An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition. CVPR 2019. paper Cucurull G , Taslakian P , Vazquez D . Context-Aware Visual Compatibility Prediction. CVPR 2019. paper 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 Michael Kampffmeyer, Yinbo Chen, Xiaodan Liang, Hao Wang, Yujia Zhang, Eric P. Xing. Rethinking Knowledge Graph Propagation for Zero-Shot Learning. CVPR 2019. paper Arushi Goel, Keng Teck Ma, Cheston Tan. An End-to-End Network for Generating Social Relationship Graphs. CVPR 2019. paper Yichao Yan, Qiang Zhang, Bingbing Ni, Wendong Zhang, Minghao Xu, Xiaokang Yang. Learning Context Graph for Person Search. CVPR 2019 paper Zhongdao Wang, Liang Zheng, Yali Li, Shengjin Wang. Linkage Based Face Clustering via Graph Convolution Network. CVPR 2019 paper Lei Yang, Xiaohang Zhan, Dapeng Chen, Junjie Yan, Chen Change Loy, Dahua Lin. Learning to Cluster Faces on an Affinity Graph. CVPR 2019 paper Yao Ma, Suhang Wang, Charu C. Aggarwal, Jiliang Tang. Graph Convolutional Networks with EigenPooling. KDD2019, paper Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, Dawei Yin. Graph Neural Networks for Social Recommendation. WWW2019, paper Kim J, Kim T, Kim S, et al. Edge-labeling Graph Neural Network for Few-shot Learning. CVPR 2019. paper Jessica V. Schrouff, Kai Wohlfahrt, Bruno Marnette, Liam Atkinson. INFERRING JAVASCRIPT TYPES USING GRAPH NEURAL NETWORKS. ICLR 2019. paper Emanuele Rossi, Federico Monti, Michael Bronstein, Pietro liò. ncRNA Classification with Graph Convolutional Networks. SIGKDD 2019. paper Wu F, Zhang T, Souza Jr A H, et al. Simplifying Graph Convolutional Networks. ICML 2019. paper. Junhyun Lee, Inyeop Lee, Jaewoo Kang. Self-Attention Graph Pooling. ICML 2019. paper. 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. Namyong Park, Andrey Kan, Xin Luna Dong, Tong Zhao, Christos Faloutsos, Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks. SIGKDD 2019. paper. Wu S, Tang Y, Zhu Y, et al. Session-based Recommendation with Graph Neural Networks. AAAI 2019. paper. Qu M, Bengio Y, Tang J. GMNN: Graph Markov Neural Networks. ICML 2019. papercoder. Li Y, Gu C, Dullien T, et al. Graph Matching Networks for Learning the Similarity of Graph Structured Objects, ICML 2019.paper. Gao H, Ji S. Graph U-Nets, ICML 2019. paper. Bojchevski A, Günnemann S. Adversarial Attacks on Node Embeddings via Graph Poisoning, ICML 2019. paper. Jeong D, Kwon T, Kim Y, et al. Graph Neural Network for Music Score Data and Modeling Expressive Piano Performance. ICML 2019. paper. Zhang G, He H, Katabi D. Circuit-GNN: Graph Neural Networks for Distributed Circuit Design. ICML 2019. paper. Alet F, Jeewajee A K, Bauza M, et al. Graph Element Networks: adaptive, structured computation and memory, ICML 2019. paper. Rieck B, Bock C, Borgwardt K. A Persistent Weisfeiler-Lehman Procedure for Graph Classification, ICML 2019. paper. Walker I, Glocker B. Graph Convolutional Gaussian Processes，ICML 2019. paper. Yu Y, Chen J, Gao T, et al. DAG-GNN: DAG Structure Learning with Graph Neural Networks, ICML 2019. paper. Zhijiang Guo, Yan Zhang and Wei Lu, Attention Guided Graph Convolutional Networks for Relation Extraction ACL 2019. paper. coder. Chang Li, Dan Goldwasser. Encoding Social Information with Graph Convolutional Networks for Political Perspective Detection in News Media ACL 2019. paper. 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. 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. 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. Zhang, Chris, et al. Graph HyperNetworks for Neural Architecture Search. ICLR 2019. paper. Chen, Zhengdao, et al. Supervised Community Detection with Line Graph Neural Networks. ICLR 2019. paper. Maron, Haggai, et al. Invariant and Equivariant Graph Networks. ICLR 2019. paper. Gulcehre, Caglar, et al. Hyperbolic Attention Networks. ICLR, 2019. paper. Prates, Marcelo O. R., et al. Learning to Solve NP-Complete Problems -- A Graph Neural Network for the Decision TSP. AAAI, 2019. paper. Liu, Ziqi, et al. GeniePath: Graph Neural Networks with Adaptive Receptive Paths. AAAI, 2019. paper. Keriven N, Peyré G. Universal invariant and equivariant graph neural networks. NeurIPS, 2019. paper. Qi Liu, et al. Hyperbolic Graph Neural Networks. NeurIPS, 2019. paper. Zhitao Ying, et al. GNNExplainer: Generating Explanations for Graph Neural Networks. NeurIPS, 2019. paper. Yaqin Zhou, et al. Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks. NeurIPS, 2019. paper. Ehsan Hajiramezanali, et al. Variational Graph Recurrent Neural Networks. NeurIPS, 2019. paper. Sitao Luan, et al. Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks. NeurIPS, 2019. paper. Difan Zou, et al. Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks. NeurIPS, 2019. paper. Seongjun Yun, et al. Graph Transformer Networks. NeurIPS, 2019. paper. Andrei Nicolicioiu, et al. Recurrent Space-time Graph Neural Networks. NeurIPS, 2019. paper. Nima Dehmamy, et al. Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology. NeurIPS, 2019. paper. Maxime Gasse, et al. Exact Combinatorial Optimization with Graph Convolutional Neural Networks. NeurIPS, 2019. paper. Zhengdao Chen, et al. On the equivalence between graph isomorphism testing and function approximation with GNNs. NeurIPS, 2019. paper. Vineet Kosaraju, et al. Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks. NeurIPS, 2019. paper. Carl Yang, et al.Conditional Structure Generation through Graph Variational Generative Adversarial Nets. NeurIPS, 2019. paper. Naganand Yadati, et al.HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs. NeurIPS, 2019. paper. Haggai Maron, et al.Provably Powerful Graph Networks. NeurIPS, 2019. paper. Eliya Nachmani, et al.Hyper-Graph-Network Decoders for Block Codes. NeurIPS, 2019. paper. Hanjun Dai, et al.Learning Transferable Graph Exploration. NeurIPS, 2019. paper. Ryoma Sato, et al.Approximation Ratios of Graph Neural Networks for Combinatorial Problems. NeurIPS, 2019. paper. Boris Knyazev, et al.Understanding Attention and Generalization in Graph Neural Networks. NeurIPS, 2019. paper. Renjie Liao, et al.Efficient Graph Generation with Graph Recurrent Attention Networks. NeurIPS, 2019. paper. Bryan Wilder, et al.End to end learning and optimization on graphs. NeurIPS, 2019. paper. Simon Du, et al.Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels. NeurIPS, 2019. paper. W. O. K. Asiri Suranga Wijesinghe, et al. DFNets: Spectral CNNs for Graphs with Feedback-looped Filters. NeurIPS, 2019. paper. Dong Wook Shu, et al.3D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutions. ICCV 2019. paper Yujun Cai, et al. Exploiting Spatial-temporal Relationships for 3D Pose Estimation via Graph Convolutional Networks. ICCV 2019. paper Runhao Zeng, et al. Graph Convolutional Networks for Temporal Action Localization. ICCV 2019. paper 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 Linjie Li, et al. Relation-Aware Graph Attention Network for Visual Question Answering. ICCV 2019. paper Jiwoong Park, et al. Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learning. ICCV 2019. paper Runzhong Wang, et al. Learning Combinatorial Embedding Networks for Deep Graph Matching. ICCV 2019. paper Zhiqiang Tao, et al. Adversarial Graph Embedding for Ensemble Clustering. IJCAI 2019. paper Xiaotong Zhang, et al. Attributed Graph Clustering via Adaptive Graph Convolution. IJCAI 2019. paper Jianwen Jiang, et al. Dynamic Hypergraph Neural Networks. IJCAI 2019. paper Hogun Park, et al. Exploiting Interaction Links for Node Classification with Deep Graph Neural Networks. IJCAI 2019. paper Hao Peng, et al. Fine-grained Event Categorization with Heterogeneous Graph Convolutional Networks. IJCAI 2019. paper Chengfeng Xu, et al. Graph Contextualized Self-Attention Network for Session-based Recommendation. IJCAI 2019. paper Ruiqing Xu, et al. Graph Convolutional Network Hashing for Cross-Modal Retrieval. IJCAI 2019. paper Bingbing Xu, et al. Graph Convolutional Networks using Heat Kernel for Semi-supervised Learning. IJCAI 2019. paper Zonghan Wu, et al. Graph WaveNet for Deep Spatial-Temporal Graph Modeling. IJCAI 2019. paper Fenyu Hu, et al. Hierarchical Graph Convolutional Networks for Semi-supervised Node Classification. IJCAI 2019. paper Li Zheng, et al. AddGraph: Anomaly Detection in Dynamic Graph Using Attention-based Temporal GCN. IJCAI 2019. paper Liang Yang, et al. Dual Self-Paced Graph Convolutional Network: Towards Reducing Attribute Distortions Induced by Topology. IJCAI 2019. paper Liang Yang, et al. Masked Graph Convolutional Network. IJCAI 2019. paper Xiaofeng Xu, et al. Learning Image-Specific Attributes by Hyperbolic Neighborhood Graph Propagation. IJCAI 2019. paper Li G, Müller M, Thabet A, et al. Can GCNs Go as Deep as CNNs?. ICCV 2019. paper. Park C, Lee C, Bahng H, et al. STGRAT: A Spatio-Temporal Graph Attention Network for Traffic Forecasting. AAAI 2020. paper. Liu Y, Wang X, Wu S, et al. Independence Promoted Graph Disentangled Networks. AAAI 2020. paper. Shi H, Fan H, Kwok J T. Effective Decoding in Graph Auto-Encoder using Triadic Closure. AAAI 2020. paper. Wang X, Wang R, Shi C, et al. Multi-Component Graph Convolutional Collaborative Filtering. AAAI 2020. paper. Su J, Beling P A, Guo R, et al. Graph Convolution Networks for Probabilistic Modeling of Driving Acceleration. AAAI 2020. paper. Claudio Gallicchio and Alessio Micheli. Fast and Deep Graph Neural Networks. AAAI 2020. paper. Peng W, Hong X, Chen H, et al. Learning Graph Convolutional Network for Skeleton-based Human Action Recognition by Neural Searching. AAAI 2020. paper. Paliwal A, Loos S, Rabe M, et al. Graph Representations for Higher-Order Logic and Theorem Proving. AAAI 2020. paper. Kenta Oono, et al. Graph Neural Networks Exponentially Lose Expressive Power for Node Classification. ICLR 2020. paper. Muhan Zhang, et al. Inductive Matrix Completion Based on Graph Neural Networks. ICLR 2020. paper. Pablo Barceló, et al. The Logical Expressiveness of Graph Neural Networks. ICLR 2020. paper Weihua Hu, et al. Strategies for Pre-training Graph Neural Networks. ICLR 2020. paper Hongbin Pei, et al. Geom-GCN: Geometric Graph Convolutional Networks. ICLR 2020. paper Ze Ye, et al. Curvature Graph Network. ICLR 2020. paper Andreas Loukas, et al. What graph neural networks cannot learn: depth vs width. ICLR 2020. paper Federico Errica, et al. A Fair Comparison of Graph Neural Networks for Graph Classification. ICLR 2020. paper Kai Zhang, et al. Adaptive Structural Fingerprints for Graph Attention Networks. ICLR 2020. paper Shikhar Vashishth, et al. Composition-based Multi-Relational Graph Convolutional Networks. ICLR 2020. paper Jiayi Wei, et al. LambdaNet: Probabilistic Type Inference using Graph Neural Networks. ICLR 2020. paper Jiechuan Jiang, et al. Graph Convolutional Reinforcement Learning. ICLR 2020. paper Yifan Hou, et al. Measuring and Improving the Use of Graph Information in Graph Neural Networks. ICLR 2020. paper Ruochi Zhang, et al. Hyper-SAGNN: a self-attention based graph neural network for hypergraphs. ICLR 2020. paper Yu Rong, et al. DropEdge: Towards Deep Graph Convolutional Networks on Node Classification. ICLR 2020. paper Yuyu Zhang, et al. Efficient Probabilistic Logic Reasoning with Graph Neural Networks. ICLR 2020. paper Amir hosein Khasahmadi, et al. 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Initiation time:2018 Source: pdf Graph_nets Graph_nets is developed and maintained by DeepMind, Google Corp. Initiation time:2018 Source: github Euler Euler is developed and maintained by Alimama, which belongs to Alibaba Group. Initiation time:2019 Source: github PyTorch Geometric PyTorch Geometric is developed and maintained by TU Dortmund University, Germany. Initiation time:2019 Source: github paper PyTorch-BigGraph（PBG） PBG is developed and maintained by Facebook AI Research. Initiation time:2019 Source: github paper Angel Angel is developed and maintained by Tencent Inc. Initiation time:2019 Source: github Plato Plato is developed and maintained by Tencent Inc. Initiation time:2019 Source: github PGL PGL is developed and maintained by Baidu Inc. Initiation time:2019 Source: github OGB Open Graph Benchmark(OGB) is developed and maintained by Standford University. 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