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。

知识荟萃

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

VIP内容

本文将图上定义的神经网络转换为消息传递神经网络(MPNNs),以研究这类模型的不同分类的区分能力。我们感兴趣的是某些架构何时能够根据作为图输入的特征标签区分顶点。我们考虑了两种不同的MPNNs: 匿名MPNNs,其消息函数只依赖于所涉及的顶点的标签; 以及程度感知的MPNNs,其消息函数可以额外使用关于顶点度数的信息。前一类涵盖了流行的图神经网络(GNN)形式,其优异的能力是已知的。后者包括Kipf和Welling提出的图卷积网络(GCNs),其区分能力未知。利用Weisfeiler-Lehman (WL)算法的辨识能力,得到了(匿名和程度感知)多神经网络辨识能力的上界和下界。我们的主要结果表明: (1) GCNs的分辨能力受到WL算法的限制,但它们可能领先一步; (ii) WL算法不能用普通的GCNs模拟,但通过在顶点和其邻居的特征之间添加一个权衡参数(Kipf和Welling提出的)可以解决这个问题。

https://proceedings.mlr.press/v139/geerts21a

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Synthesising the spatial and temporal dynamics of the human body skeleton remains a challenging task, not only in terms of the quality of the generated shapes, but also of their diversity, particularly to synthesise realistic body movements of a specific action (action conditioning). In this paper, we propose Kinetic-GAN, a novel architecture that leverages the benefits of Generative Adversarial Networks and Graph Convolutional Networks to synthesise the kinetics of the human body. The proposed adversarial architecture can condition up to 120 different actions over local and global body movements while improving sample quality and diversity through latent space disentanglement and stochastic variations. Our experiments were carried out in three well-known datasets, where Kinetic-GAN notably surpasses the state-of-the-art methods in terms of distribution quality metrics while having the ability to synthesise more than one order of magnitude regarding the number of different actions. Our code and models are publicly available at https://github.com/DegardinBruno/Kinetic-GAN.

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