工具： https://github.com/dmlc/dgl

快速了解GCN（图卷积神经网络）

2018年11月27日 14:40:48 算法学习者 阅读数：786

如何理解 Graph Convolutional Network（GCN）？
https://www.zhihu.com/question/54504471
推荐初学者可以先从知乎的这个问题出发，点赞最多的《从CNN到GCN的联系与区别——GCN从入门到精（fang）通（qi）》
该篇文章非常详细且能够帮助初学者理解的讲述了GCN的大部分理论过程。再补充以后面几人回答的知识，便可以说对GCN有了基本
的理论支撑了。

主要论文：SEMI-SUPERVISED CLASSIFICATION WITH GRAPH CONVOLUTIONAL NETWORKS
https://arxiv.org/pdf/1609.02907.pdf

Graph Convolutional Networks
https://tkipf.github.io/graph-convolutional-networks/
下面大多文章都是对该文的一个翻译加自己理解内容。

上篇英文版的中文版：深度学习新星 | 图卷积神经网络（GCN）有多强大？ 非常有助于理解 推荐阅读
https://www.sohu.com/a/234894712_741733
本文 GCN 项目仓库：https://github.com/tkipf/gcn

图卷积网络（Graph Convolutional Network）
https://blog.csdn.net/chensi1995/article/details/77232019

该篇文章主要介绍图卷积网络的卷及方式的理论推导过程

卷积神经网络不能处理“图”结构数据？这篇文章告诉你答案
https://www.leiphone.com/news/201706/ppA1Hr0M0fLqm7OP.html
该篇wen章比较浅显的介绍了如何处理图结构的卷积神经网络，可帮助理解

谱聚类（spectral clustering）原理总结
https://www.cnblogs.com/pinard/p/6221564.html
推荐看看，关于图处理和运算的理解很有帮助，还有拉普拉斯矩阵

Googlenet： TensorFlow实战：Chapter-5（CNN-3-经典卷积神经网络（GoogleNet）)
https://blog.csdn.net/u011974639/article/details/76460849#inception-v2

浅析图卷积神经网络 浅析有助于理解
https://mp.weixin.qq.com/s/356WvVn1Tz0axsKd8LJW4Q
《Graph Learning》专栏大纲
第一章 图及其应用场景
第二章 图的传播算法
第三章 社群检测以及高密子图
第四章 异构信息网络
第五章 图表示学习
第六章 图卷积神经网络

学界 | 港中文AAAI录用论文详解：ST-GCN时空图卷积网络模型
http://www.zuixu.com/dz/a/7080.html
最后可以看看 这篇文章 ST-GCN，也是论文翻译成果，英文很棒的可以直接看下面的文章
文章链接：
https://arxiv.org/abs/1801.07455
Github 代码：
https://github.com/yysijie/st-gcn

作者：ticktick3
来源：CSDN
原文：https://blog.csdn.net/u011537121/article/details/81542991
版权声明：本文为博主原创文章，转载请附上博文链接！

</article>

清华must read papers: https://github.com/HollyMeng/GNNPapers

Must-read papers on GNN

GNN: graph neural network

Contributed by Jie Zhou, Ganqu Cui and Zhengyan Zhang.

Survey papers

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

  2. A Comprehensive Survey on Graph Neural Networks. Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, Philip S. Yu. 2019. paper

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

  4. Relational Inductive Biases, Deep Learning, and Graph Networks. Battaglia, Peter W and Hamrick, Jessica B and Bapst, Victor and Sanchez-Gonzalez, Alvaro and Zambaldi, Vinicius and Malinowski, Mateusz and Tacchetti, Andrea and Raposo, David and Santoro, Adam and Faulkner, Ryan and others. 2018. paper

  5. Geometric Deep Learning: Going beyond Euclidean data. Bronstein, Michael M and Bruna, Joan and LeCun, Yann and Szlam, Arthur and Vandergheynst, Pierre. IEEE SPM 2017. paper

  6. Computational Capabilities of Graph Neural Networks. Scarselli, Franco and Gori, Marco and Tsoi, Ah Chung and Hagenbuchner, Markus and Monfardini, Gabriele. IEEE TNN 2009. paper

  7. Neural Message Passing for Quantum Chemistry. Gilmer, Justin and Schoenholz, Samuel S and Riley, Patrick F and Vinyals, Oriol and Dahl, George E. 2017. paper

  8. Non-local Neural Networks. Wang, Xiaolong and Girshick, Ross and Gupta, Abhinav and He, Kaiming. CVPR 2018. paper

  9. The Graph Neural Network Model. Scarselli, Franco and Gori, Marco and Tsoi, Ah Chung and Hagenbuchner, Markus and Monfardini, Gabriele. IEEE TNN 2009. paper

Models

  1. A new model for learning in graph domains. Marco Gori, Gabriele Monfardini, Franco Scarselli. IJCNN 2005. paper

  2. Graph Neural Networks for Ranking Web Pages. Franco Scarselli, Sweah Liang Yong, Marco Gori, Markus Hagenbuchner, Ah Chung Tsoi, Marco Maggini. WI 2005. paper

  3. Gated Graph Sequence Neural Networks. Yujia Li, Daniel Tarlow, Marc Brockschmidt, Richard Zemel. ICLR 2016. paper

  4. 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. paper

  5. Spectral Networks and Locally Connected Networks on Graphs. Joan Bruna, Wojciech Zaremba, Arthur Szlam, Yann LeCun. ICLR 2014. paper

  6. Deep Convolutional Networks on Graph-Structured Data. Mikael Henaff, Joan Bruna, Yann LeCun. 2015. paper

  7. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. Michaël Defferrard, Xavier Bresson, Pierre Vandergheynst. NIPS 2016. paper

  8. Learning Convolutional Neural Networks for Graphs. Mathias Niepert, Mohamed Ahmed, Konstantin Kutzkov. ICML 2016. paper

  9. Semi-Supervised Classification with Graph Convolutional Networks. Thomas N. Kipf, Max Welling. ICLR 2017. paper

  10. Graph Attention Networks. Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, Yoshua Bengio. ICLR 2018. paper

  11. Deep Sets. Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Ruslan Salakhutdinov, Alexander Smola.NIPS 2017. paper

  12. Graph Partition Neural Networks for Semi-Supervised Classification. Renjie Liao, Marc Brockschmidt, Daniel Tarlow, Alexander L. Gaunt, Raquel Urtasun, Richard Zemel. 2018. paper

  13. Covariant Compositional Networks For Learning Graphs. Risi Kondor, Hy Truong Son, Horace Pan, Brandon Anderson, Shubhendu Trivedi. 2018. paper

  14. Modeling Relational Data with Graph Convolutional Networks. Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, Max Welling. ESWC 2018. paper

  15. Stochastic Training of Graph Convolutional Networks with Variance Reduction. Jianfei Chen, Jun Zhu, Le Song. ICML 2018. paper

  16. Learning Steady-States of Iterative Algorithms over Graphs. Hanjun Dai, Zornitsa Kozareva, Bo Dai, Alex Smola, Le Song.ICML 2018. paper

  17. Deriving Neural Architectures from Sequence and Graph Kernels. Tao Lei, Wengong Jin, Regina Barzilay, Tommi Jaakkola.ICML 2017. paper

  18. Adaptive Graph Convolutional Neural Networks. Ruoyu Li, Sheng Wang, Feiyun Zhu, Junzhou Huang. AAAI 2018. paper

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

  20. Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning. Qimai Li, Zhichao Han, Xiao-Ming Wu. AAAI 2018. paper

  21. Graphical-Based Learning Environments for Pattern Recognition. Franco Scarselli, Ah Chung Tsoi, Marco Gori, Markus Hagenbuchner. SSPR/SPR 2004. paper

  22. A Comparison between Recursive Neural Networks and Graph Neural Networks. Vincenzo Di Massa, Gabriele Monfardini, Lorenzo Sarti, Franco Scarselli, Marco Maggini, Marco Gori.IJCNN 2006. paper

  23. Graph Neural Networks for Object Localization. Gabriele Monfardini, Vincenzo Di Massa, Franco Scarselli, Marco Gori. ECAI 2006. paper

  24. Knowledge-Guided Recurrent Neural Network Learning for Task-Oriented Action Prediction. Liang Lin, Lili Huang, Tianshui Chen, Yukang Gan, Hui Cheng. ICME 2017. paper

  25. Semantic Object Parsing with Graph LSTM. Xiaodan LiangXiaohui ShenJiashi FengLiang Lin, Shuicheng Yan. ECCV 2016. paper

  26. CelebrityNet: A Social Network Constructed from Large-Scale Online Celebrity Images. Li-Jia Li, David A. Shamma, Xiangnan Kong, Sina Jafarpour, Roelof Van Zwol, Xuanhui Wang.TOMM 2015. paper

  27. Inductive Representation Learning on Large Graphs. William L. Hamilton, Rex Ying, Jure Leskovec. NIPS 2017. paper

  28. Graph Classification using Structural Attention. John Boaz Lee, Ryan Rossi, Xiangnan Kong.KDD 18. paper

  29. Adversarial Attacks on Neural Networks for Graph Data. Daniel Zügner, Amir Akbarnejad, Stephan Günnemann. KDD 18. paper

  30. Large-Scale Learnable Graph Convolutional Networks. Hongyang Gao, Zhengyang Wang, Shuiwang Ji. KDD 18. paper

  31. Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing.Davide Bacciu, Federico Errica, Alessio Micheli. ICML 2018. paper

  32. Diffusion-Convolutional Neural Networks. James Atwood, Don Towsley. NIPS 2016. paper

  33. Neural networks for relational learning: an experimental comparison. Werner Uwents, Gabriele Monfardini, Hendrik Blockeel, Marco Gori, Franco Scarselli. Machine Learning 2011. paper

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

  35. Adaptive Sampling Towards Fast Graph Representation Learning. Wenbing Huang, Tong Zhang, Yu Rong, Junzhou Huang.NeurIPS 2018. paper

  36. Structure-Aware Convolutional Neural Networks. Jianlong Chang, Jie Gu, Lingfeng Wang, Gaofeng Meng, Shiming Xiang, Chunhong Pan. NeurIPS 2018. paper

  37. Bayesian Semi-supervised Learning with Graph Gaussian Processes. Yin Cheng Ng, Nicolò Colombo, Ricardo Silva.NeurIPS 2018. paper

  38. Mean-field theory of graph neural networks in graph partitioning. Tatsuro Kawamoto, Masashi Tsubaki, Tomoyuki Obuchi. NeurIPS 2018. paper

  39. Hierarchical Graph Representation Learning with Differentiable Pooling. Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, Will Hamilton, Jure Leskovec. NeurIPS 2018. paper

  40. How Powerful are Graph Neural Networks? Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka. ICLR 2019. paper

  41. Graph Capsule Convolutional Neural Networks. Saurabh Verma, Zhi-Li Zhang. ICML 2018 Workshop. paper

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

Applications

  1. Discovering objects and their relations from entangled scene representations. David Raposo, Adam Santoro, David Barrett, Razvan Pascanu, Timothy Lillicrap, Peter Battaglia. ICLR Workshop 2017. paper

  2. 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. paper

  3. Attend, Infer, Repeat: Fast Scene Understanding with Generative Models. S. M. Ali Eslami, Nicolas Heess, Theophane Weber, Yuval Tassa, David Szepesvari, Koray Kavukcuoglu, Geoffrey E. Hinton. NIPS 2016. paper

  4. Beyond Categories: The Visual Memex Model for Reasoning About Object Relationships.Tomasz Malisiewicz, Alyosha Efros. NIPS 2009. paper

  5. Understanding Kin Relationships in a Photo. Siyu Xia, Ming Shao, Jiebo Luo, Yun Fu. TMM 2012. paper

  6. Graph-Structured Representations for Visual Question Answering. Damien Teney, Lingqiao Liu, Anton van den Hengel.CVPR 2017. paper

  7. Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition.Sijie Yan, Yuanjun Xiong, Dahua Lin. AAAI 2018. paper

  8. Few-Shot Learning with Graph Neural Networks. Victor Garcia, Joan Bruna. ICLR 2018. paper

  9. The More You Know: Using Knowledge Graphs for Image Classification. Kenneth Marino, Ruslan Salakhutdinov, Abhinav Gupta. CVPR 2017. paper

  10. Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs. Xiaolong Wang, Yufei Ye, Abhinav Gupta. CVPR 2018. paper

  11. Rethinking Knowledge Graph Propagation for Zero-Shot Learning. Michael Kampffmeyer, Yinbo Chen, Xiaodan Liang, Hao Wang, Yujia Zhang, Eric P. Xing. 2018. paper

  12. Interaction Networks for Learning about Objects, Relations and Physics. Peter Battaglia, Razvan Pascanu, Matthew Lai, Danilo Rezende, Koray Kavukcuoglu. NIPS 2016. paper

  13. A Compositional Object-Based Approach to Learning Physical Dynamics. Michael B. Chang, Tomer Ullman, Antonio Torralba, Joshua B. Tenenbaum. ICLR 2017. paper

  14. Visual Interaction Networks: Learning a Physics Simulator from Vide.o Nicholas Watters, Andrea Tacchetti, Théophane Weber, Razvan Pascanu, Peter Battaglia, Daniel Zoran. NIPS 2017. paper

  15. Relational neural expectation maximization: Unsupervised discovery of objects and their interactions. Sjoerd van Steenkiste, Michael Chang, Klaus Greff, Jürgen Schmidhuber. ICLR 2018. paper

  16. 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. paper

  17. Learning Multiagent Communication with Backpropagation. Sainbayar Sukhbaatar, Arthur Szlam, Rob Fergus. NIPS 2016. paper

  18. VAIN: Attentional Multi-agent Predictive Modeling. Yedid Hoshen. NIPS 2017 paper

  19. Neural Relational Inference for Interacting Systems. Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, Richard Zemel. ICML 2018. paper

  20. Translating Embeddings for Modeling Multi-relational Data. Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, Oksana Yakhnenko. NIPS 2013. paper

  21. Representation learning for visual-relational knowledge graphs. Daniel Oñoro-Rubio, Mathias Niepert, Alberto García-Durán, Roberto González, Roberto J. López-Sastre. 2017. paper

  22. Knowledge Transfer for Out-of-Knowledge-Base Entities : A Graph Neural Network Approach. Takuo Hamaguchi, Hidekazu Oiwa, Masashi Shimbo, Yuji Matsumoto. IJCAI 2017. paper

  23. Representation Learning on Graphs with Jumping Knowledge Networks. Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, Stefanie Jegelka.ICML 2018. paper

  24. Multi-Label Zero-Shot Learning with Structured Knowledge Graphs. Chung-Wei Lee, Wei Fang, Chih-Kuan Yeh, Yu-Chiang Frank Wang. CVPR 2018. paper

  25. Dynamic Graph Generation Network: Generating Relational Knowledge from Diagrams.Daesik Kim, Youngjoon Yoo, Jeesoo Kim, Sangkuk Lee, Nojun Kwak. CVPR 2018. paper

  26. Deep Reasoning with Knowledge Graph for Social Relationship Understanding. Zhouxia Wang, Tianshui Chen, Jimmy Ren, Weihao Yu, Hui Cheng, Liang Lin. IJCAI 2018. paper

  27. Constructing Narrative Event Evolutionary Graph for Script Event Prediction. Zhongyang Li, Xiao Ding, Ting Liu. IJCAI 2018. paper

  28. Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering. Daniil Sorokin, Iryna Gurevych. COLING 2018. paper

  29. Convolutional networks on graphs for learning molecular fingerprints. David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael Gómez-Bombarelli, Timothy Hirzel, Alán Aspuru-Guzik, Ryan P. Adams. NIPS 2015. paper

  30. Molecular Graph Convolutions: Moving Beyond Fingerprints. Steven Kearnes, Kevin McCloskey, Marc Berndl, Vijay Pande, Patrick Riley. Journal of computer-aided molecular design 2016. paper

  31. Protein Interface Prediction using Graph Convolutional Networks. Alex Fout, Jonathon Byrd, Basir Shariat, Asa Ben-Hur.NIPS 2017. paper

  32. Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting. Zhiyong Cui, Kristian Henrickson, Ruimin Ke, Yinhai Wang. 2018. paper

  33. Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting. Bing Yu, Haoteng Yin, Zhanxing Zhu. IJCAI 2018. paper

  34. Semi-supervised User Geolocation via Graph Convolutional Networks. Afshin Rahimi, Trevor Cohn, Timothy Baldwin. ACL 2018. paper

  35. 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. paper

  36. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. Charles R. Qi, Hao Su, Kaichun Mo, Leonidas J. Guibas. CVPR 2018. paper

  37. 3D Graph Neural Networks for RGBD Semantic Segmentation. Xiaojuan Qi, Renjie Liao, Jiaya Jia, Sanja Fidler, Raquel Urtasun. CVPR 2017. paper

  38. Iterative Visual Reasoning Beyond Convolutions. Xinlei Chen, Li-Jia Li, Li Fei-Fei, Abhinav Gupta. CVPR 2018. paper

  39. Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs. Martin Simonovsky, Nikos Komodakis.CVPR 2017. paper

  40. Situation Recognition with Graph Neural Networks. Ruiyu Li, Makarand Tapaswi, Renjie Liao, Jiaya Jia, Raquel Urtasun, Sanja Fidler. ICCV 2017. paper

  41. Conversation Modeling on Reddit using a Graph-Structured LSTM. Vicky Zayats, Mari Ostendorf. TACL 2018. paper

  42. Graph Convolutional Networks for Text Classification. Liang Yao, Chengsheng Mao, Yuan Luo. AAAI 2019. paper

  43. Attention Is All You Need. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin. NIPS 2017. paper

  44. Self-Attention with Relative Position Representations. Peter Shaw, Jakob Uszkoreit, Ashish Vaswani. NAACL 2018. paper

  45. Hyperbolic Attention Networks. Caglar Gulcehre, Misha Denil, Mateusz Malinowski, Ali Razavi, Razvan Pascanu, Karl Moritz Hermann, Peter Battaglia, Victor Bapst, David Raposo, Adam Santoro, Nando de Freitas 2018. paper

  46. Effective Approaches to Attention-based Neural Machine Translation. Minh-Thang Luong, Hieu Pham, Christopher D. Manning. EMNLP 2015. paper

  47. Graph Convolutional Encoders for Syntax-aware Neural Machine Translation. Joost Bastings, Ivan Titov, Wilker Aziz, Diego Marcheggiani, Khalil Sima'an. EMNLP 2017. paper

  48. NerveNet: Learning Structured Policy with Graph Neural Networks. Tingwu Wang, Renjie Liao, Jimmy Ba, Sanja Fidler.ICLR 2018. paper

  49. Metacontrol for Adaptive Imagination-Based Optimization. Jessica B. Hamrick, Andrew J. Ballard, Razvan Pascanu, Oriol Vinyals, Nicolas Heess, Peter W. Battaglia. ICLR 2017. paper

  50. Learning model-based planning from scratch. Razvan Pascanu, Yujia Li, Oriol Vinyals, Nicolas Heess, Lars Buesing, Sebastien Racanière, David Reichert, Théophane Weber, Daan Wierstra, Peter Battaglia. 2017. paper

  51. Structured Dialogue Policy with Graph Neural Networks. Lu Chen, Bowen Tan, Sishan Long and Kai Yu. ICCL 2018. paper

  52. Relational inductive bias for physical construction in humans and machines. Jessica B. Hamrick, Kelsey R. Allen, Victor Bapst, Tina Zhu, Kevin R. McKee, Joshua B. Tenenbaum, Peter W. Battaglia. CogSci 2018. paper

  53. Relational Deep Reinforcement Learning. Vinicius Zambaldi, David Raposo, Adam Santoro, Victor Bapst, Yujia Li, Igor Babuschkin, Karl Tuyls, David Reichert, Timothy Lillicrap, Edward Lockhart, Murray Shanahan, Victoria Langston, Razvan Pascanu, Matthew Botvinick, Oriol Vinyals, Peter Battaglia. 2018. paper

  54. Action Schema Networks: Generalised Policies with Deep Learning. Sam Toyer, Felipe Trevizan, Sylvie Thiébaux, Lexing Xie. AAAI 2018. paper

  55. Neural Combinatorial Optimization with Reinforcement Learning. Irwan Bello, Hieu Pham, Quoc V. Le, Mohammad Norouzi, Samy Bengio. 2016. paper

  56. A Note on Learning Algorithms for Quadratic Assignment with Graph Neural Networks.Alex Nowak, Soledad Villar, Afonso S. Bandeira, Joan Bruna. PADL 2017. paper

  57. Learning Combinatorial Optimization Algorithms over Graphs. Hanjun Dai, Elias B. Khalil, Yuyu Zhang, Bistra Dilkina, Le Song. NIPS 2017. paper

  58. Attention Solves Your TSP, Approximately. Wouter Kool, Herke van Hoof, Max Welling. 2018. paper

  59. Learning a SAT Solver from Single-Bit Supervision. Daniel Selsam, Matthew Lamm, Benedikt Bünz, Percy Liang, Leonardo de Moura, David L. Dill. 2018. paper

  60. Learning to Represent Programs with Graphs. Miltiadis Allamanis, Marc Brockschmidt, Mahmoud Khademi. ICLR 2018. paper

  61. Learning Graphical State Transitions. Daniel D. Johnson. ICLR 2017. paper

  62. Inference in Probabilistic Graphical Models by Graph Neural Networks. KiJung Yoon, Renjie Liao, Yuwen Xiong, Lisa Zhang, Ethan Fetaya, Raquel Urtasun, Richard Zemel, Xaq Pitkow. ICLR Workshop 2018. paper

  63. Learning deep generative models of graphs. Yujia Li, Oriol Vinyals, Chris Dyer, Razvan Pascanu, Peter Battaglia. ICLR Workshop 2018. paper

  64. MolGAN: An implicit generative model for small molecular graphs. Nicola De Cao, Thomas Kipf. 2018. paper

  65. GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models. Jiaxuan You, Rex Ying, Xiang Ren, William L. Hamilton, Jure Leskovec. ICML 2018. paper

  66. NetGAN: Generating Graphs via Random Walks. Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, Stephan Günnemann. ICML 2018. paper

  67. Adversarial Attack on Graph Structured Data. Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, Le Song. ICML 2018. paper

  68. Graph Convolutional Neural Networks for Web-Scale Recommender Systems. Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, Jure Leskovec. KDD 2018. paper

  69. Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks. Kai Sheng Tai, Richard Socher, Christopher D. Manning. ACL 2015. paper

  70. Neural Module Networks. Jacob Andreas, Marcus Rohrbach, Trevor Darrell, Dan Klein. CVPR 2016. paper

  71. Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling.Diego Marcheggiani, Ivan Titov.EMNLP 2017. paper

  72. Graph Convolutional Networks with Argument-Aware Pooling for Event Detection. Thien Huu Nguyen, Ralph Grishman.AAAI 2018. paper

  73. Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks. Federico Monti, Michael M. Bronstein, Xavier Bresson. NIPS 2017. paper

  74. Graph Convolutional Matrix Completion. Rianne van den Berg, Thomas N. Kipf, Max Welling.2017. paper

  75. Hybrid Approach of Relation Network and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classification. Sungmin Rhee, Seokjun Seo, Sun Kim. IJCAI 2018. paper

  76. Modeling polypharmacy side effects with graph convolutional networks. Marinka Zitnik, Monica Agrawal, Jure Leskovec.ISMB 2018. paper

  77. DeepInf: Modeling influence locality in large social networks. Jiezhong Qiu, Jian Tang, Hao Ma, Yuxiao Dong, Kuansan Wang, Jie Tang. KDD 2018. paper

  78. Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks.Diego Marcheggiani, Joost Bastings, Ivan Titov. NAACL 2018. paper

  79. 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. paper

  80. Graph Convolution over Pruned Dependency Trees Improves Relation Extraction. Yuhao Zhang, Peng Qi, Christopher D. Manning. EMNLP 2018. paper

  81. N-ary relation extraction using graph state LSTM. Linfeng Song, Yue Zhang, Zhiguo Wang, Daniel Gildea. EMNLP 18. paper

  82. A Graph-to-Sequence Model for AMR-to-Text Generation. Linfeng Song, Yue Zhang, Zhiguo Wang, Daniel Gildea. ACL 2018. paper

  83. Cross-Sentence N-ary Relation Extraction with Graph LSTMs. Nanyun Peng, Hoifung Poon, Chris Quirk, Kristina Toutanova, Wen-tau Yih. TACL. paper

  84. Sentence-State LSTM for Text Representation. Yue Zhang, Qi Liu, Linfeng Song. ACL 2018. paper

  85. End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures. Makoto Miwa, Mohit Bansal. ACL 2016. paper

  86. Learning Human-Object Interactions by Graph Parsing Neural Networks. Siyuan Qi, Wenguan Wang, Baoxiong Jia, Jianbing Shen, Song-Chun Zhu. ECCV 2018. paper

  87. Multiple Events Extraction via Attention-based Graph Information Aggregation. Xiao Liu, Zhunchen Luo, Heyan Huang.EMNLP 2018. paper

  88. Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks. Zhichun Wang, Qingsong Lv, Xiaohan Lan, Yu Zhang. EMNLP 2018. paper

  89. Graph Convolution over Pruned Dependency Trees Improves Relation Extraction. Yuhao Zhang, Peng Qi, Christopher D. Manning. EMNLP 2018. paper

  90. Recurrent Relational Networks. Rasmus Palm, Ulrich Paquet, Ole Winther. NeurIPS 2018. paper

  91. Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation.Jiaxuan You, Bowen Liu, Rex Ying, Vijay Pande, Jure Leskovec. NeurIPS 2018. paper

  92. Learning Conditioned Graph Structures for Interpretable Visual Question Answering. Will Norcliffe-Brown, Efstathios Vafeias, Sarah Parisot. NeurIPS 2018. paper

  93. Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search.Zhuwen Li, Qifeng Chen, Vladlen Koltun. NeurIPS 2018. paper

  94. Symbolic Graph Reasoning Meets Convolutions. Xiaodan Liang, Zhiting Hu, Hao Zhang, Liang Lin, Eric P. Xing. NeurIPS 2018. paper

  95. Out of the Box: Reasoning with Graph Convolution Nets for Factual Visual Question Answering. Medhini Narasimhan, Svetlana Lazebnik, Alexander Schwing. NeurIPS 2018. paper

  96. Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders. Tengfei Ma, Jie Chen, Cao Xiao. NeurIPS 2018. paper

  97. Structural-RNN: Deep Learning on Spatio-Temporal Graphs. Ashesh Jain, Amir R. Zamir, Silvio Savarese, Ashutosh Saxena.CVPR 2016. paper

  98. Relation Networks for Object Detection. Han Hu, Jiayuan Gu, Zheng Zhang, Jifeng Dai, Yichen Wei. CVPR 2018. paper

  99. Learning Region features for Object Detection. Jiayuan Gu, Han Hu, Liwei Wang, Yichen Wei, Jifeng Dai. ECCV 2018. paper

  100. Deep Graph Infomax. Petar Veličković, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, R Devon Hjelm. ICLR 2019. paper

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

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