Github項目推薦-圖神經網絡(GNN)相關資源大列表

文章發佈於公號【數智物語】 （ID：decision_engine），關注公號不錯過每一篇乾貨。

轉自 | AI研習社

作者｜Zonghan Wu

這是一個與圖神經網絡相關的資源集合。相關資源瀏覽下方Github項目地址，再點擊對應鏈接跳轉下載。

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Github項目地址：

https://github.com/nnzhan/Awesome-Graph-Neural-Networks

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調查報告

A Comprehensive Survey on Graph Neural Networks. Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, Philip S. Yu. 2019 https://arxiv.org/pdf/1901.00596.pdfGeometric deep learning: going beyond euclidean data. Michael M. Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, Pierre Vandergheynst. 2016. https://arxiv.org/pdf/1611.08097.pdfRelational inductive biases, deep learning, and graph networks. Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, Caglar Gulcehre, Francis Song, Andrew Ballard, Justin Gilmer, George Dahl, Ashish Vaswani, Kelsey Allen, Charles Nash, Victoria Langston, Chris Dyer, Nicolas Heess, Daan Wierstra, Pushmeet Kohli, Matt Botvinick, Oriol Vinyals, Yujia Li, Razvan Pascanu. 2018. https://arxiv.org/pdf/1806.01261.pdfAttention models in graphs. John Boaz Lee, Ryan A. Rossi, Sungchul Kim, Nesreen K. Ahmed, Eunyee Koh. 2018. https://arxiv.org/pdf/1807.07984.pdfDeep learning on graphs: A survey. Ziwei Zhang, Peng Cui and Wenwu Zhu. 2018. https://arxiv.org/pdf/1812.04202.pdfGraph Neural Networks: A Review of Methods and Applications Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Maosong Sun. 2018 https://arxiv.org/pdf/1812.08434.pdf

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論文

01

圖卷積網絡

A new model for learning in graph domains. Marco Gori, Gabriele Monfardini, Franco Scarselli. IJCNN 2005. https://ieeexplore.ieee.org/abstract/document/1555942The graph neural network model. Franco Scarselli,Marco Gori,Ah Chung Tsoi,Markus Hagenbuchner, Gabriele Monfardini.2009. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.1015.7227&rep=rep1&type=pdfSpectral networks and locally connected networks on graphs. Joan Bruna, Wojciech Zaremba, Arthur Szlam, Yann LeCun. ICLR 2014. https://arxiv.org/pdf/1312.6203.pdfConvolutional networks on graphs for learning molecular fingerprints. David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre Rafael Go ́mez-Bombarelli, Timothy Hirzel, Ala ́n Aspuru-Guzik, Ryan P. Adams., NIPS 2015. http://papers.nips.cc/paper/5954-convolutional-networks-on-graphs-for-learning-molecular-fingerprints.pdfGated graph sequence neural networks. Yujia Li, Richard Zemel, Marc Brockschmidt, Daniel Tarlow. ICLR 2015. https://arxiv.org/pdf/1511.05493.pdfAccelerated filtering on graphs using lanczos method. Ana Susnjara, Nathanael Perraudin, Daniel Kressner, Pierre Vandergheynst. 2015. https://arxiv.org/pdf/1509.04537.pdfDeep convolutional networks on graph-structured data. Mikael Henaff, Joan Bruna, Yann LeCun. 2015. https://arxiv.org/abs/1506.05163Convolutional neural networks on graphs with fast localized spectral filtering. Michaël Defferrard, Xavier Bresson, Pierre Vandergheynst. NIPS 2016. https://arxiv.org/pdf/1606.09375.pdfDiffusion-convolutional neural networks James Atwood, Don Towsley. NIPS 2016. https://arxiv.org/pdf/1511.02136.pdfLearning convolutional neural networks for graphs. Mathias Niepert, Mohamed Ahmed, Konstantin Kutzkov. ICML 2016. https://arxiv.org/pdf/1605.05273.pdfMolecular graph convolutions: moving beyond fingerprints. Steven Kearnes, Kevin McCloskey, Marc Berndl, Vijay Pande, Patrick Riley 2016. https://arxiv.org/pdf/1603.00856.pdfInductive representation learning on large graphs. William L. Hamilton, Rex Ying, Jure Leskovec. NIPS 2017. http://papers.nips.cc/paper/6703-inductive-representation-learning-on-large-graphs.pdfNeural message passing for quantum chemistry. Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, George E. Dahl. ICML 2017. https://arxiv.org/pdf/1704.01212.pdfDynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs Martin Simonovsky, Nikos KomodakisCVPR 2017. https://arxiv.org/pdf/1704.02901.pdfGeometric 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. https://arxiv.org/pdf/1611.08402.pdfSemi-supervised classification with graph convolutional networks. Thomas N. Kipf, Max Welling. ICLR 2017. https://arxiv.org/pdf/1609.02907.pdfRobust spatial filtering with graph convolutional neural networks. 2017. Felipe Petroski Such, Shagan Sah, Miguel Dominguez, Suhas Pillai, Chao Zhang, Andrew Michael, Nathan Cahill, Raymond Ptucha. https://arxiv.org/abs/1703.00792Cayleynets: graph convolutional neural networks with complex rational spectral filters. Ron Levie, Federico Monti, Xavier Bresson, Michael M. Bronstein. 2017. https://arxiv.org/pdf/1705.07664.pdfHierarchical graph representation learning with differentiable pooling. Rex Ying, Jiaxuan You, Christopher Morris, Xiang Ren, William L. Hamilton, Jure Leskovec. NeurIPS 2018. https://arxiv.org/pdf/1806.08804.pdfStructure-Aware Convolutional Neural Networks. Jianlong Chang, Jie Gu, Lingfeng Wang, Gaofeng Meng, Shiming Xiang, Chunhong Pan. NeurIPS 2018. http://papers.nips.cc/paper/7287-structure-aware-convolutional-neural-networks.pdfAdaptive graph convolutional neural networks. Ruoyu Li, Sheng Wang, Feiyun Zhu, Junzhou Huang. AAAI 2018. https://arxiv.org/pdf/1801.03226.pdfDeeper insights into graph convolutional networks for semi-supervised learning. Qimai Li, Zhichao Han, Xiao-Ming Wu. AAAI 2018. https://arxiv.org/pdf/1801.07606.pdfLarge-Scale Learnable Graph Convolutional Networks. Hongyang Gao, Zhengyang Wang, Shuiwang Ji. KDD 2018. https://arxiv.org/pdf/1808.03965.pdfFastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling. Jie Chen, Tengfei Ma, Cao Xiao.ICLR 2018. https://arxiv.org/pdf/1801.10247.pdfLearning steady-states of iterative algorithms over graphs. Hanjun Dai, Zornitsa Kozareva, Bo Dai, Alexander J. Smola, Le Song ICML 2018. http://proceedings.mlr.press/v80/dai18a/dai18a.pdfRepresentation learning on graphs with jumping knowledge networks. Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, Stefanie Jegelka. ICML 2018. https://arxiv.org/pdf/1806.03536.pdfStochastic Training of Graph Convolutional Networks with Variance Reduction. Jianfei Chen, Jun Zhu, Le Song. ICML 2018. https://arxiv.org/pdf/1710.10568.pdfDual graph convolutional networks for graph-based semi-supervised classification Chenyi Zhuang, Qiang Ma. WWW 2018. http://delivery.acm.org/10.1145/3190000/3186116/p499-zhuang.pdf?ip=1.129.110.137&id=3186116&acc=OPEN&key=4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E6D218144511F3437&__acm__=1546208231_ba22bb40f3bc41441d1fea0606eb8adbGraph capsule convolutional neural networks Saurabh Verma, Zhi-Li Zhang. 2018. https://arxiv.org/abs/1805.08090How powerful are graph neural networks? Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka. 2018. https://arxiv.org/pdf/1810.00826.pdfModeling relational data with graph convolutional networks Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, Max Welling. ESW 2018. https://arxiv.org/pdf/1703.06103.pdfMultidimensional graph convolutional networks Yao Ma, Suhang Wang, Charu C. Aggarwal, Dawei Yin, Jiliang Tang.2018. https://arxiv.org/pdf/1808.06099.pdfSigned graph convolutional network. Tyler Derr, Yao Ma, Jiliang Tang. 2018. https://arxiv.org/pdf/1808.06354.pdfCapsule Graph Neural Network Zhang Xinyi, Lihui Chen. ICLR 2019. https://openreview.net/pdf?id=Byl8BnRcYmCombining Neural Networks with Personalized PageRank for Classification on Graphs Johannes Klicpera, Aleksandar Bojchevski, Stephan Günnemann. ICLR 2019. https://openreview.net/pdf?id=H1gL-2A9YmDIFFUSION SCATTERING TRANSFORMS ON GRAPHS. Fernando Gama, Alejandro Ribeiro, Joan Bruna. ICLR 2019. https://arxiv.org/pdf/1806.08829.pdfGraph Wavelet Neural Network. Bingbing Xu, Huawei Shen, Qi Cao, Yunqi Qiu, Xueqi Cheng. ICLR 2019. https://openreview.net/pdf?id=H1ewdiR5tQLanczosNet: Multi-Scale Deep Graph Convolutional Networks Renjie Liao, Zhizhen Zhao, Raquel Urtasun, Richard Zemel. ICLR 2019. https://openreview.net/pdf?id=BkedznAqKQBayesian Graph Convolutional Neural Networks for Semi-supervised Classification Yingxue Zhang, Soumyasundar Pal, Mark Coates, Deniz Üstebay. AAAI 2019. https://arxiv.org/pdf/1811.11103.pdfGeniepath: Graph neural networks with adaptive receptive paths. Ziqi Liu, Chaochao Chen, Longfei Li, Jun Zhou, Xiaolong Li, Le Song, Yuan Qi. AAAI 2019. https://arxiv.org/pdf/1802.00910.pdfHypergraph Neural Networks. Yifan Feng, Haoxuan You, Zizhao Zhang, Rongrong Ji, Yue Gao AAAI 2019. https://arxiv.org/pdf/1809.09401.pdfWeisfeiler and Leman Go Neural: Higher-order Graph Neural Networks. Christopher Morris, Martin Ritzert, Matthias Fey, William L. Hamilton, Jan Eric Lenssen, Gaurav Rattan, Martin Grohe AAAI 2019. https://arxiv.org/pdf/1810.02244.pdfCan GCNs Go as Deep as CNNs?. Guohao Li, Matthias Müller, Ali Thabet, Bernard Ghanem. 2019. https://arxiv.org/abs/1904.03751

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圖的注意力模型

Graph Attention Networks. Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, Yoshua Bengio. ICLR 2018. https://arxiv.org/pdf/1710.10903.pdfGaan: Gated attention networks for learning on large and spatiotemporal graphs. Jiani Zhang, Xingjian Shi, Junyuan Xie, Hao Ma, Irwin King, Dit-Yan Yeung. 2018. https://arxiv.org/pdf/1803.07294.pdfWatch your step: Learning node embeddings via graph attention. Sami Abu-El-Haija, Bryan Perozzi, Rami Al-Rfou, Alex Alemi. NeurIPS 2018. https://arxiv.org/pdf/1710.09599.pdfGraph classification using structural attention. John Boaz Lee, Ryan Rossi, Xiangnan Kong KDD 2018. https://dl.acm.org/citation.cfm?id=3219980

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圖的自動編碼器

Structural deep network embedding Daixin Wang, Peng Cui, Wenwu Zhu. https://www.kdd.org/kdd2016/papers/files/rfp0191-wangAemb.pdfDeep neural networks for learning graph representations. Shaosheng Cao, Wei Lu, Qiongkai Xu. AAAI 2016. https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/12423/11715Variational graph auto-encoders. Thomas N. Kipf, Max Welling. 2016. https://arxiv.org/pdf/1611.07308.pdfMgae: Marginalized graph autoencoder for graph clustering Chun Wang, Shirui Pan, Guodong Long, Xingquan Zhu, Jing Jiang. CIKM 2017. https://shiruipan.github.io/pdf/CIKM-17-Wang.pdfLink Prediction Based on Graph Neural Networks. Muhan Zhang, Yixin Chen. NeurIPS 2018. https://arxiv.org/pdf/1802.09691.pdfSpectralNet: Spectral Clustering using Deep Neural Networks Uri Shaham, Kelly Stanton, Henry Li, Boaz Nadler, Ronen Basri, Yuval Kluger. ICLR 2018. https://arxiv.org/pdf/1801.01587.pdfDeep Recursive Network Embedding with Regular Equivalence. Ke Tu, Peng Cui, Xiao Wang, Philip S. Yu, Wenwu Zhu.KDD 2018. http://cuip.thumedialab.com/papers/NE-RegularEquivalence.pdfLearning Deep Network Representations with Adversarially Regularized Autoencoders. Wenchao Yu, Cheng Zheng, Wei Cheng, Charu Aggarwal, Dongjin Song, Bo Zong, Haifeng Chen, Wei Wang. KDD 2018. http://www.cs.ucsb.edu/~bzong/doc/kdd-18.pdfAdversarially Regularized Graph Autoencoder for Graph Embedding. Shirui Pan, Ruiqi Hu, Guodong Long, Jing Jiang, Lina Yao, Chengqi Zhang. IJCAI 2018. https://www.ijcai.org/proceedings/2018/0362.pdfDeep graph infomax. Petar Veličković, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, R Devon Hjelm.ICLR 2019. https://arxiv.org/abs/1809.10341

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圖生成網絡

Learning graphical state transitions. Daniel D. Johnson. ICLR 2016. https://openreview.net/pdf?id=HJ0NvFzxlMolGAN: An implicit generative model for small molecular graphs. Nicola De Cao, Thomas Kipf. 2018. https://arxiv.org/pdf/1805.11973.pdfLearning deep generative models of graphs. Yujia Li, Oriol Vinyals, Chris Dyer, Razvan Pascanu, Peter Battaglia. ICML 2018. https://arxiv.org/abs/1803.03324Netgan: Generating graphs via random walks. Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, Stephan Günnemann. ICML 2018. https://arxiv.org/pdf/1803.00816.pdfGraphrnn: A deep generative model for graphs. Jiaxuan You, Rex Ying, Xiang Ren, William L. Hamilton, Jure Leskovec.ICML 2018. https://arxiv.org/pdf/1802.08773.pdfConstrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders. Tengfei Ma, Jie Chen, Cao Xiao. NeurIPS 2018. https://papers.nips.cc/paper/7942-constrained-generation-of-semantically-valid-graphs-via-regularizing-variational-autoencoders.pdfGraph convolutional policy network for goal-directed molecular graph generation. Jiaxuan You, Bowen Liu, Rex Ying, Vijay Pande, Jure Leskovec. NeurIPS 2018. https://arxiv.org/abs/1806.02473

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圖時空網絡

Structured sequence modeling with graph convolutional recurrent networks. Youngjoo Seo, Michaël Defferrard, Pierre Vandergheynst, Xavier Bresson. 2016. https://arxiv.org/pdf/1612.07659.pdfStructural-rnn: Deep learning on spatio-temporal graphs. Ashesh Jain, Amir R. Zamir, Silvio Savarese, Ashutosh Saxena.CVPR 2016. https://arxiv.org/abs/1511.05298Deep multi-view spatial-temporal network for taxi. Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, Zhenhui Li. AAAI 2018. https://arxiv.org/abs/1802.08714Spatial temporal graph convolutional networks for skeleton-based action recognition. Sijie Yan, Yuanjun Xiong, Dahua Lin. AAAI 2018. https://arxiv.org/abs/1801.07455Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. Yaguang Li, Rose Yu, Cyrus Shahabi, Yan Liu. ICLR 2018. https://arxiv.org/pdf/1707.01926.pdfSpatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. Bing Yu, Haoteng Yin, Zhanxing Zhu. IJCAI 2018. https://arxiv.org/pdf/1709.04875.pdfAttention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting. Shengnan Guo, Youfang Lin, Ning Feng, Chao Song, HuaiyuWan AAAI 2019. https://github.com/Davidham3/ASTGCN/blob/master/2019%20AAAI_Attention%20Based%20Spatial-Temporal%20Graph%20Convolutional%20Networks%20for%20Traffic%20Flow%20Forecasting.pdfSpatiotemporal Multi-Graph Convolution Network for Ride-hailing Demand Forecasting. Xu Geng, Yaguang Li, Leye Wang, Lingyu Zhang, Qiang Yang, Jieping Ye, Yan Liu. AAAI 2019. http://www-scf.usc.edu/~yaguang/papers/aaai19_multi_graph_convolution.pdfSpatio-Temporal Graph Routing for Skeleton-based Action Recognition. Bin Li, Xi Li, Zhongfei Zhang, Fei Wu. AAAI 2019. https://www.aaai.org/Papers/AAAI/2019/AAAI-LiBin.6992.pdf

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各領域的應用

01

計算機視覺（CV）

3d graph neural networks for rgbd semantic segmentation. Xiaojuan Qi, Renjie Liao, Jiaya Jia†, Sanja Fidler, Raquel Urtasun. CVPR 2017. http://openaccess.thecvf.com/content_ICCV_2017/papers/Qi_3D_Graph_Neural_ICCV_2017_paper.pdfSyncspeccnn: Synchronized spectral cnn for 3d shape segmentation. Li Yi, Hao Su, Xingwen Guo, Leonidas Guibas.CVPR 2017. https://arxiv.org/pdf/1612.00606.pdfA 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 https://arxiv.org/pdf/1706.01427.pdfSituation Recognition with Graph Neural Networks. Ruiyu Li, Makarand Tapaswi, Renjie Liao, Jiaya Jia, Raquel Urtasun, Sanja Fidler. ICCV 2017. https://arxiv.org/pdf/1708.04320Image generation from scene graphs. Justin Johnson, Agrim Gupta, Li Fei-Fei. CVPR 2018. https://arxiv.org/pdf/1804.01622.pdfPointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. Charles R. Qi, Hao Su, Kaichun Mo, Leonidas J. Guibas. CVPR 2018. https://arxiv.org/pdf/1612.00593.pdfIterative visual reasoning beyond convolutions. Xinlei Chen, Li-Jia Li, Li Fei-Fei, Abhinav Gupta. CVPR 2018. https://arxiv.org/pdf/1803.11189.pdfLarge-scale point cloud semantic segmentation with superpoint graphs. Loic Landrieu, Martin Simonovsky. CVPR 2018. https://arxiv.org/pdf/1711.09869.pdfLearning Conditioned Graph Structures for Interpretable Visual Question Answering. Will Norcliffe-Brown, Efstathios Vafeias, Sarah Parisot. NeurIPS 2018. https://arxiv.org/pdf/1806.07243Out of the box: Reasoning with graph convolution nets for factual visual question answering. Medhini Narasimhan, Svetlana Lazebnik, Alexander G. Schwing. NeurIPS 2018. https://arxiv.org/pdf/1811.00538.pdfSymbolic Graph Reasoning Meets Convolutions. Xiaodan Liang, Zhiting Hu, Hao Zhang, Liang Lin, Eric P. Xing. NeurIPS 2018. http://papers.nips.cc/paper/7456-symbolic-graph-reasoning-meets-convolutions.pdfFew-shot learning with graph neural networks. Victor Garcia, Joan Bruna. ICLR 2018. https://arxiv.org/abs/1711.04043Factorizable net: an efficient subgraph-based framework for scene graph generation. Yikang Li, Wanli Ouyang, Bolei Zhou, Jianping Shi, Chao Zhang, Xiaogang Wang. ECCV 2018. https://arxiv.org/abs/1806.11538Graph r-cnn for scene graph generation. Jianwei Yang, Jiasen Lu, Stefan Lee, Dhruv Batra, Devi Parikh. ECCV 2018. https://arxiv.org/pdf/1808.00191.pdfLearning Human-Object Interactions by Graph Parsing Neural Networks. Siyuan Qi, Wenguan Wang, Baoxiong Jia, Jianbing Shen, Song-Chun Zhu. ECCV 2018. https://arxiv.org/pdf/1808.07962.pdfNeural graph matching networks for fewshot 3d action recognition. Michelle Guo, Edward Chou, De-An Huang, Shuran Song, Serena Yeung, Li Fei-Fei ECCV 2018. http://openaccess.thecvf.com/content_ECCV_2018/papers/Michelle_Guo_Neural_Graph_Matching_ECCV_2018_paper.pdfRgcnn: Regularized graph cnn for point cloud segmentation. Gusi Te, Wei Hu, Zongming Guo, Amin Zheng. 2018. https://arxiv.org/pdf/1806.02952.pdfDynamic graph cnn for learning on point clouds. Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E. Sarma, Michael M. Bronstein, Justin M. Solomon. 2018. https://arxiv.org/pdf/1801.07829.pdf

02

自然語言處理（NLP）

Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling. Diego Marcheggiani, Ivan Titov.EMNLP 2017. https://arxiv.org/abs/1703.04826Graph Convolutional Encoders for Syntax-aware Neural Machine Translation. Joost Bastings, Ivan Titov, Wilker Aziz, Diego Marcheggiani, Khalil Sima'an. EMNLP 2017. https://arxiv.org/pdf/1704.04675Diffusion maps for textual network embedding. Xinyuan Zhang, Yitong Li, Dinghan Shen, Lawrence Carin. NeurIPS 2018. https://arxiv.org/pdf/1805.09906.pdfA Graph-to-Sequence Model for AMR-to-Text Generation. Linfeng Song, Yue Zhang, Zhiguo Wang, Daniel Gildea. ACL 2018. https://arxiv.org/abs/1805.02473Graph-to-Sequence Learning using Gated Graph Neural Networks. Daniel Beck, Gholamreza Haffari, Trevor Cohn. ACL 2018. https://arxiv.org/pdf/1806.09835.pdfCross-lingual Knowledge Graph Alignment via Graph Convolutional Networks. Zhichun Wang, Qingsong Lv, Xiaohan Lan, Yu Zhang. EMNLP 2018. http://www.aclweb.org/anthology/D18-1032Graph Convolution over Pruned Dependency Trees Improves Relation Extraction. Yuhao Zhang, Peng Qi, Christopher D. Manning. EMNLP 2018. https://arxiv.org/pdf/1809.10185Multiple Events Extraction via Attention-based Graph Information Aggregation. Xiao Liu, Zhunchen Luo, Heyan Huang.EMNLP 2018. https://arxiv.org/pdf/1809.09078.pdfExploiting Semantics in Neural Machine Translation with Graph Convolutional Networks. Diego Marcheggiani, Joost Bastings, Ivan Titov. NAACL 2018. http://www.aclweb.org/anthology/N18-2078Graph Convolutional Networks for Text Classification. Liang Yao, Chengsheng Mao, Yuan Luo. AAAI 2019. https://arxiv.org/pdf/1809.05679.pdf

03

互聯網

Graph Convolutional Networks with Argument-Aware Pooling for Event Detection. Thien Huu Nguyen, Ralph Grishman. AAAI 2018. http://ix.cs.uoregon.edu/~thien/pubs/graphConv.pdfSemi-supervised User Geolocation via Graph Convolutional Networks. Afshin Rahimi, Trevor Cohn, Timothy Baldwin.ACL 2018. https://arxiv.org/pdf/1804.08049.pdfAdversarial attacks on neural networks for graph data. Daniel Zügner, Amir Akbarnejad, Stephan Günnemann. KDD 2018. https://arxiv.org/pdf/1805.07984.pdfDeepinf: Social influence prediction with deep learning. Jiezhong Qiu, Jian Tang, Hao Ma, Yuxiao Dong, Kuansan Wang, Jie Tang. KDD 2018. https://arxiv.org/pdf/1807.05560.pdf

04

推薦系統

Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks. Federico Monti, Michael M. Bronstein, Xavier Bresson. NIPS 2017. https://arxiv.org/abs/1704.06803Graph Convolutional Matrix Completion. Rianne van den Berg, Thomas N. Kipf, Max Welling. 2017. https://arxiv.org/abs/1706.02263Graph Convolutional Neural Networks for Web-Scale Recommender Systems. Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, Jure Leskovec. KDD 2018. https://arxiv.org/pdf/1806.01973.pdfSession-based Recommendation with Graph Neural Networks. Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, Tieniu Tan. AAAI 2019. https://arxiv.org/pdf/1811.00855.pdf

05

醫療健康

Gram:graph-based attention model for healthcare representation learning Edward Choi, Mohammad Taha Bahadori, Le Song, Walter F. Stewart, Jimeng Sun. KDD 2017. https://arxiv.org/pdf/1611.07012.pdfMILE: A Multi-Level Framework for Scalable Graph Embedding. Jiongqian Liang, Saket Gurukar, Srinivasan Parthasarathy. https://arxiv.org/pdf/1802.09612.pdfHybrid Approach of Relation Network and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classification. Sungmin Rhee, Seokjun Seo, Sun Kim. IJCAI 2018. https://arxiv.org/abs/1711.05859GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination. Junyuan Shang, Cao Xiao, Tengfei Ma, Hongyan Li, Jimeng Sun. AAAI 2019. https://arxiv.org/pdf/1809.01852.pdf

06

化學

Molecular Graph Convolutions: Moving Beyond Fingerprints. Steven Kearnes, Kevin McCloskey, Marc Berndl, Vijay Pande, Patrick Riley. Journal of computer-aided molecular design 2016. https://arxiv.org/pdf/1603.00856.pdfProtein interface prediction using graph convolutional networks. Alex Fout, Jonathon Byrd, Basir Shariat, Asa Ben-Hur.NIPS 2017. https://papers.nips.cc/paper/7231-protein-interface-prediction-using-graph-convolutional-networks.pdfModeling polypharmacy side effects with graph convolutional networks. Marinka Zitnik, Monica Agrawal, Jure Leskovec. ISMB 2018. https://arxiv.org/abs/1802.00543

07

物理學

Interaction Networks for Learning about Objects, Relations and Physics. Peter Battaglia, Razvan Pascanu, Matthew Lai, Danilo Rezende, Koray Kavukcuoglu. NIPS 2016. https://arxiv.org/pdf/1612.00222.pdfVain: Attentional multi-agent predictive modeling. Yedid Hoshen. NIPS 2017 https://arxiv.org/pdf/1706.06122.pdf

08

其他領域

Learning to represent programs with graphs. Miltiadis Allamanis, Marc Brockschmidt, Mahmoud Khademi. ICLR 2017. https://arxiv.org/pdf/1711.00740.pdfCombinatorial Optimization with Graph Convolutional Networks and Guided Tree Search. Zhuwen Li, Qifeng Chen, Vladlen Koltun. NeurIPS 2018. http://papers.nips.cc/paper/7335-combinatorial-optimization-with-graph-convolutional-networks-and-guided-tree-search.pdfRecurrent Relational Networks. Rasmus Palm, Ulrich Paquet, Ole Winther. NeurIPS 2018. http://papers.nips.cc/paper/7597-recurrent-relational-networks.pdfNerveNet: Learning Structured Policy with Graph Neural Networks. Tingwu Wang, Renjie Liao, Jimmy Ba, Sanja Fidler.ICLR 2018. https://openreview.net/pdf?id=S1sqHMZCb

05

文庫

pytorch geometric（Pytorch幾何）https://github.com/rusty1s/pytorch_geometricdeep graph library（深度圖像庫）https://github.com/dmlc/dglgraph nets library（圖像網絡庫）https://github.com/deepmind/graph_nets

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