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圖神經網絡(GNN)必讀論文及最新進展跟蹤

    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。

    本文內容整理自網絡,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,例如社交網絡、蛋白質相互作用網絡、大腦網絡、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絡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—圖捲積網絡(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.

    Mallea M D G, Meltzer P, Bentley P J. Capsule Neural Networks for Graph Classification using Explicit Tensorial Graph Representations. arXiv 2019. paper.

    Peter Meltzer, Marcelo Daniel Gutierrez Mallea and Peter J. Bentley. PiNet: A Permutation Invariant Graph Neural Network for Graph Classification. arXiv 2019. paper.

    Padraig Corcoran. Function Space Pooling For Graph Convolutional Networks. arXiv 2019. paper.

    Sbastien Lerique, Jacob Levy Abitbol, and Mrton Karsai. Joint embedding of structure and features via graph convolutional networks. arXiv 2019. paper.

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

超高維網絡/圖形結構空間的藝術展

社交網絡圖

生物網絡之美


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