图神经网络(Graph Neural Networks, GNN)专知荟萃

入门

• A Gentle Introduction to Graph Neural Networks (Basics, DeepWalk, and GraphSage).
  https://towardsdatascience.com/a-gentle-introduction-to-graph-neural-network-basics-deepwalk-and-graphsage-db5d540d50b3
• Intro to Graph Convolutional Networks
  https://www.youtube.com/watch?v=UAwrDY_Bcdc

综述

• A Comprehensive Survey on Graph Neural Networks. Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, Philip S. Yu. 2019
  https://arxiv.org/pdf/190-00596.pdf
• Relational inductive biases, deep learning, and graph networks. Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, Caglar Gulcehre, Francis Song, Andrew Ballard, Justin Gilmer, George Dahl, Ashish Vaswani, Kelsey Allen, Charles Nash, Victoria Langston, Chris Dyer, Nicolas Heess, Daan Wierstra, Pushmeet Kohli, Matt Botvinick, Oriol Vinyals, Yujia Li, Razvan Pascanu. 2018.
  https://arxiv.org/pdf/1806.0126-pdf
• Attention models in graphs. John Boaz Lee, Ryan A. Rossi, Sungchul Kim, Nesreen K. Ahmed, Eunyee Koh. 2018.
  https://arxiv.org/pdf/1807.07984.pdf
• Deep learning on graphs: A survey. Ziwei Zhang, Peng Cui and Wenwu Zhu. 2018.
  https://arxiv.org/pdf/1812.04202.pdf
• Graph Neural Networks: A Review of Methods and Applications. Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Maosong Sun. 2018
  https://arxiv.org/pdf/1812.08434.pdf
• Geometric deep learning: going beyond euclidean data. Michael M. Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, Pierre Vandergheynst. 2016.
  https://arxiv.org/pdf/161-08097.pdf

进阶论文

Recurrent Graph Neural Networks

• Learning steady-states of iterative algorithms over graphs. Hanjun Dai, Zornitsa Kozareva, Bo Dai, Alexander J. Smola, Le Song ICML 2018.
  http://proceedings.mlr.press/v80/dai18a/dai18a.pdf
• Gated graph sequence neural networks. Yujia Li, Richard Zemel, Marc Brockschmidt, Daniel Tarlow. ICLR 2015.
  https://arxiv.org/pdf/151-05493.pdf
• Graph echo state networks. Claudio Gallicchio, Alessio Micheli IJCNN 2010.
  https://ieeexplore.ieee.org/abstract/document/5596796
• The graph neural network model. Franco Scarselli,Marco Gori,Ah Chung Tsoi,Markus Hagenbuchner,Gabriele Monfardini. 2009.
  http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.--1015.7227&rep=rep1&type=pdf
• A new model for learning in graph domains. Marco Gori, Gabriele Monfardini, Franco Scarselli. IJCNN 2005.
  https://ieeexplore.ieee.org/abstract/document/1555942
• Supervised neural networks for the classification of structures A. Sperduti and A. Starita. IEEE Transactions on Neural Networks 1997.
  https://www.ncbi.nlm.nih.gov/pubmed/18255672

Convolutional Graph Neural Networks

Spectral and Spatial

• DIFFUSION SCATTERING TRANSFORMS ON GRAPHS. Fernando Gama, Alejandro Ribeiro, Joan Bruna. ICLR 2019.
  https://arxiv.org/pdf/1806.08829.pdf
• Predict then Propagate: Graph Neural Networks meet Personalized PageRank. Johannes Klicpera, Aleksandar Bojchevski, Stephan Günnemann. ICLR 2019.
  https://arxiv.org/pdf/1810.05997.pdf) [code](https://github.com/benedekrozemberczki/APPNP code
• Simplifying Graph Convolutional Networks. Felix Wu, Tianyi Zhang, Amauri Holanda de Souza Jr., Christopher Fifty, Tao Yu, Kilian Q. Weinberger. ICML 2019.
  https://arxiv.org/pdf/1902.07153.pdf
• Graph Wavelet Neural Network. Bingbing Xu, Huawei Shen, Qi Cao, Yunqi Qiu, Xueqi Cheng. ICLR 2019.
  https://openreview.net/pdf?id=H1ewdiR5tQ
• Structure-Aware Convolutional Neural Networks. Jianlong Chang, Jie Gu, Lingfeng Wang, Gaofeng Meng, Shiming Xiang, Chunhong Pan. NeurIPS 2018.
  http://papers.nips.cc/paper/7287-structure-aware-convolutional-neural-networks.pdf
• On filter size in graph convolutional network. D. V. Tran, A. Sperduti et al. SSCI. IEEE, 2018.
  https://arxiv.org/pdf/181-10435.pdf
• Cayleynets: graph convolutional neural networks with complex rational spectral filters. Ron Levie, Federico Monti, Xavier Bresson, Michael M. Bronstein. 2017.
  https://arxiv.org/pdf/1705.07664.pdf
• Semi-supervised classification with graph convolutional networks. Thomas N. Kipf, Max Welling. ICLR 2017.
  https://arxiv.org/pdf/1609.02907.pdf
• Neural message passing for quantum chemistry. Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, George E. Dahl. ICML 2017.
  https://arxiv.org/pdf/1704.01212.pdf
• Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs Martin Simonovsky, Nikos Komodakis CVPR 2017.
  https://arxiv.org/pdf/1704.0290-pdf
• Geometric deep learning on graphs and manifolds using mixture model cnns. Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodolà, Jan Svoboda, Michael M. Bronstein. CVPR 2017.
  https://arxiv.org/pdf/161-08402.pdf
• Robust spatial filtering with graph convolutional neural networks. 2017. Felipe Petroski Such, Shagan Sah, Miguel Dominguez, Suhas Pillai, Chao Zhang, Andrew Michael, Nathan Cahill, Raymond Ptucha.
  https://arxiv.org/abs/1703.00792
• Convolutional neural networks on graphs with fast localized spectral filtering. Michaël Defferrard, Xavier Bresson, Pierre Vandergheynst. NIPS 2016.
  https://arxiv.org/pdf/1606.09375.pdf
• Diffusion-convolutional neural networks James Atwood, Don Towsley. NIPS 2016.
  https://arxiv.org/pdf/151-02136.pdf
• Deep convolutional networks on graph-structured data. Mikael Henaff, Joan Bruna, Yann LeCun. 2015.
  https://arxiv.org/abs/1506.05163
• Convolutional networks on graphs for learning molecular fingerprints. David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre Rafael Go ́mez-Bombarelli, Timothy Hirzel, Ala ́n Aspuru-Guzik, Ryan P. Adams., NIPS 2015.
  http://papers.nips.cc/paper/5954-convolutional-networks-on-graphs-for-learning-molecular-fingerprints.pdf
• Accelerated filtering on graphs using lanczos method. Ana Susnjara, Nathanael Perraudin, Daniel Kressner, Pierre Vandergheynst. 2015.
  https://arxiv.org/pdf/1509.04537.pdf
• Spectral networks and locally connected networks on graphs. Joan Bruna, Wojciech Zaremba, Arthur Szlam, Yann LeCun. ICLR 2014.
  https://arxiv.org/pdf/1312.6203.pdf
• Neural network for graphs: A contextual constructive approach. A. Micheli. IEEE Transactions on Neural Networks 2009.
  https://ieeexplore.ieee.org/abstract/document/4773279

Architecture

• Graph U-nets Hongyang Gao, Shuiwang Ji. ICML 2019.
  https://arxiv.org/pdf/1905.05178.pdf
• MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing. Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, Aram Galstyan.
  https://arxiv.org/pdf/1905.00067.pdf
• Representation learning on graphs with jumping knowledge networks. Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, Stefanie Jegelka. ICML 2018.
  https://arxiv.org/pdf/1806.03536.pdf
• Dual graph convolutional networks for graph-based semi-supervised classification Chenyi Zhuang, Qiang Ma. WWW 2018.
  https://dl.acm.org/citation.cfm?id=3186116

Attention Mechanisms

• Geniepath: Graph neural networks with adaptive receptive paths. Ziqi Liu, Chaochao Chen, Longfei Li, Jun Zhou, Xiaolong Li, Le Song, Yuan Qi. AAAI 2019.
  https://arxiv.org/pdf/1802.00910.pdf
• Graph Representation Learning via Hard and Channel-Wise Attention Networks. Hongyang Gao, Shuiwang Ji. 2019 KDD.
  https://www.kdd.org/kdd2019/accepted-papers/view/graph-representation-learning-via-hard-and-channel-wise-attention-networks
• Understanding Attention and Generalization in Graph Neural Networks. Boris Knyazev, Graham W. Taylor, Mohamed R. Amer. NeurIPS 2019.
  https://arxiv.org/abs/1905.02850
• Graph Attention Networks. Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, Yoshua Bengio. ICLR 2018.
  https://arxiv.org/pdf/1710.10903.pdf
• Gaan: Gated attention networks for learning on large and spatiotemporal graphs. Jiani Zhang, Xingjian Shi, Junyuan Xie, Hao Ma, Irwin King, Dit-Yan Yeung. 2018.
  https://arxiv.org/pdf/1803.07294.pdf

Convolution

• Large-Scale Learnable Graph Convolutional Networks. Hongyang Gao, Zhengyang Wang, Shuiwang Ji. KDD 2018.
  https://arxiv.org/pdf/1808.03965.pdf
• Learning convolutional neural networks for graphs. Mathias Niepert, Mohamed Ahmed, Konstantin Kutzkov. ICML 2016.
  https://arxiv.org/pdf/1605.05273.pdf

Training Methods

• Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks. KDD 2019.
  https://arxiv.org/pdf/1905.07953.pdf
• Stochastic Training of Graph Convolutional Networks with Variance Reduction. Jianfei Chen, Jun Zhu, Le Song. ICML 2018.
  https://arxiv.org/pdf/1710.10568.pdf
• Adaptive Sampling Towards Fast Graph Representation Learning. Wenbing Huang, Tong Zhang, Yu Rong, Junzhou Huang. NeurIPS 2018.
  https://arxiv.org/pdf/1809.05343.pdf
• FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling. Jie Chen, Tengfei Ma, Cao Xiao. ICLR 2018.
  https://arxiv.org/pdf/180-10247.pdf
• Inductive representation learning on large graphs. William L. Hamilton, Rex Ying, Jure Leskovec. NIPS 2017.
  http://papers.nips.cc/paper/6703-inductive-representation-learning-on-large-graphs.pdf

Pooling

• Self-Attention Graph Pooling. Junhyun Lee, Inyeop Lee, Jaewoo Kang. ICML 2019.
  https://arxiv.org/abs/1904.08082
• Hierarchical graph representation learning with differentiable pooling. Rex Ying, Jiaxuan You, Christopher Morris, Xiang Ren, William L. Hamilton, Jure Leskovec. NeurIPS 2018.
  https://arxiv.org/pdf/1806.08804.pdf

Bayesian

• Bayesian Graph Convolutional Neural Networks for Semi-supervised Classification Yingxue Zhang, Soumyasundar Pal, Mark Coates, Deniz Üstebay. AAAI 2019.
  https://arxiv.org/pdf/181-11103.pdf
• Bayesian Semi-supervised Learning with Graph Gaussian Processes . Yin Cheng Ng, Nicolò Colombo, Ricardo Silva NeurIPS 2018.
  https://papers.nips.cc/paper/7440-bayesian-semi-supervised-learning-with-graph-gaussian-processes.pdf

Analysis

• How powerful are graph neural networks? Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka. ICLR 2019.
  https://arxiv.org/pdf/1810.00826.pdf
• Can GCNs Go as Deep as CNNs?. Guohao Li, Matthias Müller, Ali Thabet, Bernard Ghanem. 2019. ICCV 2019.
  https://arxiv.org/abs/1904.03751
• Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks. Christopher Morris, Martin Ritzert, Matthias Fey, William L. Hamilton, Jan Eric Lenssen, Gaurav Rattan, Martin Grohe AAAI 2019.
  https://arxiv.org/pdf/1810.02244.pdf
• Deeper insights into graph convolutional networks for semi-supervised learning. Qimai Li, Zhichao Han, Xiao-Ming Wu. AAAI 2018.
  https://arxiv.org/pdf/180-07606.pdf

GAE

• Deep graph infomax. Petar Veličković, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, R Devon Hjelm. ICLR 2019.
  https://arxiv.org/abs/1809.10341

• Link Prediction Based on Graph Neural Networks. Muhan Zhang, Yixin Chen. NeurIPS 2018.
  https://arxiv.org/pdf/1802.0969-pdf

• SpectralNet: Spectral Clustering using Deep Neural Networks Uri Shaham, Kelly Stanton, Henry Li, Boaz Nadler, Ronen Basri, Yuval Kluger. ICLR 2018.
  https://arxiv.org/pdf/180-01587.pdf

• Deep Recursive Network Embedding with Regular Equivalence. Ke Tu, Peng Cui, Xiao Wang, Philip S. Yu, Wenwu Zhu. KDD 2018.
  http://cuip.thumedialab.com/papers/NE-RegularEquivalence.pdf

• Learning Deep Network Representations with Adversarially Regularized Autoencoders. Wenchao Yu, Cheng Zheng, Wei Cheng, Charu Aggarwal, Dongjin Song, Bo Zong, Haifeng Chen, Wei Wang. KDD 2018.
  http://www.cs.ucsb.edu/~bzong/doc/kdd-18.pdf

• Adversarially Regularized Graph Autoencoder for Graph Embedding. Shirui Pan, Ruiqi Hu, Guodong Long, Jing Jiang, Lina Yao, Chengqi Zhang. IJCAI 2018.
  https://www.ijcai.org/proceedings/2018/0362.pdf

• Mgae: Marginalized graph autoencoder for graph clustering Chun Wang, Shirui Pan, Guodong Long, Xingquan Zhu, Jing Jiang. CIKM 2017.
  https://shiruipan.github.io/pdf/CIKM-17-Wang.pdf

• Structural deep network embedding Daixin Wang, Peng Cui, Wenwu Zhu.
  https://www.kdd.org/kdd2016/papers/files/rfp0191-wangAemb.pdf

• Deep neural networks for learning graph representations. Shaosheng Cao, Wei Lu, Qiongkai Xu. AAAI 2016.
  https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/12423/11715

• Variational graph auto-encoders. Thomas N. Kipf, Max Welling. 2016.
  https://arxiv.org/pdf/161-07308.pdf

Spatial-Temporal Graph Neural Networks

• Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting. Shengnan Guo, Youfang Lin, Ning Feng, Chao Song, HuaiyuWan AAAI 2019.
  https://aaai.org/ojs/index.php/AAAI/article/view/3881

• Spatio-temporal Multi-Graph Convolution Network for Ride-hailing Demand Forecasting. Xu Geng, Yaguang Li, Leye Wang, Lingyu Zhang, Qiang Yang, Jieping Ye, Yan Liu. AAAI 2019.
  http://www-scf.usc.edu/~yaguang/papers/aaai19_multi_graph_convolution.pdf

• Spatio-Temporal Graph Routing for Skeleton-based Action Recognition. Bin Li, Xi Li, Zhongfei Zhang, Fei Wu. AAAI 2019.
  https://www.aaai.org/Papers/AAAI/2019/AAAI-LiBin.6992.pdf

• Graph wavenet for deep spatial-temporal graph modeling Z. Wu, S. Pan, G. Long, J. Jiang, and C. Zhang IJCAI 2019.
  https://arxiv.org/abs/1906.00121

• Deep multi-view spatial-temporal network for taxi. Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, Zhenhui Li. AAAI 2018.
  https://arxiv.org/abs/1802.08714

• Spatial temporal graph convolutional networks for skeleton-based action recognition. Sijie Yan, Yuanjun Xiong, Dahua Lin. AAAI 2018.
  https://arxiv.org/abs/180-07455

• Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. Yaguang Li, Rose Yu, Cyrus Shahabi, Yan Liu. ICLR 2018.
  https://arxiv.org/pdf/1707.01926.pdf

• Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. Bing Yu, Haoteng Yin, Zhanxing Zhu. IJCAI 2018.
  https://arxiv.org/pdf/1709.04875.pdf

• Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs. Rakshit Trivedi, Hanjun Dai, Yichen Wang, Le Song. ICML 2017
  https://arxiv.org/pdf/1705.05742.pdf

• Structured sequence modeling with graph convolutional recurrent networks. Youngjoo Seo, Michaël Defferrard, Pierre Vandergheynst, Xavier Bresson. 2016. https://arxiv.org/pdf/1612.07659.pdf

• Structural-rnn: Deep learning on spatio-temporal graphs. Ashesh Jain, Amir R. Zamir, Silvio Savarese, Ashutosh Saxena. CVPR 2016.
  https://arxiv.org/abs/151-05298

应用

Physics

• Graph Element Networks: adaptive, structured computation and memory. Ferran Alet, Adarsh K. Jeewajee, Maria Bauza, Alberto Rodriguez, Tomas Lozano-Perez, Leslie Pack Kaelbling. ICML 2019.
  https://arxiv.org/pdf/1904.09019

• Graph networks as learnable physics engines for inference and control. Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg, Josh Merel, Martin Riedmiller, Raia Hadsell, Peter Battaglia. ICML 2018.
  https://arxiv.org/pdf/1806.01242.pdf

• Discovering objects and their relations from entangled scene representations. David Raposo, Adam Santoro, David Barrett, Razvan Pascanu, Timothy Lillicrap, Peter Battaglia. ICLR Workshop 2017.
  https://arxiv.org/pdf/1702.05068.pdf

• A simple neural network module for relational reasoning. Adam Santoro, David Raposo, David G.T. Barrett, Mateusz Malinowski, Razvan Pascanu, Peter Battaglia, Timothy Lillicrap. NIPS 2017.
  https://arxiv.org/pdf/1706.01427.pdf

• Interaction Networks for Learning about Objects, Relations and Physics. Peter Battaglia, Razvan Pascanu, Matthew Lai, Danilo Rezende, Koray Kavukcuoglu. NIPS 2016.
  https://arxiv.org/pdf/1612.00222.pdf

• Visual Interaction Networks: Learning a Physics Simulator from Video. Nicholas Watters, Andrea Tacchetti, Théophane Weber, Razvan Pascanu, Peter Battaglia, Daniel Zoran. NIPS 2017.
  http://papers.nips.cc/paper/7040-visual-interaction-networks-learning-a-physics-simulator-from-video.pdf

• Learning Multiagent Communication with Backpropagation. Sainbayar Sukhbaatar, Arthur Szlam, Rob Fergus. NIPS 2016.
  https://arxiv.org/pdf/1605.07736.pdf

• VAIN: Attentional Multi-agent Predictive Modeling. Yedid Hoshen. NIPS 2017
  https://arxiv.org/pdf/1706.06122.pdf

• Neural Relational Inference for Interacting Systems. Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, Richard Zemel. ICML 2018.
  https://arxiv.org/pdf/1802.04687.pdf

Knowledge Graph

• Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks. Namyong Park, Andrey Kan, Xin Luna Dong, Tong Zhao, Christos Faloutsos. KDD 2019.
  https://arxiv.org/pdf/1905.08865

• OAG: Toward Linking Large-scale Heterogeneous Entity Graphs. Fanjin Zhang, Xiao Liu, Jie Tang, Yuxiao Dong, Peiran Yao, Jie Zhang, Xiaotao Gu, Yan Wang, Bin Shao, Rui Li, Kuansan Wang. KDD 2019.
  http://keg.cs.tsinghua.edu.cn/jietang/publications/KDD19-Zhang-et-al-Open_Academic_Graph.pdf

• End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completion. Chao Shang, Yun Tang, Jing Huang, Jinbo Bi, Xiaodong He, Bowen Zhou. AAAI 2019.
  https://arxiv.org/pdf/181-0444-pdf

• Logic Attention Based Neighborhood Aggregation for Inductive Knowledge Graph Embedding. Peifeng Wang, Jialong Han, Chenliang Li, Rong Pan. AAAI 2019.
  https://arxiv.org/pdf/181-01399.pdf

• Modeling Relational Data with Graph Convolutional Networks. Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, Max Welling. ESWC 2018.
  https://arxiv.org/pdf/1703.06103.pdf

• Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks. Zhichun Wang, Qingsong Lv, Xiaohan Lan, Yu Zhang. EMNLP 2018.
  http://www.aclweb.org/anthology/D18-1032

• Representation learning for visual-relational knowledge graphs. Daniel Oñoro-Rubio, Mathias Niepert, Alberto García-Durán, Roberto González, Roberto J. López-Sastre. arxiv 2017.
  https://arxiv.org/pdf/1709.02314.pdf

• Knowledge Transfer for Out-of-Knowledge-Base Entities : A Graph Neural Network Approach. Takuo Hamaguchi, Hidekazu Oiwa, Masashi Shimbo, Yuji Matsumoto. IJCAI 2017.
  https://arxiv.org/pdf/1706.05674.pdf

• Dynamic Graph Generation Network: Generating Relational Knowledge from Diagrams. Haoyu Wang, Defu Lian, Yong Ge. CVPR 2018.
  http://openaccess.thecvf.com/content_cvpr_2018/papers/Kim_Dynamic_Graph_Generation_CVPR_2018_paper.pdf

• Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs. Deepak Nathani, Jatin Chauhan, Charu Sharma, Manohar Kaul. ACL 2019.
  https://arxiv.org/pdf/1906.01195

• Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Network. Kun Xu, Mo Yu, Yansong Feng, Yan Song, Zhiguo Wang, Dong Yu. ACL 2019.
  https://128.84.2-199/pdf/1905.11605

Recommender Systems

• STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems. Jiani Zhang, Xingjian Shi, Shenglin Zhao, Irwin King. IJCAI 2019.
  https://arxiv.org/pdf/1905.13129.pdf

• Binarized Collaborative Filtering with Distilling Graph Convolutional Networks. Haoyu Wang, Defu Lian, Yong Ge. IJCAI 2019.
  https://arxiv.org/pdf/1906.01829.pdf

• Graph Contextualized Self-Attention Network for Session-based Recommendation. Chengfeng Xu, Pengpeng Zhao, Yanchi Liu, Victor S. Sheng, Jiajie Xu, Fuzhen Zhuang, Junhua Fang, Xiaofang Zhou. IJCAI 2019.
  https://www.ijcai.org/proceedings/2019/0547.pdf

• Session-based Recommendation with Graph Neural Networks. Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, Tieniu Tan. AAAI 2019.
  https://arxiv.org/pdf/181-00855.pdf

• Geometric Hawkes Processes with Graph Convolutional Recurrent Neural Networks. Jin Shang, Mingxuan Sun. AAAI 2019.
  https://jshang2.github.io/pubs/geo.pdf

• Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems. Hongwei Wang, Fuzheng Zhang, Mengdi Zhang, Jure Leskovec, Miao Zhao, Wenjie Li, Zhongyuan Wang. KDD 2019.
  https://arxiv.org/pdf/1905.04413

• Exact-K Recommendation via Maximal Clique Optimization. Yu Gong, Yu Zhu, Lu Duan, Qingwen Liu, Ziyu Guan, Fei Sun, Wenwu Ou, Kenny Q. Zhu. KDD 2019.
  https://arxiv.org/pdf/1905.07089

• KGAT: Knowledge Graph Attention Network for Recommendation. Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, Tat-Seng Chua. KDD 2019.
  https://arxiv.org/pdf/1905.07854

• Knowledge Graph Convolutional Networks for Recommender Systems. Hongwei Wang, Miao Zhao, Xing Xie, Wenjie Li, Minyi Guo. WWW 2019.
  https://arxiv.org/pdf/1904.12575.pdf

• Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems. Qitian Wu, Hengrui Zhang, Xiaofeng Gao, Peng He, Paul Weng, Han Gao, Guihai Chen. WWW 2019.
  https://arxiv.org/pdf/1903.10433.pdf

• Graph Neural Networks for Social Recommendation. Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, Dawei Yin. WWW 2019.
  https://arxiv.org/pdf/1902.07243.pdf

• Graph Convolutional Neural Networks for Web-Scale Recommender Systems. Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, Jure Leskovec. KDD 2018.
  https://arxiv.org/abs/1806.01973

• Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks. Federico Monti, Michael M. Bronstein, Xavier Bresson. NIPS 2017.
  https://arxiv.org/abs/1704.06803

• Graph Convolutional Matrix Completion. Rianne van den Berg, Thomas N. Kipf, Max Welling. 2017.
  https://arxiv.org/abs/1706.02263

Computer Vision

• Graph CNNs with Motif and Variable Temporal Block for Skeleton-based Action Recognition. Yu-Hui Wen, Lin Gao, Hongbo Fu, Fang-Lue Zhang, Shihong Xia. AAAI 2019.
  https://ecs.victoria.ac.nz/foswiki/pub/Groups/Graphics/RGB-DDataProcessingForRobotics/Graph%20CNNs%20with%20Motif%20and%20Variable%20Temporal%20Block%20for%20Skeleton-based%20Action%20Recognition.pdf

• Multi-Label Image Recognition with Graph Convolutional Networks. Zhao-Min Chen, Xiu-Shen Wei, Peng Wang, Yanwen Guo. CVPR 2019.
  https://arxiv.org/pdf/1904.03582.pdf

• GCAN: Graph Convolutional Adversarial Network for Unsupervised Domain Adaptation. Xinhong Ma, Tianzhu Zhang, Changsheng Xu. CVPR 2019.
  http://openaccess.thecvf.com/content_CVPR_2019/papers/Ma_GCAN_Graph_Convolutional_Adversarial_Network_for_Unsupervised_Domain_Adaptation_CVPR_2019_paper.pdf

• Mind Your Neighbours: Image Annotation With Metadata Neighbourhood Graph Co-Attention Networks. Junjie Zhang, Qi Wu, Jian Zhang, Chunhua Shen, Jianfeng Lu. CVPR 2019.
  http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhang_Mind_Your_Neighbours_Image_Annotation_With_Metadata_Neighbourhood_Graph_Co-Attention_CVPR_2019_paper.pdf

• Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detection. Jia-Xing Zhong, Nannan Li, Weijie Kong, Shan Liu, Thomas H. Li, Ge Li. CVPR 2019.
  https://arxiv.org/pdf/1903.07256.pdf

• Neighbourhood Watch: Referring Expression Comprehension via Language-guided Graph Attention Networks. Peng Wang, Qi Wu, Jiewei Cao, Chunhua Shen, Lianli Gao, Anton van den Hengel. CVPR 2019.
  https://arxiv.org/pdf/1812.04794.pdf

• Linkage Based Face Clustering via Graph Convolution Network. Zhongdao Wang, Liang Zheng, Yali Li, Shengjin Wang. CVPR 2019.
  https://arxiv.org/pdf/1903.11306.pdf

• Fast Interactive Object Annotation with Curve-GCN. Huan Ling, Jun Gao, Amlan Kar, Wenzheng Chen, Sanja Fidler. CVPR 2019.
  https://arxiv.org/pdf/1903.06874.pdf

• Semantic Graph Convolutional Networks for 3D Human Pose Regression. Long Zhao, Xi Peng, Yu Tian, Mubbasir Kapadia, Dimitris N. Metaxas. CVPR 2019.
  https://arxiv.org/pdf/1904.03345.pdf

• Occlusion-Net: 2D/3D Occluded Keypoint Localization Using Graph Networks. N. Dinesh Reddy, Minh Vo, Srinivasa G. Narasimhan. CVPR 2019.
  http://www.cs.cmu.edu/~mvo/index_files/Papers/ONet_19.pdf

• Graph Attention Convolution for Point Cloud Semantic Segmentation. Lei Wang, Yuchun Huang, Yaolin Hou, Shenman Zhang, Jie Shan. CVPR 2019.
  http://openaccess.thecvf.com/content_CVPR_2019/papers/Wang_Graph_Attention_Convolution_for_Point_Cloud_Semantic_Segmentation_CVPR_2019_paper.pdf

• An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition. Chenyang Si, Wentao Chen, Wei Wang, Liang Wang, Tieniu Tan. CVPR 2019.
  https://arxiv.org/pdf/1902.09130.pdf

• Actional-Structural Graph Convolutional Networks for Skeleton-based Action Recognition. Maosen Li, Siheng Chen, Xu Chen, Ya Zhang, Yanfeng Wang, Qi Tian. CVPR 2019.
  https://arxiv.org/pdf/1904.12659.pdf

• Graph Convolutional Tracking. CVPR 2019. Junyu Gao, Tianzhu Zhang, Changsheng Xu.
  http://nlpr-web.ia.ac.cn/mmc/homepage/jygao/JY_Gao_files/Conference_Papers/GCT-CVPR2019-GJY.pdf

• Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition. Lei Shi, Yifan Zhang, Jian Cheng, Hanqing Lu. CVPR 2019.
  http://openaccess.thecvf.com/content_CVPR_2019/papers/Shi_Two-Stream_Adaptive_Graph_Convolutional_Networks_for_Skeleton-Based_Action_Recognition_CVPR_2019_paper.pdf

• Skeleton-Based Action Recognition With Directed Graph Neural Networks. Lei Shi, Yifan Zhang, Jian Cheng, Hanqing Lu. CVPR 2019.
  http://openaccess.thecvf.com/content_CVPR_2019/papers/Shi_Skeleton-Based_Action_Recognition_With_Directed_Graph_Neural_Networks_CVPR_2019_paper.pdf

• Graph Convolutional Gaussian Processes. Ian Walker, Ben Glocker. ICML 2019.
  https://arxiv.org/pdf/1905.05739

• Relation Networks for Object Detection. Han Hu, Jiayuan Gu, Zheng Zhang, Jifeng Dai, Yichen Wei. CVPR 2018.
  http://openaccess.thecvf.com/content_cvpr_2018/papers_backup/Hu_Relation_Networks_for_CVPR_2018_paper.pdf

• Learning Region features for Object Detection. Jiayuan Gu, Han Hu, Liwei Wang, Yichen Wei, Jifeng Dai. ECCV 2018.
  https://arxiv.org/pdf/1803.07066

• The More You Know: Using Knowledge Graphs for Image Classification. Kenneth Marino, Ruslan Salakhutdinov, Abhinav Gupta. CVPR 2017.
  https://arxiv.org/pdf/1612.04844.pdf

• Graph Neural Networks for Object Localization. Gabriele Monfardini, Vincenzo Di Massa, Franco Scarselli, Marco Gori. ECAI 2006.
  http://ebooks.iospress.nl/volumearticle/2775

• Learning Human-Object Interactions by Graph Parsing Neural Networks. Siyuan Qi, Wenguan Wang, Baoxiong Jia, Jianbing Shen, Song-Chun Zhu. ECCV 2018.
  https://arxiv.org/pdf/1808.07962.pdf

• Learning Conditioned Graph Structures for Interpretable Visual Question Answering. Will Norcliffe-Brown, Efstathios Vafeias, Sarah Parisot. NeurIPS 2018.
  https://arxiv.org/pdf/1806.07243

• Symbolic Graph Reasoning Meets Convolutions. Xiaodan Liang, Zhiting Hu, Hao Zhang, Liang Lin, Eric P. Xing. NeurIPS 2018.
  http://papers.nips.cc/paper/7456-symbolic-graph-reasoning-meets-convolutions.pdf

• Out of the Box: Reasoning with Graph Convolution Nets for Factual Visual Question Answering. Medhini Narasimhan, Svetlana Lazebnik, Alexander Schwing. NeurIPS 2018.
  http://papers.nips.cc/paper/7531-out-of-the-box-reasoning-with-graph-convolution-nets-for-factual-visual-question-answering.pdf

• Structural-RNN: Deep Learning on Spatio-Temporal Graphs. Ashesh Jain, Amir R. Zamir, Silvio Savarese, Ashutosh Saxena. CVPR 2016.
  https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Jain_Structural-RNN_Deep_Learning_CVPR_2016_paper.pdf

• Understanding Kin Relationships in a Photo. Siyu Xia, Ming Shao, Jiebo Luo, Yun Fu. TMM 2012.
  https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6151163

• Graph-Structured Representations for Visual Question Answering. Damien Teney, Lingqiao Liu, Anton van den Hengel. CVPR 2017.
  https://arxiv.org/pdf/1609.05600.pdf

• Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition. Sijie Yan, Yuanjun Xiong, Dahua Lin. AAAI 2018.
  https://arxiv.org/pdf/180-07455.pdf

• Dynamic Graph CNN for Learning on Point Clouds. Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E. Sarma, Michael M. Bronstein, Justin M. Solomon. CVPR 2018.
  https://arxiv.org/pdf/180-07829.pdf

• PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. Charles R. Qi, Hao Su, Kaichun Mo, Leonidas J. Guibas. CVPR 2018.
  https://arxiv.org/pdf/1612.00593.pdf

• 3D Graph Neural Networks for RGBD Semantic Segmentation. Xiaojuan Qi, Renjie Liao, Jiaya Jia, Sanja Fidler, Raquel Urtasun. CVPR 2017.
  http://openaccess.thecvf.com/content_ICCV_2017/papers/Qi_3D_Graph_Neural_ICCV_2017_paper.pdf

• Iterative Visual Reasoning Beyond Convolutions. Xinlei Chen, Li-Jia Li, Li Fei-Fei, Abhinav Gupta. CVPR 2018.
  https://arxiv.org/pdf/1803.11189

• Situation Recognition with Graph Neural Networks. Ruiyu Li, Makarand Tapaswi, Renjie Liao, Jiaya Jia, Raquel Urtasun, Sanja Fidler. ICCV 2017.
  https://arxiv.org/pdf/1708.04320

• I Know the Relationships: Zero-Shot Action Recognition via Two-Stream Graph Convolutional Networks and Knowledge Graphs. Junyu Gao, Tianzhu Zhang, Changsheng Xu. AAAI 2019.
  http://nlpr-web.ia.ac.cn/mmc/homepage/jygao/JY_Gao_files/Conference_Papers/AAAI2019-GJY.pdf

Natural Language Processing

• Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks. Shikhar Vashishth, Manik Bhandari, Prateek Yadav, Piyush Rai, Chiranjib Bhattacharyya, Partha Talukdar ACL 2019.
  https://arxiv.org/pdf/1809.04283

• Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network. Sunil Kumar Sahu, Fenia Christopoulou, Makoto Miwa, Sophia Ananiadou. ACL 2019.
  https://arxiv.org/pdf/1906.04684

• Textbook Question Answering with Multi-modal Context Graph Understanding and Self-supervised Open-set Comprehension. Daesik Kim, Seonhoon Kim, Nojun Kwak. ACL 2019.
  https://arxiv.org/pdf/181-00232

• Dynamically Fused Graph Network for Multi-hop Reasoning. Yunxuan Xiao, Yanru Qu, Lin Qiu, Hao Zhou, Lei Li, Weinan Zhang, Yong Yu. ACL 2019.
  https://arxiv.org/pdf/1905.06933

• Joint Type Inference on Entities and Relations via Graph Convolutional Networks. Changzhi Sun, Yeyun Gong, Yuanbin Wu, Ming Gong, Daxing Jiang, Man Lan, Shiliang Sun1, Nan Duan. ACL 2019.
  http://www.czsun.site/publications/joint_entrel_gcn.pdf

• Attention Guided Graph Convolutional Networks for Relation Extraction. Zhijiang Guo, Yan Zhang, Wei Lu. ACL 2019.
  http://www.statnlp.org/wp-content/uploads/2019/06/Attention_Guided_Graph_Convolutional_Networks_for_Relation_Extraction.pdf

• GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction. Tsu-Jui Fu, Peng-Hsuan Li, Wei-Yun Ma. ACL 2019.
  https://tsujuifu.github.io/pubs/acl19_graph-rel.pdf

• Graph Neural Networks with Generated Parameters for Relation Extraction. Hao Zhu, Yankai Lin, Zhiyuan Liu, Jie Fu, Tat-seng Chua, Maosong Sun. ACL 2019.
  https://arxiv.org/pdf/1902.00756

• Generating Logical Forms from Graph Representations of Text and Entities. Peter Shaw, Philip Massey, Angelica Chen, Francesco Piccinno, Yasemin Altun. ACL 2019.
  https://arxiv.org/pdf/1905.08407

• Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing. Ben Bogin, Matt Gardner, Jonathan Berant. ACL 2019.
  https://arxiv.org/pdf/1905.06241

• Coherent Comment Generation for Chinese Articles with a Graph-to-Sequence Model. Wei Li, Jingjing Xu, Yancheng He, Shengli Yan, Yunfang Wu, Xu sun. ACL 2019.
  https://arxiv.org/pdf/1906.01231

• GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verification. Jie Zhou, Xu Han, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, Maosong Sun. ACL 2019.
  https://www.aclweb.org/anthology/P19-1085

• Look Again at the Syntax: Relational Graph Convolutional Network for Gendered Ambiguous Pronoun Resolution. Yinchuan Xu, Junlin Yang. ACL 2019.
  https://arxiv.org/pdf/1905.08868.pdf

• Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks. Ningyu Zhang, Shumin Deng, Zhanlin Sun, Guanying Wang, Xi Chen, Wei Zhang, Huajun Chen. NAACL 2019.
  https://arxiv.org/pdf/1903.01306.pdf

• Text Generation from Knowledge Graphs with Graph Transformers. Rik Koncel-Kedziorski, Dhanush Bekal, Yi Luan, Mirella Lapata, Hannaneh Hajishirzi. NAACL 2019.
  https://arxiv.org/pdf/1904.02342.pdf

• Question Answering by Reasoning Across Documents with Graph Convolutional Networks. Nicola De Cao, Wilker Aziz, Ivan Titov. NAACL 2019.
  https://arxiv.org/pdf/1808.09920.pdf

• BAG: Bi-directional Attention Entity Graph Convolutional Network for Multi-hop Reasoning Question Answering. Yu Cao, Meng Fang, Dacheng Tao. NAACL 2019.
  https://arxiv.org/pdf/1904.04969.pdf

• GraphIE: A Graph-Based Framework for Information Extraction. Yujie Qian, Enrico Santus, Zhijing Jin, Jiang Guo, Regina Barzilay. NAACL 2019.
  https://arxiv.org/pdf/1810.13083.pdf

• Graph Convolution for Multimodal Information Extraction from Visually Rich Documents. Xiaojing Liu, Feiyu Gao, Qiong Zhang, Huasha Zhao. NAACL 2019.
  https://arxiv.org/pdf/1903.11279.pdf

• Abusive Language Detection with Graph Convolutional Networks. Pushkar Mishra, Marco Del Tredici, Helen Yannakoudakis, Ekaterina Shutova. NAACL 2019.
  https://arxiv.org/pdf/1904.04073.pdf

• Learning Graph Pooling and Hybrid Convolutional Operations for Text Representations. Hongyang Gao, Yongjun Chen, Shuiwang Ji. WWW 2019.
  https://arxiv.org/pdf/190-06965.pdf

• Graph Convolutional Networks with Argument-Aware Pooling for Event Detection. Thien Huu Nguyen, Ralph Grishman. AAAI 2018.
  http://ix.cs.uoregon.edu/~thien/pubs/graphConv.pdf

• Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks. Diego Marcheggiani, Joost Bastings, Ivan Titov. NAACL 2018.
  http://www.aclweb.org/anthology/N18-2078

• Exploring Graph-structured Passage Representation for Multi-hop Reading Comprehension with Graph Neural Networks. Linfeng Song, Zhiguo Wang, Mo Yu, Yue Zhang, Radu Florian, Daniel Gildea. 2018.
  https://arxiv.org/abs/1809.02040

• Graph Convolution over Pruned Dependency Trees Improves Relation Extraction. Yuhao Zhang, Peng Qi, Christopher D. Manning. EMNLP 2018.
  https://arxiv.org/abs/1809.10185

• N-ary relation extraction using graph state LSTM. Linfeng Song, Yue Zhang, Zhiguo Wang, Daniel Gildea. EMNLP 18.
  https://arxiv.org/abs/1808.09101

• A Graph-to-Sequence Model for AMR-to-Text Generation. Linfeng Song, Yue Zhang, Zhiguo Wang, Daniel Gildea. ACL 2018.
  https://arxiv.org/abs/1805.02473

• Graph-to-Sequence Learning using Gated Graph Neural Networks. Daniel Beck, Gholamreza Haffari, Trevor Cohn. ACL 2018.
  https://arxiv.org/pdf/1806.09835.pdf

• Multiple Events Extraction via Attention-based Graph Information Aggregation. Xiao Liu, Zhunchen Luo, Heyan Huang. EMNLP 2018.
  https://arxiv.org/pdf/1809.09078.pdf

• Recurrent Relational Networks. Rasmus Palm, Ulrich Paquet, Ole Winther. NeurIPS 2018.
  http://papers.nips.cc/paper/7597-recurrent-relational-networks.pdf

• Learning Graphical State Transitions. Daniel D. Johnson. ICLR 2017.
  https://openreview.net/forum?id=HJ0NvFzxl

• Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks. Kai Sheng Tai, Richard Socher, Christopher D. Manning. ACL 2015.
  https://www.aclweb.org/anthology/P15-1150

• Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling. Diego Marcheggiani, Ivan Titov. EMNLP 2017.
  https://arxiv.org/abs/1703.04826

• Cross-Sentence N-ary Relation Extraction with Graph LSTMs. Nanyun Peng, Hoifung Poon, Chris Quirk, Kristina Toutanova, Wen-tau Yih. TACL.
  https://arxiv.org/abs/1708.03743

• Graph Convolutional Encoders for Syntax-aware Neural Machine Translation. Joost Bastings, Ivan Titov, Wilker Aziz, Diego Marcheggiani, Khalil Sima'an. EMNLP 2017.
  https://arxiv.org/pdf/1704.04675

• Semi-supervised User Geolocation via Graph Convolutional Networks. Afshin Rahimi, Trevor Cohn, Timothy Baldwin. ACL 2018.
  https://arxiv.org/pdf/1804.08049.pdf

• Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering. Daniil Sorokin, Iryna Gurevych. COLING 2018.
  https://arxiv.org/pdf/1808.04126.pdf

• Graph Convolutional Networks for Text Classification. Liang Yao, Chengsheng Mao, Yuan Luo. AAAI 2019.
  https://arxiv.org/pdf/1809.05679.pdf

• Constructing Narrative Event Evolutionary Graph for Script Event Prediction. Zhongyang Li, Xiao Ding, Ting Liu. IJCAI 2018.
  https://arxiv.org/pdf/1805.0508-pdf

Others

• Recurrent Relational Networks. Rasmus Palm, Ulrich Paquet, Ole Winther. NeurIPS 2018.
  http://papers.nips.cc/paper/7597-recurrent-relational-networks.pdf
• Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search. Zhuwen Li, Qifeng Chen, Vladlen Koltun. NeurIPS 2018.
  http://papers.nips.cc/paper/7335-combinatorial-optimization-with-graph-convolutional-networks-and-guided-tree-search.pdf
• Learning to represent programs with graphs. Miltiadis Allamanis, Marc Brockschmidt, Mahmoud Khademi. ICLR 2017.
  https://arxiv.org/pdf/171-00740.pdf

Tutorial

• tutorial on Stanford Network Analysis Project(SNAP)
  http://snap.stanford.edu/
• Deep Learning for Network Biology
  http://snap.stanford.edu/deepnetbio-ismb/
• Representation Learning on Networks
  http://snap.stanford.edu/proj/embeddings-www/
• How to do Deep Learning on Graphs with Graph Convolutional Networks
  https://towardsdatascience.com/how-to-do-deep-learning-on-graphs-with-graph-convolutional-networks-7d2250723780
• Graph Convolutional Networks
  https://tkipf.github.io/graph-convolutional-networks/
• Graph Convolutional Networks tutorial
  https://github.com/dbusbridge/gcn_tutorial

视频教程

• Graph Convolutional Networks for Node Classification
  https://www.youtube.com/watch?v=LFSR27BaNIQ
• Jure Leskovec - Large-scale Graph Representation Learning
  https://www.youtube.com/watch?v=oQL4E1gK3VU
• Attention in Neural Networks
  https://www.youtube.com/watch?v=W2rWgXJBZhU
• Michael Bronstein - Geometric deep learning on graphs: going beyond Euclidean data
  https://www.youtube.com/watch?v=b187J4ndZWY
• Yann LeCun - Graph Embedding, Content Understanding, and Self-Supervised Learning
  https://www.youtube.com/watch?v=UGPT64wo7lU
• DeepWalk: Turning Graphs Into Features via Network Embeddings with Neo4j
  https://www.youtube.com/watch?v=aZNtHJwfIVg
• What is a Random Walk?
  https://www.youtube.com/watch?v=stgYW6M5o4k

代码

• Graph Nets in TensorFlow by DeepMind
  https://github.com/deepmind/graph_nets
• Colab Notebook For Graph Nets and Item Connections / Recommendations
  https://colab.research.google.com/github/deepmind/graph_nets/blob/master/graph_nets/demos/sort.ipynb
• Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
  https://github.com/mdeff/cnn_graph
• Semi-Supervised Classification with Graph Convolutional Networks
  https://github.com/tkipf/gcn
• GraphSAGE
  https://github.com/williamleif/GraphSAGE
• Large-Scale Learnable Graph Convolutional Networks
  https://github.com/divelab/lgcn/
• RippleNet
  https://github.com/hwwang55/RippleNet
• MKR (multi-task learning for knowledge graph enhanced recommendation)
  https://github.com/hwwang55/MKR
• Graph Attention Networks
  https://github.com/PetarV-/GAT
• Variational Graph Auto-Encoder
  https://github.com/limaosen0/Variational-Graph-Auto-Encoders
• Adversarially Regularized Graph Autoencoder
  https://github.com/Ruiqi-Hu/ARGA
• Deep Recursive Network Embedding with Regular Equivalence
  https://github.com/tadpole/DRNE
• DeepWalk
  https://github.com/triandicAnt/GraphEmbeddingRecommendationSystem
• GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Model
  https://github.com/snap-stanford/GraphRNN
• Diffusion Convolutional Recurrent Neural Network
  https://github.com/liyaguang/DCRNN
• Spatio-Temporal Graph Convolutional Networks
  https://github.com/VeritasYin/STGCN_IJCAI-18
• pytorch geometric
  https://github.com/rusty1s/pytorch_geometric
• deep graph library
  https://github.com/dmlc/dgl
• graph nets library
  https://github.com/deepmind/graph_nets

领域专家

• THOMAS KIPF
  https://tkipf.github.io/
• Petar Veličković
  https://www.cl.cam.ac.uk/~pv273/
• Michaël Defferrard
  https://deff.ch/
• William L. Hamilton
  https://williamleif.github.io/