知識星球2018年AI領域最閃耀的技術，除了NLP領域以Bert、GPT模型等為代表的無監督預訓練技術之外，另外一個研究熱點就是Graph Neural Network（GNN），並且這一熱點在2019年還會繼續持續。本文以GNN為重點，列出相關必讀論文，並跟蹤技術最新進展情況。我們期待著推動這一方向技術進步，並向這一方向的研究人員提供一些幫助。
本文內容整理自網路，原文地址：https://github.com/jdlc105/Must-read-papers-and-continuous-tracking-on-Graph-Neural-Network-GNN-progress
現實世界中的很多問題和應用都可以圖的形式來表示，例如社交網路、蛋白質相互作用網路、大腦網路、化學分子圖和3D點雲。因此，在跨學科研究的推動下，面向圖形資料的神經網路模型已經成為一個新興的研究熱點。其中，深度學習的三位先驅中的兩位，Yann LeCun教授(2018年圖靈獎獲得者)、Yoshua Bengio教授(2018年圖靈獎獲得者)和斯坦福大學人工智慧實驗室著名的Jure Leskovec教授也加入到這個領域研究之中。
技術關鍵詞
Graph Neural Network, Graph convolutional network, Graph network, Graph attention network, Graph auto-encoder
當前熱門的研究課題：由 T.N. Kipf和M. Welling在ICLR2017中提出的代表性工作—圖摺積網路(GCNs)，在Google Scholar(截至2019年5月9日)中被取用了1020次。更新:1065次(截至2019年5月20日)。更新:1106次(截至2019年5月27日)。
專案開始時間:2018年12月11日，最新更新時間:2019年5月27日
更多關於GCN模型及其應用的論文將來自CVPR 2019、WWW2019、SIGKDD2019、ICML2019….坐等這些論文Release出來。
綜述論文
1、Ziwei Zhang, Peng Cui, Wenwu Zhu, Deep Learning on Graphs: A Survey, ArXiv, 2018.
由清華大學校崔鵬老師等整理的深度學習圖技術分類論文。
2、Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Maosong Sun, Graph Neural Networks: A Review of Methods and Applications, ArXiv, 2018.
來自清華大學劉洋老師團隊的綜述論文。
3、Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, Philip S. Yu(Fellow,IEEE), A Comprehensive Survey on Graph Neural Networks, ArXiv, 2019.
期刊論文
F. Scarselli, M. Gori, A.C. Tsoi, M. Hagenbuchner, G. Monfardini, The graph neural network model, IEEE Transactions on Neural Networks(IEEE Transactions on Neural Networks and Learning Systems), 2009. paper.
Scarselli F, Gori M, Tsoi A C, et al. Computational capabilities of graph neural networks, IEEE Transactions on Neural Networks, 2009. paper.
Micheli A . Neural Network for Graphs: A Contextual Constructive Approach. IEEE Transactions on Neural Networks, 2009. paper.
Goles, Eric, and Gonzalo A. Ruz. Dynamics of Neural Networks over Undirected Graphs. Neural Networks, 2015. paper.
Z. Luo, L. Liu, J. Yin, Y. Li, Z. Wu, Deep Learning of Graphs with Ngram Convolutional Neural Networks, IEEE Transactions on Knowledge & Data Engineering, 2017. paper. code.
Petroski Such F , Sah S , Dominguez M A , et al. Robust Spatial Filtering with Graph Convolutional Neural Networks. IEEE Journal of Selected Topics in Signal Processing, 2017. paper.
Kawahara J, Brown C J, Miller S P, et al. BrainNetCNN: convolutional neural networks for brain networks; towards predicting neurodevelopment. NeuroImage, 2017. paper.
Muscoloni A , Thomas J M , Ciucci S , et al. Machine learning meets complex networks via coalescent embedding in the hyperbolic space. Nature Communications, 2017. paper.
D.M. Camacho, K.M. Collins, R.K. Powers, J.C. Costello, J.J. Collins, Next-Generation Machine Learning for Biological Networks, Cell, 2018. paper.
Marinka Z , Monica A , Jure L . Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics, 2018. paper.
Sarah P , Ira K S , Enzo F , et al. Disease Prediction using Graph Convolutional Networks: Application to Autism Spectrum Disorder and Alzheimer’s Disease. Medical Image Analysis, 2018. paper.
Sofia Ira Ktena, Sarah Parisot, Enzo Ferrante, Martin Rajchl, Matthew Lee, Ben Glocker, Daniel Rueckert, Metric learning with spectral graph convolutions on brain connectivity networks, NeuroImage, 2018. paper.
Xie T , Grossman J C . Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties. Physical Review Letters, 2018. paper.
Phan, Anh Viet, Minh Le Nguyen, Yen Lam Hoang Nguyen, and Lam Thu Bui. DGCNN: A Convolutional Neural Network over Large-Scale Labeled Graphs. Neural Networks, 2018. paper
Song T, Zheng W, Song P, et al. Eeg emotion recognition using dynamical graph convolutional neural networks. IEEE Transactions on Affective Computing, 2018. paper
Levie R, Monti F, Bresson X, et al. Cayleynets: Graph convolutional neural networks with complex rational spectral filters. IEEE Transactions on Signal Processing 2019. paper
Zhang, Zhihong, Dongdong Chen, Jianjia Wang, Lu Bai, and Edwin R. Hancock. Quantum-Based Subgraph Convolutional Neural Networks. Pattern Recognition, 2019. paper
Qin A, Shang Z, Tian J, et al. Spectral–Spatial Graph Convolutional Networks for Semisupervised Hyperspectral Image Classification. IEEE Geoscience and Remote Sensing Letters, 2019. paper
Coley C W, Jin W, Rogers L, et al. A graph-convolutional neural network model for the prediction of chemical reactivity. Chemical Science, 2019. paper
Zhang Z, Chen D, Wang Z, et al. Depth-based Subgraph Convolutional Auto-Encoder for Network Representation Learning. Pattern Recognition, 2019. paper
Hong Y, Kim J, Chen G, et al. Longitudinal Prediction of Infant Diffusion MRI Data via Graph Convolutional Adversarial Networks. IEEE transactions on medical imaging, 2019. paper
Khodayar M, Mohammadi S, Khodayar M E, et al. Convolutional Graph Autoencoder: A Generative Deep Neural Network for Probabilistic Spatio-temporal Solar Irradiance Forecasting. IEEE Transactions on Sustainable Energy, 2019. paper
會議論文
Duvenaud D, Maclaurin D, Aguilera-Iparraguirre J, et al. Convolutional networks on graphs for learning molecular fingerprints, NeurIPS(NIPS) 2015. paper. code.
M. Niepert, M. Ahmed, K. Kutzkov, Learning Convolutional Neural Networks for Graphs, ICML 2016. paper.
S. Cao, W. Lu, Q. Xu, Deep neural networks for learning graph representations, AAAI 2016. paper.
M. Defferrard, X. Bresson, P. Vandergheynst, Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering, NeurIPS(NIPS) 2016. paper. code.
T.N. Kipf, M. Welling, Semi-Supervised Classification with Graph Convolutional Networks, ICLR 2017. paper. code.
A. Fout, B. Shariat, J. Byrd, A. Benhur, Protein Interface Prediction using Graph Convolutional Networks, NeurIPS(NIPS) 2017. paper.
Monti F, Bronstein M, Bresson X. Geometric matrix completion with recurrent multi-graph neural networks, NeurIPS(NIPS) 2017. paper.
Simonovsky M, Komodakis N. Dynamic edgeconditioned filters in convolutional neural networks on graphs, CVPR. 2017. paper
R. Li, S. Wang, F. Zhu, J. Huang, Adaptive Graph Convolutional Neural Networks, AAAI 2018. paper
J. You, B. Liu, R. Ying, V. Pande, J. Leskovec, Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation, NeurIPS(NIPS) 2018. paper.
C. Zhuang, Q. Ma, Dual Graph Convolutional Networks for Graph-Based Semi-Supervised Classification, WWW 2018. paper
H. Gao, Z. Wang, S. Ji, Large-Scale Learnable Graph Convolutional Networks, KDD 2018. paper
D. Zügner, A. Akbarnejad, S. Günnemann, Adversarial Attacks on Neural Networks for Graph Data, KDD 2018. paper
Ying R , He R , Chen K , et al. Graph Convolutional Neural Networks for Web-Scale Recommender Systems. KDD 2018. paper
P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò, Y. Bengio, Graph Attention Networks, ICLR, 2018. paper
Beck, Daniel Edward Robert, Gholamreza Haffari and Trevor Cohn. Graph-to-Sequence Learning using Gated Graph Neural Networks. ACL 2018. paper
Marcheggiani D , Bastings J , Titov I . Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks. NAACL 2018. paper
Chen J , Zhu J , Song L . Stochastic Training of Graph Convolutional Networks with Variance Reduction. ICML 2018. paper
Gusi Te, Wei Hu, Amin Zheng, Zongming Guo, RGCNN: Regularized Graph CNN for Point Cloud Segmentation. ACM Multimedia 2018. paper, code,
Talukdar, Partha, Shikhar Vashishth, Shib Sankar Dasgupta and Swayambhu Nath Ray. Dating Documents using Graph Convolution Networks. ACL 2018. paper, code
Sanchez-Gonzalez A , Heess N , Springenberg J T , et al. Graph networks as learnable physics engines for inference and control. ICML 2018. paper
Muhan Zhang, Yixin Chen. Link Prediction Based on Graph Neural Networks. NeurIPS(NIPS) 2018. paper
Chen, Jie, Tengfei Ma, and Cao Xiao. FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling. ICLR 2018. paper
Zhang, Zhen, Hongxia Yang, Jiajun Bu, Sheng Zhou, Pinggang Yu, Jianwei Zhang, Martin Ester, and Can Wang. ANRL: Attributed Network Representation Learning via Deep Neural Networks.. IJCAI 2018. paper
Rahimi A , Cohn T , Baldwin T . Semi-supervised User Geolocation via Graph Convolutional Networks. ACL 2018. paper
Morris C , Ritzert M , Fey M , et al.Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks.. AAAI 2019. paper
Xu K, Hu W, Leskovec J, et al. How Powerful are Graph Neural Networks?, ICLR 2019. paper
Johannes Klicpera, Aleksandar Bojchevski, Stephan Günnemann. Combining Neural Networks with Personalized PageRank for Classification on Graphs, ICLR 2019. paper
Daniel Zügner, Stephan Günnemann. Adversarial Attacks on Graph Neural Networks via Meta Learning, ICLR 2019. paper
Zhang Xinyi, Lihui Chen. Capsule Graph Neural Network, ICLR 2019. paper
Liao, R., Zhao, Z., Urtasun, R., and Zemel, R. LanczosNet: Multi-Scale Deep Graph Convolutional Networks, ICLR 2019, paper
Bingbing Xu, Huawei Shen, Qi Cao, Yunqi Qiu, Xueqi Cheng. Graph Wavelet Neural Network, ICLR 2019, paper
Hu J, Guo C, Yang B, et al. Stochastic Weight Completion for Road Networks using Graph Convolutional Networks ICDE. 2019. paper
Yao L, Mao C, Luo Y . Graph Convolutional Networks for Text Classification. AAAI 2019. paper
Landrieu L , Boussaha M . Point Cloud Oversegmentation with Graph-Structured Deep Metric Learning. CVPR 2019. paper
Si C , Chen W , Wang W , et al. An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition. CVPR 2019. paper
Cucurull G , Taslakian P , Vazquez D . Context-Aware Visual Compatibility Prediction. CVPR 2019. paper
Jia-Xing Zhong, Nannan Li, Weijie Kong, Shan Liu, Thomas H. Li, Ge Li. Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detection. CVPR 2019. paper
Michael Kampffmeyer, Yinbo Chen, Xiaodan Liang, Hao Wang, Yujia Zhang, Eric P. Xing. Rethinking Knowledge Graph Propagation for Zero-Shot Learning. CVPR 2019. paper
Arushi Goel, Keng Teck Ma, Cheston Tan. An End-to-End Network for Generating Social Relationship Graphs. CVPR 2019. paper
Yichao Yan, Qiang Zhang, Bingbing Ni, Wendong Zhang, Minghao Xu, Xiaokang Yang. Learning Context Graph for Person Search. CVPR 2019 paper
Zhongdao Wang, Liang Zheng, Yali Li, Shengjin Wang. Linkage Based Face Clustering via Graph Convolution Network. CVPR 2019 paper
Lei Yang, Xiaohang Zhan, Dapeng Chen, Junjie Yan, Chen Change Loy, Dahua Lin. Learning to Cluster Faces on an Affinity Graph. CVPR 2019 paper
Yao Ma, Suhang Wang, Charu C. Aggarwal, Jiliang Tang. Graph Convolutional Networks with EigenPooling. KDD2019, paper
Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, Dawei Yin. Graph Neural Networks for Social Recommendation. WWW2019, paper
Kim J, Kim T, Kim S, et al. Edge-labeling Graph Neural Network for Few-shot Learning. CVPR 2019. paper
Jessica V. Schrouff, Kai Wohlfahrt, Bruno Marnette, Liam Atkinson. INFERRING JAVASCRIPT TYPES USING GRAPH NEURAL NETWORKS. ICLR 2019. paper
Emanuele Rossi, Federico Monti, Michael Bronstein, Pietro liò. ncRNA Classification with Graph Convolutional Networks. SIGKDD 2019. paper
Wu F, Zhang T, Souza Jr A H, et al. Simplifying Graph Convolutional Networks. ICML 2019. paper.
Junhyun Lee, Inyeop Lee, Jaewoo Kang. Self-Attention Graph Pooling. ICML 2019. paper.
Chiang W L, Liu X, Si S, et al. Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks. SIGKDD 2019. paper.
Namyong Park, Andrey Kan, Xin Luna Dong, Tong Zhao, Christos Faloutsos, Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks. SIGKDD 2019. paper.
Wu S, Tang Y, Zhu Y, et al. Session-based Recommendation with Graph Neural Networks. AAAI 2019. paper.
ArXiv論文
Li Y, Tarlow D, Brockschmidt M, et al. Gated graph sequence neural networks. arXiv 2015. paper
Henaff M, Bruna J, LeCun Y. Deep convolutional networks on graph-structured data, arXiv 2015. paper
Hechtlinger Y, Chakravarti P, Qin J. A generalization of convolutional neural networks to graph-structured data. arXiv 2017. paper
Marcheggiani D, Titov I. Encoding sentences with graph convolutional networks for semantic role labeling. arXiv 2017. paper
Battaglia P W, Hamrick J B, Bapst V, et al. Relational inductive biases, deep learning, and graph networks, arXiv 2018. paper
Verma S, Zhang Z L. Graph Capsule Convolutional Neural Networks. arXiv 2018. paper
Zhang T , Zheng W , Cui Z , et al. Tensor graph convolutional neural network. arXiv 2018. paper
Zou D, Lerman G. Graph Convolutional Neural Networks via Scattering. arXiv 2018. paper
Du J , Zhang S , Wu G , et al. Topology Adaptive Graph Convolutional Networks. arXiv 2018. paper.
Shang C , Liu Q , Chen K S , et al. Edge Attention-based Multi-Relational Graph Convolutional Networks. arXiv 2018. paper.
Scardapane S , Vaerenbergh S V , Comminiello D , et al. Improving Graph Convolutional Networks with Non-Parametric Activation Functions. arXiv 2018. paper.
Wang Y , Sun Y , Liu Z , et al. Dynamic Graph CNN for Learning on Point Clouds. arXiv 2018. paper.
Ryu S , Lim J , Hong S H , et al. Deeply learning molecular structure-property relationships using attention- and gate-augmented graph convolutional network. arXiv 2018. paper.
Cui Z , Henrickson K , Ke R , et al. High-Order Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting. arXiv 2018. paper.
Shchur O , Mumme M , Bojchevski A , et al. Pitfalls of Graph Neural Network Evaluation. arXiv 2018. paper.
Bai Y , Ding H , Bian S , et al. Graph Edit Distance Computation via Graph Neural Networks. arXiv 2018. paper.
Pedro H. C. Avelar, Henrique Lemos, Marcelo O. R. Prates, Luis Lamb, Multitask Learning on Graph Neural Networks – Learning Multiple Graph Centrality Measures with a Unified Network. arXiv 2018. paper.
Matthew Baron, Topology and Prediction Focused Research on Graph Convolutional Neural Networks. arXiv 2018. paper.
Wenting Zhao, Chunyan Xu, Zhen Cui, Tong Zhang, Jiatao Jiang, Zhenyu Zhang, Jian Yang, When Work Matters: Transforming Classical Network Structures to Graph CNN. arXiv 2018. paper.
Xavier Bresson, Thomas Laurent, Residual Gated Graph ConvNets. arXiv 2018. paper.
Kun XuLingfei WuZhiguo WangYansong FengVadim Sheinin, Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks. arXiv 2018. paper.
Xiaojie GuoLingfei WuLiang Zhao. Deep Graph Translation. arXiv 2018. paper.
Choma, Nicholas, et al. Graph Neural Networks for IceCube Signal Classification. ArXiv 2018. paper.
Tyler Derr, Yao Ma, Jiliang Tang. Signed Graph Convolutional Network ArXiv 2018. paper.
Yawei Luo, Tao Guan, Junqing Yu, Ping Liu, Yi Yang. Every Node Counts: Self-Ensembling Graph Convolutional Networks for Semi-Supervised Learning ArXiv 2018. paper.
Sun K, Koniusz P, Wang J. Fisher-Bures Adversary Graph Convolutional Networks. arXiv 2019. paper.
Kazi A, Burwinkel H, Vivar G, et al. InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction. arXiv 2019. paper.
Lemos H, Prates M, Avelar P, et al. Graph Colouring Meets Deep Learning: Effective Graph Neural Network Models for Combinatorial Problems. arXiv 2019. paper.
Diehl F, Brunner T, Le M T, et al. Graph Neural Networks for Modelling Traffic Participant Interaction. arXiv 2019. paper.
Murphy R L, Srinivasan B, Rao V, et al. Relational Pooling for Graph Representations. arXiv 2019. paper.
Zhang W, Shu K, Liu H, et al. Graph Neural Networks for User Identity Linkage. arXiv 2019. paper.
Ruiz L, Gama F, Ribeiro A. Gated Graph Convolutional Recurrent Neural Networks. arXiv 2019. paper.
Phillips S, Daniilidis K. All Graphs Lead to Rome: Learning Geometric and Cycle-Consistent Representations with Graph Convolutional Networks. arXiv 2019. paper.
Hu F, Zhu Y, Wu S, et al. Semi-supervised Node Classification via Hierarchical Graph Convolutional Networks. arXiv 2019. paper.
Deng Z, Dong Y, Zhu J. Batch Virtual Adversarial Training for Graph Convolutional Networks. arXiv 2019. paper.
Chen Z M, Wei X S, Wang P, et al.Multi-Label Image Recognition with Graph Convolutional Networks. arXiv 2019. paper.
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GNN相關的一些開源平臺
Deep Graph Library（DGL)
DGL由紐約大學、紐約大學上海分校、AWS上海研究所和AWS MXNet科學小組開發和維護GNN平臺。
開始時間: 2018.
地址: https://www.dgl.ai/,
github地址:https://github.com/jermainewang/dgl
NGra
NGra是由北京大學和微軟亞洲研究院開發和維護一款GNN平臺。
開始時間:2018
地址: https://arxiv.org/pdf/1810.08403.pdf
Graph_nets
Graph_nets是由DeepMind, Google Corp開發和維護的.
開始時間:2018
地址: https://github.com/deepmind/graph_nets
Euler
Euler是一款由阿裡巴巴旗下的阿裡媽媽開源的GNN平臺.
開始時間:2019
地址: https://github.com/alibaba/euler
PyTorch Geometric
PyTorch Geometric由德國杜特蒙德大學開發和維護的GNN平臺。
開始時間:2019
地址:https://github.com/rusty1s/pytorch_geometric
論文：https://arxiv.org/abs/1903.02428?context=cs.LG
PyTorch-BigGraph（PBG）
PBG是由Facebook人工智慧研究開發和維護的GNN平臺。
開始時間:2019
地址: https://github.com/facebookresearch/PyTorch-BigGraph
論文：https://arxiv.org/abs/1903.12287
一些開胃小菜
超高維網路/圖形結構空間的藝術展
社交網路圖
生物網路之美
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