图卷积网络(简称GCN),由Thomas Kpif于2017年在论文Semi-supervised classification with graph convolutional networks中提出。它为图(graph)结构数据的处理提供了一个崭新的思路,将深度学习中常用于图像的卷积神经网络应用到图数据上。

图卷积神经网络 (GCN)荟萃

入门学习

  1. GCN 入门 https://www.cnblogs.com/megachen/p/11492647.html
  2. GCN入门理解 [https://blog.csdn.net/weixin_30468137/article/details/96697624]
  3. 从CNN到GCN的联系与区别——GCN从入门到精(fang)通(qi) [https://blog.csdn.net/weixin_40013463/article/details/81089223]
  4. GCN学习笔记 [https://www.cnblogs.com/ranup/p/10914494.html]
  5. [GCN] 论文笔记:SEMI-SUPERVISED CLASSIFICATION WITH GRAPH CONVOLUTIONAL NETWORKS https://blog.csdn.net/qq_41727666/article/details/84640549

综述

  1. Relational inductive biases, deep learning, and graph networks Peter W. Battaglia [https://arxiv.org/pdf/1806.01261.pdf]
  2. Graph Neural Networks: A Review of Methods and Applications [https://arxiv.org/pdf/1812.08434v1.pdf]
  3. A Comprehensive Survey on Graph Neural Networks [https://arxiv.org/pdf/1901.00596.pdf]
  4. Deep Learning on Graphs: A Survey [https://arxiv.org/pdf/1812.04202.pdf]

进阶论文

2015

  1. David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael GómezBombarelli, Timothy Hirzel, Alán Aspuru-Guzik, Ryan P. Adams. Convolutional networks on graphs for learning molecular fingerprints [https://arxiv.org/pdf/1509.09292.pdf]

2016

  1. Sainbayar Sukhbaatar, Arthur Szlam, Rob Fergus. Learning Multiagent Communication with Backpropagation
    [https://arxiv.org/pdf/1605.07736.pdf]
  2. Ashesh Jain, Amir R. Zamir, Silvio Savarese, Ashutosh Saxena. Structural-RNN: Deep Learning on Spatio-Temporal Graphs
    [https://arxiv.org/abs/1511.05298]

2017

  1. Alex Fout, Jonathon Byrd, Basir Shariat, Asa Ben-Hur. Protein Interface Prediction using Graph Convolutional Networks
    [http://papers.nips.cc/paper/7231-protein-interface-prediction-using-graph-convolutional-networks.pdf]
  2. Daniel Oñoro-Rubio, Mathias Niepert, Alberto García-Durán, Roberto González, Roberto J. López-Sastre. Representation learning for visual-relational knowledge graphs
    [https://arxiv.org/pdf/1709.02314.pdf]
  3. Takuo Hamaguchi, Hidekazu Oiwa, Masashi Shimbo, Yuji Matsumoto. Knowledge Transfer for Out-of-Knowledge-Base Entities : A Graph Neural Network Approach
    [https://arxiv.org/pdf/1706.05674.pdf]
  4. Federico Monti, Michael M. Bronstein, Xavier Bresson. Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks
    [https://arxiv.org/abs/1704.06803]
  5. Rianne van den Berg, Thomas N. Kipf, Max Welling. Graph Convolutional Matrix Completion
    [https://arxiv.org/abs/1706.02263]
  6. Kenneth Marino, Ruslan Salakhutdinov, Abhinav Gupta. The More You Know: Using Knowledge Graphs for Image Classification
    [https://arxiv.org/pdf/1612.04844.pdf]
  7. Damien Teney, Lingqiao Liu, Anton van den Hengel. Graph-Structured Representations for Visual Question Answering
    [https://arxiv.org/pdf/1609.05600.pdf]
  8. Xiaojuan Qi, Renjie Liao, Jiaya Jia, Sanja Fidler, Raquel Urtasun. 3D Graph Neural Networks for RGBD Semantic Segmentation
    [https://ieeexplore.ieee.org/document/8237818]
  9. Martin Simonovsky, Nikos Komodakis. Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs
    [https://arxiv.org/pdf/1704.02901.pdf]
  10. Ruiyu Li, Makarand Tapaswi, Renjie Liao, Jiaya Jia, Raquel Urtasun, Sanja Fidler. Situation Recognition with Graph Neural Networks
    [https://arxiv.org/pdf/1708.04320.pdf]
  11. Diego Marcheggiani, Ivan Titov. Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling
    [https://arxiv.org/abs/1703.04826]
  12. Joost Bastings, Ivan Titov, Wilker Aziz, Diego Marcheggiani, Khalil Sima'an. Graph Convolutional Encoders for Syntax-aware Neural Machine Translation
    [https://arxiv.org/pdf/1704.04675.pdf]

2018

  1. Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, Richard Zemel. Neural Relational Inference for Interacting Systems
    [https://arxiv.org/pdf/1802.04687.pdf]
  2. Marinka Zitnik, Monica Agrawal, Jure Leskovec. Modeling polypharmacy side effects with graph convolutional networks
    [https://arxiv.org/abs/1802.00543.pdf]
  3. Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, Max Welling. Modeling Relational Data with Graph Convolutional Networks
    [https://arxiv.org/pdf/1703.06103.pdf]
  4. Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, Jure Leskovec. Graph Convolutional Neural Networks for Web-Scale Recommender Systems
    [https://arxiv.org/abs/1806.01973.pdf]
  5. Medhini Narasimhan, Svetlana Lazebnik, Alexander Schwing. Out of the Box: Reasoning with Graph Convolution Nets for Factual Visual Question Answering
    [https://arxiv.org/abs/1811.00538]
  6. Han Hu, Jiayuan Gu, Zheng Zhang, Jifeng Dai, Yichen Wei. Relation Networks for Object Detection
    [https://arxiv.org/abs/1711.11575]
  7. DSijie Yan, Yuanjun Xiong, Dahua Lin. Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
    [https://arxiv.org/pdf/1801.07455.pdf]
  8. Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E. Sarma, Michael M. Bronstein, Justin M. Solomon. Dynamic Graph CNN for Learning on Point Clouds
    [https://arxiv.org/pdf/1801.07829.pdf]
  9. Charles R. Qi, Hao Su, Kaichun Mo, Leonidas J. Guibas. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
    [https://arxiv.org/pdf/1612.00593.pdf]
  10. Zhouxia Wang, Tianshui Chen, Jimmy Ren, Weihao Yu, Hui Cheng, Liang Lin. Deep Reasoning with Knowledge Graph for Social Relationship Understanding
    [https://arxiv.org/pdf/1807.00504.pdf]
  11. Thien Huu Nguyen, Ralph Grishman. Graph Convolutional Networks with Argument-Aware Pooling for Event Detection
    [http://ix.cs.uoregon.edu/~thien/pubs/graphConv.pdf]
  12. Diego Marcheggiani, Joost Bastings, Ivan Titov. Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks
    [https://www.aclweb.org/anthology/N18-2078/]
  13. Linfeng Song, Zhiguo Wang, Mo Yu, Yue Zhang, Radu Florian, Daniel Gildea. Exploring Graph-structured Passage Representation for Multi-hop Reading Comprehension with Graph Neural Networks
    [https://arxiv.org/abs/1809.02040]
  14. Yuhao Zhang, Peng Qi, Christopher D. Manning. Graph Convolution over Pruned Dependency Trees Improves Relation Extraction
    [https://arxiv.org/abs/1809.10185]
  15. Daniel Beck, Gholamreza Haffari, Trevor Cohn. Graph-to-Sequence Learning using Gated Graph Neural Networks
    [https://arxiv.org/pdf/1806.09835.pdf]
  16. Afshin Rahimi, Trevor Cohn, Timothy Baldwin. Semi-supervised User Geolocation via Graph Convolutional Networks
    [https://arxiv.org/pdf/1804.08049.pdf]
  17. Daniil Sorokin, Iryna Gurevych. Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering
    [https://arxiv.org/pdf/1808.04126.pdf]

2019

  1. Wengong Jin, Kevin Yang, Regina Barzilay, Tommi Jaakkola. Learning Multimodal Graph-to-Graph Translation for Molecular Optimization
    [https://openreview.net/pdf?id=B1xJAsA5F7]
  2. Peifeng Wang, Jialong Han, Chenliang Li, Rong Pan. Logic Attention Based Neighborhood Aggregation for Inductive Knowledge Graph Embedding
    [https://arxiv.org/pdf/1811.01399.pdf]
  3. Namyong Park, Andrey Kan, Xin Luna Dong, Tong Zhao, Christos Faloutsos. Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks
    [https://arxiv.org/pdf/1905.08865.pdf]
  4. Deepak Nathani, Jatin Chauhan, Charu Sharma, Manohar Kaul. Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs
    [https://arxiv.org/pdf/1906.01195.pdf]
  5. Kun Xu, Mo Yu, Yansong Feng, Yan Song, Zhiguo Wang, Dong Yu. Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Network
    [https://128.84.21.199/pdf/1905.11605.pdf]
  6. Jiani Zhang, Xingjian Shi, Shenglin Zhao, Irwin King. STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems
    [https://arxiv.org/pdf/1905.13129.pdf]
  7. Haoyu Wang, Defu Lian, Yong Ge. Binarized Collaborative Filtering with Distilling Graph Convolutional Networks
    [https://arxiv.org/pdf/1906.01829.pdf]
  8. Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, Tieniu Tan. Session-based Recommendation with Graph Neural Networks
    [https://arxiv.org/pdf/1811.00855.pdf]
  9. Jin Shang, Mingxuan Sun. Geometric Hawkes Processes with Graph Convolutional Recurrent Neural Networks
    [https://jshang2.github.io/pubs/geo.pdf]
  10. Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, Tat-Seng Chua. KGAT: Knowledge Graph Attention Network for Recommendation
    [https://arxiv.org/pdf/1905.07854.pdf]
  11. Hongwei Wang, Miao Zhao, Xing Xie, Wenjie Li, Minyi Guo. Knowledge Graph Convolutional Networks for Recommender Systems
    [https://arxiv.org/pdf/1904.12575.pdf]
  12. Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, Dawei Yin. Graph Neural Networks for Social Recommendation
    [https://arxiv.org/pdf/1902.07243.pdf]
  13. Junyu Gao, Tianzhu Zhang, Changsheng Xu. I Know the Relationships: Zero-Shot Action Recognition via Two-Stream Graph Convolutional Networks and Knowledge Graphs
    [https://aaai.org/ojs/index.php/AAAI/article/view/4843]
  14. Zhao-Min Chen, Xiu-Shen Wei, Peng Wang, Yanwen Guo. Multi-Label Image Recognition with Graph Convolutional Networks
    [https://arxiv.org/pdf/1904.03582.pdf]
  15. Xinhong Ma, Tianzhu Zhang, Changsheng Xu. GCAN: Graph Convolutional Adversarial Network for Unsupervised Domain Adaptation
    [http://openaccess.thecvf.com/content_CVPR_2019/papers/Ma_GCAN_Graph_Convolutional_Adversarial_Network_for_Unsupervised_Domain_Adaptation_CVPR_2019_paper.pdf]
  16. Mengshi Qi, Weijian Li, Zhengyuan Yang, Yunhong Wang, Jiebo Luo. Attentive Relational Networks for Mapping Images to Scene Graphs
    [https://arxiv.org/pdf/1811.10696.pdf]
  17. Jianchao Wu, Limin Wang, Li Wang, Jie Guo, Gangshan Wu. Learning Actor Relation Graphs for Group Activity Recognition
    [https://arxiv.org/pdf/1904.10117.pdf]
  18. Yunpeng Chen, Marcus Rohrbach, Zhicheng Yan, Shuicheng Yan, Jiashi Feng, Yannis Kalantidis. Graph-Based Global Reasoning Networks
    [https://arxiv.org/pdf/1811.12814.pdf]
  19. Zhongdao Wang, Liang Zheng, Yali Li, Shengjin Wang. Linkage Based Face Clustering via Graph Convolution Network
    [https://arxiv.org/pdf/1903.11306.pdf]
  20. Long Zhao, Xi Peng, Yu Tian, Mubbasir Kapadia, Dimitris N. Metaxas. Semantic Graph Convolutional Networks for 3D Human Pose Regression
    [https://arxiv.org/pdf/1904.03345.pdf]
  21. Yichao Yan, Qiang Zhang, Bingbing Ni, Wendong Zhang, Minghao Xu, Xiaokang Yang. Learning Context Graph for Person Search
    [https://arxiv.org/pdf/1904.01830.pdf]
  22. Lei Wang, Yuchun Huang, Yaolin Hou, Shenman Zhang, Jie Shan. Graph Attention Convolution for Point Cloud Semantic Segmentation
    [http://openaccess.thecvf.com/content_CVPR_2019/papers/Wang_Graph_Attention_Convolution_for_Point_Cloud_Semantic_Segmentation_CVPR_2019_paper.pdf]
  23. Maosen Li, Siheng Chen, Xu Chen, Ya Zhang, Yanfeng Wang, Qi Tian. Actional-Structural Graph Convolutional Networks for Skeleton-based Action Recognition
    [https://arxiv.org/pdf/1904.12659.pdf]
  24. Junyu Gao, Tianzhu Zhang, Changsheng Xu. Graph Convolutional Tracking
    [http://nlpr-web.ia.ac.cn/mmc/homepage/jygao/JY_Gao_files/Conference_Papers/GCT-CVPR2019-GJY.pdf]
  25. Lei Shi, Yifan Zhang, Jian Cheng, Hanqing Lu. Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition
    [http://openaccess.thecvf.com/content_CVPR_2019/papers/Shi_Two-Stream_Adaptive_Graph_Convolutional_Networks_for_Skeleton-Based_Action_Recognition_CVPR_2019_paper.pdf]
  26. Lei Shi, Yifan Zhang, Jian Cheng, Hanqing Lu. Skeleton-Based Action Recognition With Directed Graph Neural Networks
    [http://openaccess.thecvf.com/content_CVPR_2019/papers/Shi_Skeleton-Based_Action_Recognition_With_Directed_Graph_Neural_Networks_CVPR_2019_paper.pdf]
  27. Liang Yao, Chengsheng Mao, Yuan Luo. Graph Convolutional Networks for Text Classification
    [https://arxiv.org/pdf/1809.05679.pdf]
  28. Shikhar Vashishth, Manik Bhandari, Prateek Yadav, Piyush Rai, Chiranjib Bhattacharyya, Partha Talukdar Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks
    [https://arxiv.org/pdf/1809.04283.pdf]
  29. Sunil Kumar Sahu, Fenia Christopoulou, Makoto Miwa, Sophia Ananiadou. Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network
    [https://arxiv.org/pdf/1906.04684.pdf]
  30. Daesik Kim, Seonhoon Kim, Nojun Kwak. Textbook Question Answering with Multi-modal Context Graph Understanding and Self-supervised Open-set Comprehension
    [https://arxiv.org/pdf/1811.00232.pdf]
  31. Yunxuan Xiao, Yanru Qu, Lin Qiu, Hao Zhou, Lei Li, Weinan Zhang, Yong Yu. Dynamically Fused Graph Network for Multi-hop Reasoning
    [https://arxiv.org/pdf/1905.06933.pdf]
  32. Zhijiang Guo, Yan Zhang, Wei Lu. Attention Guided Graph Convolutional Networks for Relation Extraction
    [http://www.statnlp.org/wp-content/uploads/2019/06/Attention_Guided_Graph_Convolutional_Networks_for_Relation_Extraction.pdf]
  33. Hao Zhu, Yankai Lin, Zhiyuan Liu, Jie Fu, Tat-seng Chua, Maosong Sun. Graph Neural Networks with Generated Parameters for Relation Extraction
    [https://arxiv.org/pdf/1902.00756.pdf]
  34. Ningyu Zhang, Shumin Deng, Zhanlin Sun, Guanying Wang, Xi Chen, Wei Zhang, Huajun Chen. Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks
    [https://arxiv.org/pdf/1903.01306.pdf]
  35. Chun Wang, Shirui Pan, Ruiqi Hu, Guodong Long, Jing Jiang, Chengqi Zhang. Attributed Graph Clustering: A Deep Attentional Embedding Approach
    [https://arxiv.org/pdf/1906.06532.pdf]
  36. Xiaotong Zhang, Han Liu, Qimai Li, Xiao-Ming Wu. Attributed Graph Clustering via Adaptive Graph Convolution
    [https://arxiv.org/pdf/1906.01210.pdf]
  37. Jia Li, Yu Rong, Hong Cheng, Helen Meng, Wenbing Huang, Junzhou Huang. Semi-Supervised Graph Classification: A Hierarchical Graph Perspective
    [https://arxiv.org/pdf/1904.05003.pdf]
  38. Hao Wang, Tong Xu, Qi Liu, Defu Lian, Enhong Chen, Dongfang Du, Han Wu, Wen Su. MCNE: An End-to-End Framework for Learning Multiple Conditional Network Representations of Social Network
    [https://arxiv.org/pdf/1905.11013.pdf]
  39. Ninghao Liu, Qiaoyu Tan, Yuening Li, Hongxia Yang, Jingren Zhou, Xia Hu. Is a Single Vector Enough? Exploring Node Polysemy for Network Embedding
    [https://arxiv.org/pdf/1905.10668.pdf]
  40. Chang Li, Dan Goldwasser. Encoding Social Information with Graph Convolutional Networks for Political Perspective Detection in News Media [https://www.cs.purdue.edu/homes/dgoldwas//downloads/papers/LiG_acl_2019.pdf]
  41. Hao Peng, Jianxin Li, Qiran Gong, Yangqiu Song, Yuanxing Ning, Kunfeng Lai, Philip S. Yu. Fine-grained Event Categorization with Heterogeneous Graph Convolutional Networks
    [https://arxiv.org/pdf/1906.04580.pdf]

Tutorial

  1. AAAI2019 Tutorial 《图表示学习》, 180页PPT带你从入门到精通 [https://www.zhuanzhi.ai/document/91ad3a1b183061fc8cca79b20b7d0e58]
  2. Graph Convolutional Neural Networks [https://github.com/dbusbridge/gcn_tutorial]
  3. 洛桑理工27页PPT带你入坑GCN:Graph上的深度学习报告 [https://mp.weixin.qq.com/s?src=11&timestamp=1575874094&ver=2023&signature=9LcXP6hPe0E4kcqXSwrO5P3AoVj4Snzqsic-wJm7dd9FwRiGZuS0uK12Mi4RQBTu4fqag1W4LVGDdRsVEn4eqOAJe0-SA7Ey2LlSq-XPEn8KGQIZsBW9PF*axzDhPS&new=1]

视频教程

  1. KDD 2019 Graph Convolutional Networks with EigenPooling [https://www.kdd.org/kdd2019/accepted-papers/view/graph-convolutional-networks-with-eigenpooling]
  2. 图卷积介绍
    [https://www.youtube.com/watch?v=UAwrDY_Bcdc]
  3. Graph Convolutional Neural Networks [https://www.youtube.com/watch?v=Mf-mIRF3ao8]
  4. Graph Convolutional Network (GCN), Graph Neural Networks (Graph Nets), Geometric Deep Learning
    [http://primo.ai/index.php?title=Graph_Convolutional_Network_(GCN),Graph_Neural_Networks(Graph_Nets),_Geometric_Deep_Learning]
  5. Convolutional Neural Networks on Graphs [https://www.youtube.com/watch?v=v3jZRkvIOIM]

代码

  1. 使用DGL实现图卷积网络 [https://docs.dgl.ai/en/latest/tutorials/models/1_gnn/1_gcn.html]
  2. pointnet代码 [https://github.com/charlesq34/pointnet]
  3. pointnet++代码 [https://github.com/charlesq34/pointnet2]
  4. pytorch实现GCN [https://github.com/tkipf/pygcn]
  5. tensorflow 实现GCN [https://github.com/tkipf/gcn]
  6. Deep Learning on Graphs with Keras [https://github.com/tkipf/keras-gcn]

领域专家

  1. University of Amsterdam: Thomas Kipf
  2. TU Dortmund University - Interested in Representation Learning on Graphs and Manifolds; PyTorch, CUDA, Vim and macOS Enthusiast: Matthias Fey
  3. School of Computer Science and Engineering, Nanyang Technological University: Xavier Bresson
  4. Department of Computer Science, Stanford University: Jure Leskovec
  5. Stanford University: Zhitao Ying
  6. 北京大学数学科学学院: 朱占星
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