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转自 | AI研习社
作者｜Zonghan Wu
这是一个与图神经网络相关的资源集合。相关资源浏览下方Github项目地址，再点击对应链接跳转下载。
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Github项目地址：
https://github.com/nnzhan/Awesome-Graph-Neural-Networks
02
调查报告
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.pdf
Geometric deep learning: going beyond euclidean data. Michael M. Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, Pierre Vandergheynst. 2016.
https://arxiv.org/pdf/1611.08097.pdf
Relational 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.pdf
Attention models in graphs. John Boaz Lee, Ryan A. Rossi, Sungchul Kim, Nesreen K. Ahmed, Eunyee Koh. 2018.
https://arxiv.org/pdf/1807.07984.pdf
Deep learning on graphs: A survey. Ziwei Zhang, Peng Cui and Wenwu Zhu. 2018.
https://arxiv.org/pdf/1812.04202.pdf
Graph 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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论文
01
图卷积网络
A new model for learning in graph domains. Marco Gori, Gabriele Monfardini, Franco Scarselli. IJCNN 2005.
https://ieeexplore.ieee.org/abstract/document/1555942
The 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=pdf
Spectral networks and locally connected networks on graphs. Joan Bruna, Wojciech Zaremba, Arthur Szlam, Yann LeCun. ICLR 2014.
https://arxiv.org/pdf/1312.6203.pdf
Convolutional 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.pdf
Gated graph sequence neural networks. Yujia Li, Richard Zemel, Marc Brockschmidt, Daniel Tarlow. ICLR 2015.
https://arxiv.org/pdf/1511.05493.pdf
Accelerated filtering on graphs using lanczos method. Ana Susnjara, Nathanael Perraudin, Daniel Kressner, Pierre Vandergheynst. 2015.
https://arxiv.org/pdf/1509.04537.pdf
Deep convolutional networks on graph-structured data. Mikael Henaff, Joan Bruna, Yann LeCun. 2015.
https://arxiv.org/abs/1506.05163
Convolutional neural networks on graphs with fast localized spectral filtering. Michaël Defferrard, Xavier Bresson, Pierre Vandergheynst. NIPS 2016.
https://arxiv.org/pdf/1606.09375.pdf
Diffusion-convolutional neural networks James Atwood, Don Towsley. NIPS 2016.
https://arxiv.org/pdf/1511.02136.pdf
Learning convolutional neural networks for graphs. Mathias Niepert, Mohamed Ahmed, Konstantin Kutzkov. ICML 2016.
https://arxiv.org/pdf/1605.05273.pdf
Molecular graph convolutions: moving beyond fingerprints. Steven Kearnes, Kevin McCloskey, Marc Berndl, Vijay Pande, Patrick Riley 2016.
https://arxiv.org/pdf/1603.00856.pdf
Inductive 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.pdf
Neural 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.pdf
Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs Martin Simonovsky, Nikos KomodakisCVPR 2017.
https://arxiv.org/pdf/1704.02901.pdf
Geometric 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.pdf
Semi-supervised classification with graph convolutional networks. Thomas N. Kipf, Max Welling. ICLR 2017.
https://arxiv.org/pdf/1609.02907.pdf
Robust 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/1703.00792
Cayleynets: 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.pdf
Hierarchical 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.pdf
Structure-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.pdf
Adaptive graph convolutional neural networks. Ruoyu Li, Sheng Wang, Feiyun Zhu, Junzhou Huang. AAAI 2018.
https://arxiv.org/pdf/1801.03226.pdf
Deeper insights into graph convolutional networks for semi-supervised learning. Qimai Li, Zhichao Han, Xiao-Ming Wu. AAAI 2018.
https://arxiv.org/pdf/1801.07606.pdf
Large-Scale Learnable Graph Convolutional Networks. Hongyang Gao, Zhengyang Wang, Shuiwang Ji. KDD 2018.
https://arxiv.org/pdf/1808.03965.pdf
FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling. Jie Chen, Tengfei Ma, Cao Xiao.ICLR 2018.
https://arxiv.org/pdf/1801.10247.pdf
Learning 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.pdf
Representation 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.pdf
Stochastic Training of Graph Convolutional Networks with Variance Reduction. Jianfei Chen, Jun Zhu, Le Song. ICML 2018.
https://arxiv.org/pdf/1710.10568.pdf
Dual 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_ba22bb40f3bc41441d1fea0606eb8adb
Graph capsule convolutional neural networks Saurabh Verma, Zhi-Li Zhang. 2018.
https://arxiv.org/abs/1805.08090
How powerful are graph neural networks? Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka. 2018.
https://arxiv.org/pdf/1810.00826.pdf
Modeling 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.pdf
Multidimensional graph convolutional networks Yao Ma, Suhang Wang, Charu C. Aggarwal, Dawei Yin, Jiliang Tang.2018.
https://arxiv.org/pdf/1808.06099.pdf
Signed graph convolutional network. Tyler Derr, Yao Ma, Jiliang Tang. 2018.
https://arxiv.org/pdf/1808.06354.pdf
Capsule Graph Neural Network Zhang Xinyi, Lihui Chen. ICLR 2019.
https://openreview.net/pdf?id=Byl8BnRcYm
Combining Neural Networks with Personalized PageRank for Classification on Graphs Johannes Klicpera, Aleksandar Bojchevski, Stephan Günnemann. ICLR 2019.
https://openreview.net/pdf?id=H1gL-2A9Ym
DIFFUSION SCATTERING TRANSFORMS ON GRAPHS. Fernando Gama, Alejandro Ribeiro, Joan Bruna. ICLR 2019.
https://arxiv.org/pdf/1806.08829.pdf
Graph Wavelet Neural Network. Bingbing Xu, Huawei Shen, Qi Cao, Yunqi Qiu, Xueqi Cheng. ICLR 2019.
https://openreview.net/pdf?id=H1ewdiR5tQ
LanczosNet: Multi-Scale Deep Graph Convolutional Networks Renjie Liao, Zhizhen Zhao, Raquel Urtasun, Richard Zemel. ICLR 2019.
https://openreview.net/pdf?id=BkedznAqKQ
Bayesian Graph Convolutional Neural Networks for Semi-supervised Classification Yingxue Zhang, Soumyasundar Pal, Mark Coates, Deniz Üstebay. AAAI 2019.
https://arxiv.org/pdf/1811.11103.pdf
Geniepath: 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.pdf
Hypergraph Neural Networks. Yifan Feng, Haoxuan You, Zizhao Zhang, Rongrong Ji, Yue Gao AAAI 2019.
https://arxiv.org/pdf/1809.09401.pdf
Weisfeiler 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.pdf
Can GCNs Go as Deep as CNNs?. Guohao Li, Matthias Müller, Ali Thabet, Bernard Ghanem. 2019.
https://arxiv.org/abs/1904.03751
02
图的注意力模型
Graph Attention Networks. Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, Yoshua Bengio. ICLR 2018.
https://arxiv.org/pdf/1710.10903.pdf
Gaan: 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.pdf
Watch 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.pdf
Graph classification using structural attention. John Boaz Lee, Ryan Rossi, Xiangnan Kong KDD 2018.
https://dl.acm.org/citation.cfm?id=3219980
03
图的自动编码器
Structural deep network embedding Daixin Wang, Peng Cui, Wenwu Zhu.
https://www.kdd.org/kdd2016/papers/files/rfp0191-wangAemb.pdf
Deep 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/12423/11715
Variational graph auto-encoders. Thomas N. Kipf, Max Welling. 2016.
https://arxiv.org/pdf/1611.07308.pdf
Mgae: 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.pdf
Link Prediction Based on Graph Neural Networks. Muhan Zhang, Yixin Chen. NeurIPS 2018.
https://arxiv.org/pdf/1802.09691.pdf
SpectralNet: 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.pdf
Deep 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.pdf
Learning 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.pdf
Adversarially 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.pdf
Deep graph infomax. Petar Veličković, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, R Devon Hjelm.ICLR 2019.
https://arxiv.org/abs/1809.10341
04
图生成网络
Learning graphical state transitions. Daniel D. Johnson. ICLR 2016.
https://openreview.net/pdf?id=HJ0NvFzxl
MolGAN: An implicit generative model for small molecular graphs. Nicola De Cao, Thomas Kipf. 2018.
https://arxiv.org/pdf/1805.11973.pdf
Learning deep generative models of graphs. Yujia Li, Oriol Vinyals, Chris Dyer, Razvan Pascanu, Peter Battaglia. ICML 2018.
https://arxiv.org/abs/1803.03324
Netgan: Generating graphs via random walks. Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, Stephan Günnemann. ICML 2018.
https://arxiv.org/pdf/1803.00816.pdf
Graphrnn: 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.pdf
Constrained 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.pdf
Graph 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
05
图时空网络
Structured sequence modeling with graph convolutional recurrent networks. Youngjoo Seo, Michaël Defferrard, Pierre Vandergheynst, Xavier Bresson. 2016.
https://arxiv.org/pdf/1612.07659.pdf
Structural-rnn: Deep learning on spatio-temporal graphs. Ashesh Jain, Amir R. Zamir, Silvio Savarese, Ashutosh Saxena.CVPR 2016.
https://arxiv.org/abs/1511.05298
Deep 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/1802.08714
Spatial temporal graph convolutional networks for skeleton-based action recognition. Sijie Yan, Yuanjun Xiong, Dahua Lin. AAAI 2018.
https://arxiv.org/abs/1801.07455
Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. Yaguang Li, Rose Yu, Cyrus Shahabi, Yan Liu. ICLR 2018.
https://arxiv.org/pdf/1707.01926.pdf
Spatio-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.pdf
Attention 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.pdf
Spatiotemporal 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.pdf
Spatio-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
04
各领域的应用
01
计算机视觉（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.pdf
Syncspeccnn: Synchronized spectral cnn for 3d shape segmentation. Li Yi, Hao Su, Xingwen Guo, Leonidas Guibas.CVPR 2017.
https://arxiv.org/pdf/1612.00606.pdf
A 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.pdf
Situation Recognition with Graph Neural Networks. Ruiyu Li, Makarand Tapaswi, Renjie Liao, Jiaya Jia, Raquel Urtasun, Sanja Fidler. ICCV 2017.
https://arxiv.org/pdf/1708.04320
Image generation from scene graphs. Justin Johnson, Agrim Gupta, Li Fei-Fei. CVPR 2018.
https://arxiv.org/pdf/1804.01622.pdf
PointNet: 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.pdf
Iterative visual reasoning beyond convolutions. Xinlei Chen, Li-Jia Li, Li Fei-Fei, Abhinav Gupta. CVPR 2018.
https://arxiv.org/pdf/1803.11189.pdf
Large-scale point cloud semantic segmentation with superpoint graphs. Loic Landrieu, Martin Simonovsky. CVPR 2018.
https://arxiv.org/pdf/1711.09869.pdf
Learning Conditioned Graph Structures for Interpretable Visual Question Answering. Will Norcliffe-Brown, Efstathios Vafeias, Sarah Parisot. NeurIPS 2018.
https://arxiv.org/pdf/1806.07243
Out 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.pdf
Symbolic 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.pdf
Few-shot learning with graph neural networks. Victor Garcia, Joan Bruna. ICLR 2018.
https://arxiv.org/abs/1711.04043
Factorizable 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/1806.11538
Graph r-cnn for scene graph generation. Jianwei Yang, Jiasen Lu, Stefan Lee, Dhruv Batra, Devi Parikh. ECCV 2018.
https://arxiv.org/pdf/1808.00191.pdf
Learning 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.pdf
Neural 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.pdf
Rgcnn: Regularized graph cnn for point cloud segmentation. Gusi Te, Wei Hu, Zongming Guo, Amin Zheng. 2018.
https://arxiv.org/pdf/1806.02952.pdf
Dynamic 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
自然语言处理（NLP）
Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling. Diego Marcheggiani, Ivan Titov.EMNLP 2017.
https://arxiv.org/abs/1703.04826
Graph 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/1704.04675
Diffusion maps for textual network embedding. Xinyuan Zhang, Yitong Li, Dinghan Shen, Lawrence Carin. NeurIPS 2018.
https://arxiv.org/pdf/1805.09906.pdf
A Graph-to-Sequence Model for AMR-to-Text Generation. Linfeng Song, Yue Zhang, Zhiguo Wang, Daniel Gildea. ACL 2018.
https://arxiv.org/abs/1805.02473
Graph-to-Sequence Learning using Gated Graph Neural Networks. Daniel Beck, Gholamreza Haffari, Trevor Cohn. ACL 2018.
https://arxiv.org/pdf/1806.09835.pdf
Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks. Zhichun Wang, Qingsong Lv, Xiaohan Lan, Yu Zhang. EMNLP 2018.
http://www.aclweb.org/anthology/D18-1032
Graph Convolution over Pruned Dependency Trees Improves Relation Extraction. Yuhao Zhang, Peng Qi, Christopher D. Manning. EMNLP 2018.
https://arxiv.org/pdf/1809.10185
Multiple Events Extraction via Attention-based Graph Information Aggregation. Xiao Liu, Zhunchen Luo, Heyan Huang.EMNLP 2018.
https://arxiv.org/pdf/1809.09078.pdf
Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks. Diego Marcheggiani, Joost Bastings, Ivan Titov. NAACL 2018.
http://www.aclweb.org/anthology/N18-2078
Graph Convolutional Networks for Text Classification. Liang Yao, Chengsheng Mao, Yuan Luo. AAAI 2019.
https://arxiv.org/pdf/1809.05679.pdf
03
互联网
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.pdf
Semi-supervised User Geolocation via Graph Convolutional Networks. Afshin Rahimi, Trevor Cohn, Timothy Baldwin.ACL 2018.
https://arxiv.org/pdf/1804.08049.pdf
Adversarial attacks on neural networks for graph data. Daniel Zügner, Amir Akbarnejad, Stephan Günnemann. KDD 2018.
https://arxiv.org/pdf/1805.07984.pdf
Deepinf: 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
推荐系统
Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks. Federico Monti, Michael M. Bronstein, Xavier Bresson. NIPS 2017.
https://arxiv.org/abs/1704.06803
Graph Convolutional Matrix Completion. Rianne van den Berg, Thomas N. Kipf, Max Welling. 2017.
https://arxiv.org/abs/1706.02263
Graph 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.pdf
Session-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
医疗健康
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.pdf
MILE: A Multi-Level Framework for Scalable Graph Embedding. Jiongqian Liang, Saket Gurukar, Srinivasan Parthasarathy.
https://arxiv.org/pdf/1802.09612.pdf
Hybrid 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/1711.05859
GAMENet: 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
化学
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.pdf
Protein 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.pdf
Modeling polypharmacy side effects with graph convolutional networks. Marinka Zitnik, Monica Agrawal, Jure Leskovec. ISMB 2018.
https://arxiv.org/abs/1802.00543
07
物理学
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.pdf
Vain: Attentional multi-agent predictive modeling. Yedid Hoshen. NIPS 2017
https://arxiv.org/pdf/1706.06122.pdf
08
其他领域
Learning to represent programs with graphs. Miltiadis Allamanis, Marc Brockschmidt, Mahmoud Khademi. ICLR 2017.
https://arxiv.org/pdf/1711.00740.pdf
Combinatorial 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.pdf
Recurrent Relational Networks. Rasmus Palm, Ulrich Paquet, Ole Winther. NeurIPS 2018.
http://papers.nips.cc/paper/7597-recurrent-relational-networks.pdf
NerveNet: Learning Structured Policy with Graph Neural Networks. Tingwu Wang, Renjie Liao, Jimmy Ba, Sanja Fidler.ICLR 2018.
https://openreview.net/pdf?id=S1sqHMZCb
05
文库
pytorch geometric（Pytorch几何）
https://github.com/rusty1s/pytorch_geometric
deep graph library（深度图像库）
https://github.com/dmlc/dgl
graph nets library（图像网络库）
https://github.com/deepmind/graph_nets
星标我，每天多一点智慧
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