Literature of Deep Learning for Graphs

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This is a paper list about deep learning for graphs.

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Node Representation Learning

Unsupervised Node Representation Learning

DeepWalk: Online Learning of Social Representations <https://arxiv.org/pdf/1403.6652>_ | :authors:Bryan Perozzi, Rami Al-Rfou, Steven Skiena | :venue:KDD 2014 | :keywords:Node classification, Random walk, Skip-gram

LINE: Large-scale Information Network Embedding <https://arxiv.org/pdf/1503.03578>_ | :authors:Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, Qiaozhu Mei | :venue:WWW 2015 | :keywords:First-order, Second-order, Node classification

GraRep: Learning Graph Representations with Global Structural Information <https://dl.acm.org/citation.cfm?id=2806512>_ | :authors:Shaosheng Cao, Wei Lu, Qiongkai Xu | :venue:CIKM 2015 | :keywords:High-order, SVD

node2vec: Scalable Feature Learning for Networks <https://arxiv.org/pdf/1607.00653>_ | :authors:Aditya Grover, Jure Leskovec | :venue:KDD 2016 | :keywords:Breadth-first Search, Depth-first Search, Node Classification, Link Prediction

Variational Graph Auto-Encoders <https://arxiv.org/abs/1611.07308>_ | :authors:Thomas N. Kipf, Max Welling | :venue:arXiv 2016

Scalable Graph Embedding for Asymmetric Proximity <https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14696>_ | :authors:Chang Zhou, Yuqiong Liu, Xiaofei Liu, Zhongyi Liu, Jun Gao | :venue:AAAI 2017

Fast Network Embedding Enhancement via High Order Proximity Approximation <https://www.ijcai.org/proceedings/2017/544>_ | :authors:Cheng Yang, Maosong Sun, Zhiyuan Liu, Cunchao Tu | :venue:IJCAI 2017

struc2vec: Learning Node Representations from Structural Identity <https://arxiv.org/pdf/1704.03165>_ | :authors:Leonardo F. R. Ribeiro, Pedro H. P. Savarese, Daniel R. Figueiredo | :venue:KDD 2017 | :keywords:Structural Identity

Poincaré Embeddings for Learning Hierarchical Representations <https://arxiv.org/pdf/1705.08039>_ | :authors:Maximilian Nickel, Douwe Kiela | :venue:NIPS 2017

VERSE: Versatile Graph Embeddings from Similarity Measures <https://arxiv.org/pdf/1803.04742>_ | :authors:Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Emmanuel Müller | :venue:WWW 2018

Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec <https://arxiv.org/pdf/1710.02971>_ | :authors:Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, Jie Tang | :venue:WSDM 2018

Learning Structural Node Embeddings via Diffusion Wavelets <https://arxiv.org/pdf/1710.10321>_ | :authors:Claire Donnat, Marinka Zitnik, David Hallac, Jure Leskovec | :venue:KDD 2018

Adversarial Network Embedding <https://arxiv.org/pdf/1711.07838>_ | :authors:Quanyu Dai, Qiang Li, Jian Tang, Dan Wang | :venue:AAAI 2018

GraphGAN: Graph Representation Learning with Generative Adversarial Nets <https://arxiv.org/pdf/1711.08267>_ | :authors:Hongwei Wang, Jia Wang, Jialin Wang, Miao Zhao, Weinan Zhang, Fuzheng Zhang, Xing Xie, Minyi Guo | :venue:AAAI 2018

A General View for Network Embedding as Matrix Factorization <https://dl.acm.org/citation.cfm?id=3291029>_ | :authors:Xin Liu, Tsuyoshi Murata, Kyoung-Sook Kim, Chatchawan Kotarasu, Chenyi Zhuang | :venue:WSDM 2019

Deep Graph Infomax <https://arxiv.org/pdf/1809.10341>_ | :authors:Petar Veličković, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, R Devon Hjelm | :venue:ICLR 2019

NetSMF: Large-Scale Network Embedding as Sparse Matrix Factorization <http://keg.cs.tsinghua.edu.cn/jietang/publications/www19-Qiu-et-al-NetSMF-Large-Scale-Network-Embedding.pdf>_ | :authors:Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Chi Wang, Kuansan Wang, Jie Tang | :venue:WWW 2019

Adversarial Training Methods for Network Embedding <https://dl.acm.org/citation.cfm?id=3313445>_ | :authors:Quanyu Dai, Xiao Shen, Liang Zhang, Qiang Li, Dan Wang | :venue:WWW 2019

vGraph: A Generative Model for Joint Community Detection and Node Representation Learning <https://arxiv.org/pdf/1906.07159.pdf>_ | :authors:Fan-Yun Sun, Meng Qu, Jordan Hoffmann, Chin-Wei Huang, Jian Tang | :venue:NeurIPS 2019

ProGAN: Network Embedding via Proximity Generative Adversarial Network <https://dl.acm.org/citation.cfm?id=3330866>_ | :authors:Hongchang Gao, Jian Pei, Heng Huang | :venue:KDD 2019

GraphZoom: A Multi-level Spectral Approach for Accurate and Scalable Graph Embedding <https://openreview.net/pdf?id=r1lGO0EKDH>_ | :authors:Chenhui Deng, Zhiqiang Zhao, Yongyu Wang, Zhiru Zhang, Zhuo Feng | :venue:ICLR 2020

Node Representation Learning in Heterogeneous Graphs

Learning Latent Representations of Nodes for Classifying in Heterogeneous Social Networks <https://dl.acm.org/citation.cfm?id=2556225>_ | :authors:Yann Jacob, Ludovic Denoyer, Patrick Gallinari | :venue:WSDM 2014

PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks <https://arxiv.org/pdf/1508.00200>_ | :authors:Jian Tang, Meng Qu, Qiaozhu Mei | :venue:KDD 2015 | :keywords:Text Embedding, Heterogeneous Text Graphs

Heterogeneous Network Embedding via Deep Architectures <https://dl.acm.org/citation.cfm?id=2783296>_ | :authors:Shiyu Chang, Wei Han, Jiliang Tang, Guo-Jun Qi, Charu C. Aggarwal, Thomas S. Huang | :venue:KDD 2015

Network Representation Learning with Rich Text Information <https://www.aaai.org/ocs/index.php/IJCAI/IJCAI15/paper/view/11098>_ | :authors:Cheng Yang, Zhiyuan Liu, Deli Zhao, Maosong Sun, Edward Chang | :venue:AAAI 2015

Max-Margin DeepWalk: Discriminative Learning of Network Representation <https://www.ijcai.org/Proceedings/16/Papers/547.pdf>_ | :authors:Cunchao Tu, Weicheng Zhang, Zhiyuan Liu, Maosong Sun | :venue:IJCAI 2016

metapath2vec: Scalable Representation Learning for Heterogeneous Networks <https://dl.acm.org/citation.cfm?id=3098036>_ | :authors:Yuxiao Dong, Nitesh V. Chawla, Ananthram Swami | :venue:KDD 2017

Meta-Path Guided Embedding for Similarity Search in Large-Scale Heterogeneous Information Networks <https://arxiv.org/pdf/1610.09769>_ | :authors:Jingbo Shang, Meng Qu, Jialu Liu, Lance M. Kaplan, Jiawei Han, Jian Peng | :venue:arXiv 2016

HIN2Vec: Explore Meta-paths in Heterogeneous Information Networks for Representation Learning <https://dl.acm.org/citation.cfm?id=3132953>_ | :authors:Tao-yang Fu, Wang-Chien Lee, Zhen Lei | :venue:CIKM 2017

An Attention-based Collaboration Framework for Multi-View Network Representation Learning <https://arxiv.org/pdf/1709.06636>_ | :authors:Meng Qu, Jian Tang, Jingbo Shang, Xiang Ren, Ming Zhang, Jiawei Han | :venue:CIKM 2017

Multi-view Clustering with Graph Embedding for Connectome Analysis <https://dl.acm.org/citation.cfm?id=3132909>_ | :authors:Guixiang Ma, Lifang He, Chun-Ta Lu, Weixiang Shao, Philip S. Yu, Alex D. Leow, Ann B. Ragin | :venue:CIKM 2017

Attributed Signed Network Embedding <https://dl.acm.org/citation.cfm?id=3132847.3132905>_ | :authors:Suhang Wang, Charu Aggarwal, Jiliang Tang, Huan Liu | :venue:CIKM 2017

CANE: Context-Aware Network Embedding for Relation Modeling <https://aclweb.org/anthology/papers/P/P17/P17-1158/>_ | :authors:Cunchao Tu, Han Liu, Zhiyuan Liu, Maosong Sun | :venue:ACL 2017

PME: Projected Metric Embedding on Heterogeneous Networks for Link Prediction <https://dl.acm.org/citation.cfm?id=3219986>_ | :authors:Hongxu Chen, Hongzhi Yin, Weiqing Wang, Hao Wang, Quoc Viet Hung Nguyen, Xue Li | :venue:KDD 2018

BiNE: Bipartite Network Embedding <https://dl.acm.org/citation.cfm?id=3209978.3209987>_ | :authors:Ming Gao, Leihui Chen, Xiangnan He, Aoying Zhou | :venue:SIGIR 2018

StarSpace: Embed All The Things <https://arxiv.org/pdf/1709.03856>_ | :authors:Ledell Wu, Adam Fisch, Sumit Chopra, Keith Adams, Antoine Bordes, Jason Weston | :venue:AAAI 2018

Exploring Expert Cognition for Attributed Network Embedding <https://dl.acm.org/citation.cfm?id=3159655>_ | :authors:Xiao Huang, Qingquan Song, Jundong Li, Xia Hu | :venue:WSDM 2018

SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction <https://arxiv.org/pdf/1712.00732>_ | :authors:Hongwei Wang, Fuzheng Zhang, Min Hou, Xing Xie, Minyi Guo, Qi Liu | :venue:WSDM 2018

Multidimensional Network Embedding with Hierarchical Structures <https://dl.acm.org/citation.cfm?id=3159680>_ | :authors:Yao Ma, Zhaochun Ren, Ziheng Jiang, Jiliang Tang, Dawei Yin | :venue:WSDM 2018

Curriculum Learning for Heterogeneous Star Network Embedding via Deep Reinforcement Learning <https://dl.acm.org/citation.cfm?id=3159711>_ | :authors:Meng Qu, Jian Tang, Jiawei Han | :venue:WSDM 2018

Generative Adversarial Network based Heterogeneous Bibliographic Network Representation for Personalized Citation Recommendation <https://www.semanticscholar.org/paper/Generative-Adversarial-Network-Based-Heterogeneous-Cai-Han/1596d6487012696ba400fb69904a2c372a08a2be>_ | :authors:Xiaoyan Cai, Junwei Han, Libin Yang | :venue:AAAI 2018

ANRL: Attributed Network Representation Learning via Deep Neural Networks <https://www.ijcai.org/proceedings/2018/438>_ | :authors:Zhen Zhang, Hongxia Yang, Jiajun Bu, Sheng Zhou, Pinggang Yu, Jianwei Zhang, Martin Ester, Can Wang | :venue:IJCAI 2018

Efficient Attributed Network Embedding via Recursive Randomized Hashing <https://www.ijcai.org/proceedings/2018/397>_ | :authors:Wei Wu, Bin Li, Ling Chen, Chengqi Zhang | :venue:IJCAI 2018

Deep Attributed Network Embedding <https://www.ijcai.org/proceedings/2018/467>_ | :authors:Hongchang Gao, Heng Huang | :venue:IJCAI 2018

Co-Regularized Deep Multi-Network Embedding <https://dl.acm.org/citation.cfm?id=3186113>_ | :authors:Jingchao Ni, Shiyu Chang, Xiao Liu, Wei Cheng, Haifeng Chen, Dongkuan Xu, Xiang Zhang | :venue:WWW 2018

Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks <https://arxiv.org/pdf/1807.03490>_ | :authors:Yu Shi, Qi Zhu, Fang Guo, Chao Zhang, Jiawei Han | :venue:KDD 2018

Meta-Graph Based HIN Spectral Embedding: Methods, Analyses, and Insights <https://www.semanticscholar.org/paper/Meta-Graph-Based-HIN-Spectral-Embedding%3A-Methods%2C-Yang-Feng/4d5f4d6785d550383e3f3afb04c3015bf0d28405>_ | :authors:Carl Yang, Yichen Feng, Pan Li, Yu Shi, Jiawei Han | :venue:ICDM 2018

SIDE: Representation Learning in Signed Directed Networks <https://dl.acm.org/citation.cfm?id=3186117>_ | :authors:Junghwan Kim, Haekyu Park, Ji-Eun Lee, U Kang | :venue:WWW 2018

Learning Network-to-Network Model for Content-rich Network Embedding <https://dl.acm.org/citation.cfm?id=3330924>_ | :authors: Zhicheng He, Jie Liu, Na Li, Yalou Huang | :venue:KDD 2019

Node Representation Learning in Dynamic Graphs

Know-evolve: Deep temporal reasoning for dynamic knowledge graphs <https://arxiv.org/pdf/1705.05742.pdf>_ | :authors:Rakshit Trivedi, Hanjun Dai, Yichen Wang, Le Song | :venue:ICML 2017

Dyngem: Deep embedding method for dynamic graphs <https://arxiv.org/pdf/1805.11273.pdf>_ | :authors:Palash Goyal, Nitin Kamra, Xinran He, Yan Liu | :venue:ICLR 2017 Workshop

Attributed network embedding for learning in a dynamic environment <https://arxiv.org/pdf/1706.01860.pdf>_ | :authors:Jundong Li, Harsh Dani, Xia Hu, Jiliang Tang, Yi Chang, Huan Liu | :venue:CIKM 2017

Dynamic Network Embedding by Modeling Triadic Closure Process <http://yangy.org/works/dynamictriad/dynamic_triad.pdf>_ | :authors:Lekui Zhou, Yang Yang, Xiang Ren, Fei Wu, Yueting Zhuang | :venue:AAAI 2018

DepthLGP: Learning Embeddings of Out-of-Sample Nodes in Dynamic Networks <https://pdfs.semanticscholar.org/9499/b38866b1eb87ae43fa5be02f9d08cd3c20a8.pdf?_ga=2.6780794.935636364.1561139530-1831876308.1523264869>_ | :authors:Jianxin Ma, Peng Cui, Wenwu Zhu | :venue:AAAI 2018

TIMERS: Error-Bounded SVD Restart on Dynamic Networks <https://arxiv.org/pdf/1711.09541.pdf>_ | :authors:Ziwei Zhang, Peng Cui, Jian Pei, Xiao Wang, Wenwu Zhu | :venue:AAAI 2018

Dynamic Embeddings for User Profiling in Twitter <https://dl.acm.org/citation.cfm?id=3219819.3220043>_ | :authors:Shangsong Liang, Xiangliang Zhang, Zhaochun Ren, Evangelos Kanoulas | :venue:KDD 2018

Dynamic Network Embedding : An Extended Approach for Skip-gram based Network Embedding <https://www.ijcai.org/proceedings/2018/0288.pdf>_ | :authors:Lun Du, Yun Wang, Guojie Song, Zhicong Lu, Junshan Wang | :venue:IJCAI 2018

DyRep: Learning Representations over Dynamic Graphs <https://openreview.net/pdf?id=HyePrhR5KX>_ | :authors:Rakshit Trivedi, Mehrdad Farajtabar, Prasenjeet Biswal, Hongyuan Zha | :venue:ICLR 2019

Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks <https://cs.stanford.edu/~srijan/pubs/jodie-kdd2019.pdf>_ | :authors:Srijan Kumar, Xikun Zhang, Jure Leskovec | :venue:KDD 2019

Variational Graph Recurrent Neural Networks <https://arxiv.org/pdf/1908.09710.pdf>_ | :authors:Ehsan Hajiramezanali, Arman Hasanzadeh, Nick Duffield, Krishna R Narayanan, Mingyuan Zhou, Xiaoning Qian | :venue:NeurIPS 2019

Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks <https://arxiv.org/pdf/1907.03395.pdf>_ | :authors:Vineet Kosaraju, Amir Sadeghian, Roberto Martín-Martín, Ian Reid, S. Hamid Rezatofighi, Silvio Savarese | :venue:NeurIPS 2019

Knowledge Graph Embedding

A Three-Way Model for Collective Learning on Multi-Relational Data. <http://www.icml-2011.org/papers/438_icmlpaper.pdf>_ | :authors:Maximilian Nickel, Volker Tresp, Hans-Peter Kriegel | :venue:ICML 2011

Translating Embeddings for Modeling Multi-relational Data <https://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data.pdf>_ | :authors:Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, Oksana Yakhnenko | :venue:NIPS 2013

Knowledge Graph Embedding by Translating on Hyperplanes <https://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/viewFile/8531/8546>_ | :authors:Zhen Wang, Jianwen Zhang, Jianlin Feng, Zheng Chen | :venue:AAAI 2014

Reducing the Rank of Relational Factorization Models by Including Observable Patterns <http://papers.nips.cc/paper/5448-reducing-the-rank-in-relational-factorization-models-by-including-observable-patterns.pdf>_ | :authors:Maximilian Nickel, Xueyan Jiang, Volker Tresp | :venue:NIPS 2014

Learning Entity and Relation Embeddings for Knowledge Graph Completion <https://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/viewFile/9571/9523>_ | :authors:Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, Xuan Zhu | :venue:AAAI 2015

A Review of Relational Machine Learning for Knowledge Graph <https://arxiv.org/pdf/1503.00759.pdf>_ | :authors:Maximilian Nickel, Kevin Murphy, Volker Tresp, Evgeniy Gabrilovich | :venue:IEEE 2015

Knowledge Graph Embedding via Dynamic Mapping Matrix <https://www.aclweb.org/anthology/P15-1067>_ | :authors:Guoliang Ji, Shizhu He, Liheng Xu, Kang Liu, Jun Zha | :venue:ACL 2015

Modeling Relation Paths for Representation Learning of Knowledge Bases <https://arxiv.org/pdf/1506.00379>_ | :authors:Yankai Lin, Zhiyuan Liu, Huanbo Luan, Maosong Sun, Siwei Rao, Song Liu | :venue:EMNLP 2015

Embedding Entities and Relations for Learning and Inference in Knowledge Bases <https://arxiv.org/pdf/1412.6575>_ | :authors:Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, Li Deng | :venue:ICLR 2015

Holographic Embeddings of Knowledge Graphs <https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/viewPDFInterstitial/12484/11828>_ | :authors:Maximilian Nickel, Lorenzo Rosasco, Tomaso Poggio | :venue:AAAI 2016

Complex Embeddings for Simple Link Prediction <http://www.jmlr.org/proceedings/papers/v48/trouillon16.pdf>_ | :authors:Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, Guillaume Bouchard | :venue:ICML 2016

Modeling Relational Data with Graph Convolutional Networks <https://arxiv.org/pdf/1703.06103>_ | :authors:Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, Max Welling | :venue:arXiv 2017

Fast Linear Model for Knowledge Graph Embeddings <https://arxiv.org/pdf/1710.10881>_ | :authors:Armand Joulin, Edouard Grave, Piotr Bojanowski, Maximilian Nickel, Tomas Mikolov | :venue:arXiv 2017

Convolutional 2D Knowledge Graph Embeddings <https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/download/17366/15884>_ | :authors:Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, Sebastian Riedel | :venue:AAAI 2018

Knowledge Graph Embedding With Iterative Guidance From Soft Rules <https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/download/16369/16011>_ | :authors:Shu Guo, Quan Wang, Lihong Wang, Bin Wang, Li Guo | :venue:AAAI 2018

KBGAN: Adversarial Learning for Knowledge Graph Embeddings <https://arxiv.org/abs/1711.04071>_ | :authors:Liwei Cai, William Yang Wang | :venue:NAACL 2018

Improving Knowledge Graph Embedding Using Simple Constraints <https://arxiv.org/abs/1805.02408>_ | :authors:Boyang Ding, Quan Wang, Bin Wang, Li Guo | :venue:ACL 2018

SimplE Embedding for Link Prediction in Knowledge Graphs <https://arxiv.org/abs/1802.04868>_ | :authors:Seyed Mehran Kazemi, David Poole | :venue:NeurIPS 2018

A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network <https://aclweb.org/anthology/papers/N/N18/N18-2053/>_ | :authors:Dai Quoc Nguyen, Tu Dinh Nguyen, Dat Quoc Nguyen, Dinh Phung | :venue:NAACL 2018

Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning <https://arxiv.org/abs/1903.08948>_ | :authors:Wen Zhang, Bibek Paudel, Liang Wang, Jiaoyan Chen, Hai Zhu, Wei Zhang, Abraham Bernstein, Huajun Chen | :venue:WWW 2019

RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space <https://arxiv.org/abs/1902.10197>_ | :authors:Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, Jian Tang | :venue:ICLR 2019

Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs <https://arxiv.org/abs/1906.01195>_ | :authors:Deepak Nathani, Jatin Chauhan, Charu Sharma, Manohar Kaul | :venue:ACL 2019

Probabilistic Logic Neural Networks for Reasoning <https://arxiv.org/pdf/1906.08495.pdf>_ | :authors:Meng Qu, Jian Tang | :venue:NeurIPS 2019

Quaternion Knowledge Graph Embeddings <https://arxiv.org/pdf/1904.10281.pdf>_ | :authors:Shuai Zhang, Yi Tay, Lina Yao, Qi Liu | :venue:NeurIPS 2019

Quantum Embedding of Knowledge for Reasoning <https://papers.nips.cc/paper/8797-quantum-embedding-of-knowledge-for-reasoning.pdf>_ | :authors:Dinesh Garg, Santosh K. Srivastava, Hima Karanam | :venue:NeurIPS 2019

Multi-relational Poincaré Graph Embeddings <https://arxiv.org/pdf/1905.09791.pdf>_ | :authors:Ivana Balaževic, Carl Allen, Timothy Hospedales | :venue:NeurIPS 2019

Dynamically Pruned Message Passing Networks for Large-scale Knowledge Graph Reasoning <https://openreview.net/forum?id=rkeuAhVKvB>_ | :authors:Xiaoran Xu, Wei Feng, Yunsheng Jiang, Xiaohui Xie, Zhiqing Sun, Zhi-Hong Deng | :venue:ICLR 2020

Graph Neural Networks

Revisiting Semi-supervised Learning with Graph Embeddings <https://arxiv.org/pdf/1603.08861>_ | :authors:Zhilin Yang, William W. Cohen, Ruslan Salakhutdinov | :venue:ICML 2016

Semi-Supervised Classification with Graph Convolutional Networks <https://arxiv.org/pdf/1609.02907>_ | :authors:Thomas N. Kipf, Max Welling | :venue:ICLR 2017

Neural Message Passing for Quantum Chemistry <https://arxiv.org/pdf/1704.01212>_ | :authors:Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, George E. Dahl | :venue:ICML 2017

Motif-Aware Graph Embeddings <http://gearons.org/assets/docs/motif-aware-graph-final.pdf>_ | :authors:Hoang Nguyen, Tsuyoshi Murata | :venue:IJCAI 2017

Learning Graph Representations with Embedding Propagation <https://arxiv.org/pdf/1710.03059>_ | :authors:Alberto Garcia-Duran, Mathias Niepert | :venue:NIPS 2017

Inductive Representation Learning on Large Graphs <https://arxiv.org/pdf/1706.02216>_ | :authors:William L. Hamilton, Rex Ying, Jure Leskovec | :venue:NIPS 2017

Graph Attention Networks <https://arxiv.org/pdf/1710.10903>_ | :authors:Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, Yoshua Bengio | :venue:ICLR 2018

FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling <https://arxiv.org/pdf/1801.10247>_ | :authors:Jie Chen, Tengfei Ma, Cao Xiao | :venue:ICLR 2018

Representation Learning on Graphs with Jumping Knowledge Networks <https://arxiv.org/pdf/1806.03536>_ | :authors:Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, Stefanie Jegelka | :venue:ICML 2018

Stochastic Training of Graph Convolutional Networks with Variance Reduction <https://arxiv.org/pdf/1710.10568>_ | :authors:Jianfei Chen, Jun Zhu, Le Song | :venue:ICML 2018

Large-Scale Learnable Graph Convolutional Networks <https://arxiv.org/pdf/1808.03965>_ | :authors:Hongyang Gao, Zhengyang Wang, Shuiwang Ji | :venue:KDD 2018

Adaptive Sampling Towards Fast Graph Representation Learning <https://papers.nips.cc/paper/7707-adaptive-sampling-towards-fast-graph-representation-learning.pdf>_ | :authors:Wenbing Huang, Tong Zhang, Yu Rong, Junzhou Huang | :venue:NeurIPS 2018

Hierarchical Graph Representation Learning with Differentiable Pooling <https://arxiv.org/pdf/1806.08804>_ | :authors:Rex Ying, Jiaxuan You, Christopher Morris, Xiang Ren, William L. Hamilton, Jure Leskovec | :venue:NeurIPS 2018

Bayesian Semi-supervised Learning with Graph Gaussian Processes <https://papers.nips.cc/paper/7440-bayesian-semi-supervised-learning-with-graph-gaussian-processes.pdf>_ | :authors:Yin Cheng Ng, Nicolò Colombo, Ricardo Silva | :venue:NeurIPS 2018

Pitfalls of Graph Neural Network Evaluation <https://arxiv.org/pdf/1811.05868>_ | :authors:Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, Stephan Günnemann | :venue:arXiv 2018

Heterogeneous Graph Attention Network <https://arxiv.org/pdf/1903.07293>_ | :authors:Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Peng Cui, P. Yu, Yanfang Ye | :venue:WWW 2019

Bayesian graph convolutional neural networks for semi-supervised classification <https://arxiv.org/pdf/1811.11103.pdf>_ | :authors:Yingxue Zhang, Soumyasundar Pal, Mark Coates, Deniz Üstebay | :venue:AAAI 2019

How Powerful are Graph Neural Networks? <https://arxiv.org/pdf/1810.00826>_ | :authors:Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka | :venue:ICLR 2019

LanczosNet: Multi-Scale Deep Graph Convolutional Networks <https://arxiv.org/pdf/1901.01484>_ | :authors:Renjie Liao, Zhizhen Zhao, Raquel Urtasun, Richard S. Zemel | :venue:ICLR 2019

Graph Wavelet Neural Network <https://arxiv.org/pdf/1904.07785>_ | :authors:Bingbing Xu, Huawei Shen, Qi Cao, Yunqi Qiu, Xueqi Cheng | :venue:ICLR 2019

Supervised Community Detection with Line Graph Neural Networks <https://openreview.net/pdf?id=H1g0Z3A9Fm>_ | :authors:Zhengdao Chen, Xiang Li, Joan Bruna | :venue:ICLR 2019

Predict then Propagate: Graph Neural Networks meet Personalized PageRank <https://arxiv.org/pdf/1810.05997>_ | :authors:Johannes Klicpera, Aleksandar Bojchevski, Stephan Günnemann | :venue:ICLR 2019

Invariant and Equivariant Graph Networks <https://arxiv.org/pdf/1812.09902>_ | :authors:Haggai Maron, Heli Ben-Hamu, Nadav Shamir, Yaron Lipman | :venue:ICLR 2019

Capsule Graph Neural Network <https://openreview.net/pdf?id=Byl8BnRcYm>_ | :authors:Zhang Xinyi, Lihui Chen | :venue:ICLR 2019

MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing <https://arxiv.org/pdf/1905.00067>_ | :authors:Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, Aram Galstyan | :venue:ICML 2019

Graph U-Nets <https://arxiv.org/pdf/1905.05178>_ | :authors:Hongyang Gao, Shuiwang Ji | :venue:ICML 2019

Disentangled Graph Convolutional Networks <http://proceedings.mlr.press/v97/ma19a/ma19a.pdf>_ | :authors:Jianxin Ma, Peng Cui, Kun Kuang, Xin Wang, Wenwu Zhu | :venue:ICML 2019

GMNN: Graph Markov Neural Networks <https://arxiv.org/pdf/1905.06214>_ | :authors:Meng Qu, Yoshua Bengio, Jian Tang | :venue:ICML 2019

Simplifying Graph Convolutional Networks <https://arxiv.org/pdf/1902.07153>_ | :authors:Felix Wu, Tianyi Zhang, Amauri Holanda de Souza Jr., Christopher Fifty, Tao Yu, Kilian Q. Weinberger | :venue:ICML 2019

Position-aware Graph Neural Networks <https://arxiv.org/pdf/1906.04817>_ | :authors:Jiaxuan You, Rex Ying, Jure Leskovec | :venue:ICML 2019

Self-Attention Graph Pooling <https://arxiv.org/pdf/1904.08082>_ | :authors:Junhyun Lee, Inyeop Lee, Jaewoo Kang | :venue:ICML 2019

Relational Pooling for Graph Representations <https://arxiv.org/pdf/1903.02541>_ | :authors:Ryan L. Murphy, Balasubramaniam Srinivasan, Vinayak Rao, Bruno Ribeiro | :venue:ICML 2019

Graph Representation Learning via Hard and Channel-Wise Attention Networks <https://arxiv.org/pdf/1907.04652.pdf>_ | :authors:Hongyang Gao, Shuiwang Ji | :venue:KDD 2019

Conditional Random Field Enhanced Graph Convolutional Neural Networks <https://www.kdd.org/kdd2019/accepted-papers/view/conditional-random-field-enhanced-graph-convolutional-neural-networks>_ | :authors:Hongchang Gao, Jian Pei, Heng Huang | :venue:KDD 2019

Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks <https://arxiv.org/abs/1905.07953>_ | :authors:Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, Cho-Jui Hsieh | :venue:KDD 2019

DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification <https://arxiv.org/abs/1906.02319>_ | :authors:Jun Wu, Jingrui He, Jiejun Xu | :venue:KDD 2019

HetGNN: Heterogeneous Graph Neural Network <https://www.kdd.org/kdd2019/accepted-papers/view/hetgnn-heterogeneous-graph-neural-network>_ | :authors:Chuxu Zhang, Dongjin Song, Chao Huang, Ananthram Swami, Nitesh V. Chawla | :venue:KDD 2019

Graph Recurrent Networks with Attributed Random Walks <https://dl.acm.org/citation.cfm?id=3292500.3330941>_ | :authors:Xiao Huang, Qingquan Song, Yuening Li, Xia Hu | :venue:KDD 2019

Graph Convolutional Networks with EigenPooling <https://arxiv.org/abs/1904.13107>_ | :authors:Yao Ma, Suhang Wang, Charu Aggarwal, Jiliang Tang | :venue:KDD 2019

DFNets: Spectral CNNs for Graphs with Feedback-Looped Filters <http://users.cecs.anu.edu.au/~u5170295/papers/nips-wijesinghe-2019.pdf>_ | :authors:Asiri Wijesinghe, Qing Wang | :venue:NeurIPS 2019

Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology <https://arxiv.org/pdf/1907.05008.pdf>_ | :authors:Nima Dehmamy, Albert-László Barabási, Rose Yu | :venue:NeurIPS 2019

A Flexible Generative Framework for Graph-based Semi-supervised Learning <https://arxiv.org/pdf/1905.10769.pdf>_ | :authors:Jiaqi Ma, Weijing Tang, Ji Zhu, Qiaozhu Mei | :venue:NeurIPS 2019

Rethinking Kernel Methods for Node Representation Learning on Graphs <https://arxiv.org/pdf/1910.02548.pdf>_ | :authors:Yu Tian, Long Zhao, Xi Peng, Dimitris N. Metaxas | :venue:NeurIPS 2019

Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks <https://arxiv.org/pdf/1906.02174.pdf>_ | :authors:Sitao Luan, Mingde Zhao, Xiao-Wen Chang, Doina Precup | :venue:NeurIPS 2019

N-Gram Graph: A Simple Unsupervised Representation for Molecules <https://arxiv.org/pdf/1806.09206.pdf>_ | :authors:Shengchao Liu, Thevaa Chandereng, Yingyu Liang | :venue:NeurIPS 2019

DeepGCNs: Can GCNs Go as Deep as CNNs? <https://arxiv.org/pdf/1904.03751.pdf>_ | :authors:Guohao Li, Matthias Muller, Ali Thabet, Bernard Ghanem | :venue:ICCV 2019

Continuous Graph Neural Networks <https://arxiv.org/pdf/1912.00967.pdf>_ | :authors:Louis-Pascal A. C. Xhonneux, Meng Qu, Jian Tang | :venue:arXiv 2019

Curvature Graph Network <https://openreview.net/pdf?id=BylEqnVFDB>_ | :authors:Ze Ye, Kin Sum Liu, Tengfei Ma, Jie Gao, Chao Chen | :venue:ICLR 2020

Memory-based Graph Networks <https://openreview.net/pdf?id=r1laNeBYPB>_ | :authors:Amir hosein Khasahmadi, Kaveh Hassani, Parsa Moradi, Leo Lee, Quaid Morris | :venue:ICLR 2020

Strategies for Pre-training Graph Neural Networks <https://openreview.net/pdf?id=HJlWWJSFDH>_ | :authors:Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, Jure Leskovec | :venue:ICLR 2020

Applications of Graph Deep Learning

Natural Language Processing

Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling <https://www.aclweb.org/anthology/D17-1159>_ | :authors:Diego Marcheggiani, Ivan Titov | :venue:EMNLP 2017

Graph Convolutional Encoders for Syntax-aware Neural Machine Translation <https://www.aclweb.org/anthology/D17-1209>_ | :authors:Joost Bastings, Ivan Titov, Wilker Aziz, Diego Marcheggiani, Khalil Sima’an | :venue:EMNLP 2017

Graph-based Neural Multi-Document Summarization <https://www.aclweb.org/anthology/K17-1045>_ | :authors:Michihiro Yasunaga, Rui Zhang, Kshitijh Meelu, Ayush Pareek, Krishnan Srinivasan, Dragomir Radev | :venue:CoNLL 2017

QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension <https://arxiv.org/pdf/1804.09541.pdf>_ | :authors:Adams Wei Yu, David Dohan, Minh-Thang Luong, Rui Zhao, Kai Chen, Mohammad Norouzi, Quoc V. Le | :venue:ICLR 2018

A Structured Self-attentive Sentence Embedding <https://arxiv.org/pdf/1703.03130.pdf>_ | :authors:Zhouhan Lin, Minwei Feng, Cicero Nogueira dos Santos, Mo Yu, Bing Xiang, Bowen Zhou, Yoshua Bengio | :venue:ICLR 2018

Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering <https://aclweb.org/anthology/C18-1280>_ | :authors:Daniil Sorokin, Iryna Gurevych | :venue:COLING 2018

Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks <https://www.aclweb.org/anthology/N18-2078>_ | :authors:Diego Marcheggiani, Joost Bastings, Ivan Titov | :venue:NAACL 2018

Linguistically-Informed Self-Attention for Semantic Role Labeling <https://www.aclweb.org/anthology/D18-1548>_ | :authors:Emma Strubell, Patrick Verga, Daniel Andor, David Weiss, Andrew McCallum | :venue:EMNLP 2018

Graph Convolution over Pruned Dependency Trees Improves Relation Extraction <https://aclweb.org/anthology/D18-1244>_ | :authors:Yuhao Zhang, Peng Qi, Christopher D. Manning | :venue:EMNLP 2018

A Graph-to-Sequence Model for AMR-to-Text Generation <https://www.aclweb.org/anthology/P18-1150>_ | :authors:Linfeng Song, Yue Zhang, Zhiguo Wang, Daniel Gildea | :venue:ACL 2018

Graph-to-Sequence Learning using Gated Graph Neural Networks <https://www.aclweb.org/anthology/P18-1026>_ | :authors:Daniel Beck, Gholamreza Haffari, Trevor Cohn | :venue:ACL 2018

Graph Convolutional Networks for Text Classification <https://arxiv.org/pdf/1809.05679.pdf>_ | :authors:Liang Yao, Chengsheng Mao, Yuan Luo | :venue:AAAI 2019

Differentiable Perturb-and-Parse: Semi-Supervised Parsing with a Structured Variational Autoencoder <https://openreview.net/pdf?id=BJlgNh0qKQ>_ | :authors:Caio Corro, Ivan Titov | :venue:ICLR 2019

Structured Neural Summarization <https://arxiv.org/pdf/1811.01824.pdf>_ | :authors:Patrick Fernandes, Miltiadis Allamanis, Marc Brockschmid | :venue:ICLR 2019

Multi-task Learning over Graph Structures <https://arxiv.org/pdf/1811.10211.pdf>_ | :authors:Pengfei Liu, Jie Fu, Yue Dong, Xipeng Qiu, Jackie Chi Kit Cheung | :venue:AAAI 2019

Imposing Label-Relational Inductive Bias for Extremely Fine-Grained Entity Typing <https://arxiv.org/pdf/1903.02591.pdf>_ | :authors:Wenhan Xiong, Jiawei Wu, Deren Lei, Mo Yu, Shiyu Chang, Xiaoxiao Guo, William Yang Wang | :venue:NAACL 2019

Single Document Summarization as Tree Induction <https://www.aclweb.org/anthology/N19-1173>_ | :authors:Yang Liu, Ivan Titov, Mirella Lapata | :venue:NAACL 2019

Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks <https://arxiv.org/pdf/1903.01306.pdf>_ | :authors:Ningyu Zhang, Shumin Deng, Zhanlin Sun, Guanying Wang, Xi Chen, Wei Zhang, Huajun Chen | :venue:NAACL 2019

Graph Neural Networks with Generated Parameters for Relation Extraction <https://arxiv.org/pdf/1902.00756.pdf>_ | :authors:Hao Zhu, Yankai Lin, Zhiyuan Liu, Jie Fu, Tat-seng Chua, Maosong Sun | :venue:ACL 2019

Dynamically Fused Graph Network for Multi-hop Reasoning <https://arxiv.org/pdf/1905.06933.pdf>_ | :authors:Yunxuan Xiao, Yanru Qu, Lin Qiu, Hao Zhou, Lei Li, Weinan Zhang, Yong Yu | :venue:ACL 2019

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>_ | :authors:Chang Li, Dan Goldwasser | :venue:ACL 2019

Attention Guided Graph Convolutional Networks for Relation Extraction <https://arxiv.org/pdf/1906.07510.pdf>_ | :authors:Zhijiang Guo, Yan Zhang, Wei Lu | :venue:ACL 2019

Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks <https://arxiv.org/pdf/1809.04283.pdf>_ | :authors:Shikhar Vashishth, Manik Bhandari, Prateek Yadav, Piyush Rai, Chiranjib Bhattacharyya, Partha Talukdar | :venue:ACL 2019

GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction <https://tsujuifu.github.io/pubs/acl19_graph-rel.pdf>_ | :authors:Tsu-Jui Fu, Peng-Hsuan Li, Wei-Yun Ma | :venue:ACL 2019

Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs <https://arxiv.org/pdf/1905.07374.pdf>_ | :authors:Ming Tu, Guangtao Wang, Jing Huang, Yun Tang, Xiaodong He, Bowen Zhou | :venue:ACL 2019

Cognitive Graph for Multi-Hop Reading Comprehension at Scale <https://arxiv.org/pdf/1905.05460.pdf>_ | :authors:Ming Ding, Chang Zhou, Qibin Chen, Hongxia Yang, Jie Tang | :venue:ACL 2019

Coherent Comment Generation for Chinese Articles with a Graph-to-Sequence Model <https://arxiv.org/pdf/1906.01231.pdf>_ | :authors:Wei Li, Jingjing Xu, Yancheng He, Shengli Yan, Yunfang Wu, Xu Sun | :venue:ACL 2019

Matching Article Pairs with Graphical Decomposition and Convolutions <https://arxiv.org/pdf/1802.07459.pdf>_ | :authors:Bang Liu, Di Niu, Haojie Wei, Jinghong Lin, Yancheng He, Kunfeng Lai, Yu Xu | :venue:ACL 2019

Embedding Imputation with Grounded Language Information <https://arxiv.org/pdf/1906.03753.pdf>_ | :authors:Ziyi Yang, Chenguang Zhu, Vin Sachidananda, Eric Darve | :venue:ACL 2019

Encoding Social Information with Graph Convolutional Networks forPolitical Perspective Detection in News Media <https://www.aclweb.org/anthology/P19-1247.pdf>_ | :authors:Chang Li, Dan Goldwasser | :venue:ACL 2019

A Neural Multi-digraph Model for Chinese NER with Gazetteers <https://www.aclweb.org/anthology/P19-1141.pdf>_ | :authors:Ruixue Ding, Pengjun Xie, Xiaoyan Zhang, Wei Lu, Linlin Li, Luo Si | :venue:ACL 2019

Tree Communication Models for Sentiment Analysis <https://www.aclweb.org/anthology/P19-1342.pdf>_ | :authors:Yuan Zhang, Yue Zhang | :venue:ACL 2019

A2N: Attending to Neighbors for Knowledge Graph Inference <https://www.aclweb.org/anthology/P19-1431.pdf>_ | :authors:Trapit Bansal, Da-Cheng Juan, Sujith Ravi, Andrew McCallum | :venue:ACL 2019

Textbook Question Answering with Multi-modal Context Graph Understanding and Self-supervised Open-set Comprehension <https://www.aclweb.org/anthology/P19-1347.pdf>_ | :authors:Daesik Kim, Seonhoon Kim, Nojun Kwak | :venue:ACL 2019

Look Again at the Syntax: Relational Graph Convolutional Network for Gendered Ambiguous Pronoun Resolution <https://arxiv.org/pdf/1905.08868.pdf>_ | :authors:Yinchuan Xu, Junlin Yang | :venue:ACL 2019 Workshop | :keywords:https://github.com/ianycxu/RGCN-with-BERT

Learning Graph Pooling and Hybrid Convolutional Operations for Text Representations <https://arxiv.org/pdf/1901.06965.pdf>_ | :authors:Hongyang Gao, Yongjun Chen, Shuiwang Ji | :venue:WWW 2019

Learning to Create Sentence Semantic Relation Graphs for Multi-Document Summarization <https://arxiv.org/pdf/1909.12231.pdf>_ | :authors:Diego Antognini, Boi Faltings | :venue:EMNLP 2019

Dependency-Guided LSTM-CRF for Named Entity Recognition <https://arxiv.org/pdf/1909.10148.pdf>_ | :authors:Zhanming Jie, Wei Lu | :venue:EMNLP 2019

Modeling Conversation Structure and Temporal Dynamics for Jointly Predicting Rumor Stance and Veracity <https://arxiv.org/pdf/1909.08211.pdf>_ | :authors:Penghui Wei, Nan Xu, Wenji Mao | :venue:EMNLP 2019

DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation <https://arxiv.org/pdf/1908.11540.pdf>_ | :authors:Deepanway Ghosal, Navonil Majumder, Soujanya Poria, Niyati Chhaya, Alexander Gelbukh | :venue:EMNLP 2019

Modeling Graph Structure in Transformer for Better AMR-to-Text Generation <https://arxiv.org/pdf/1909.00136.pdf>_ | :authors:Jie Zhu, Junhui Li, Muhua Zhu, Longhua Qian, Min Zhang, Guodong Zhou | :venue:EMNLP 2019

KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning <https://arxiv.org/pdf/1909.02151.pdf>_ | :authors:Bill Yuchen Lin, Xinyue Chen, Jamin Chen, Xiang Ren | :venue:EMNLP 2019

Computer Vision

3D Graph Neural Networks for RGBD Semantic Segmentation <http://www.cs.toronto.edu/~rjliao/papers/iccv_2017_3DGNN.pdf>_ | :authors:Xiaojuan Qi, Renjie Liao, Jiaya Jia, Sanja Fidler, Raquel Urtasun | :venue:ICCV 2017

Situation Recognition With Graph Neural Networks <https://arxiv.org/abs/1708.04320>_ | :authors:Ruiyu Li, Makarand Tapaswi, Renjie Liao, Jiaya Jia, Raquel Urtasun, Sanja Fidler | :venue:ICCV 2017

Graph-Based Classification of Omnidirectional Images <https://arxiv.org/abs/1707.08301>_ | :authors:Renata Khasanova, Pascal Frossard | :venue:ICCV 2017

Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition <https://arxiv.org/abs/1801.07455>_ | :authors:Sijie Yan, Yuanjun Xiong, Dahua Lin | :venue:AAAI 2018

Image Generation from Scene Graphs <https://arxiv.org/abs/1804.01622>_ | :authors:Justin Johnson, Agrim Gupta, Li Fei-Fei | :venue:CVPR 2018

FoldingNet: Point Cloud Auto-Encoder via Deep Grid Deformation <https://arxiv.org/abs/1712.07262>_ | :authors:Yaoqing Yang, Chen Feng, Yiru Shen, Dong Tian | :venue:CVPR 2018

PPFNet: Global Context Aware Local Features for Robust 3D Point Matching <https://arxiv.org/abs/1802.02669>_ | :authors:Haowen Deng, Tolga Birdal, Slobodan Ilic | :venue:CVPR 2018

Iterative Visual Reasoning Beyond Convolutions <https://arxiv.org/abs/1803.11189>_ | :authors:Xinlei Chen, Li-Jia Li, Li Fei-Fei, Abhinav Gupta | :venue:CVPR 2018

Surface Networks <https://arxiv.org/abs/1705.10819>_ | :authors:Ilya Kostrikov, Zhongshi Jiang, Daniele Panozzo, Denis Zorin, Joan Bruna | :venue:CVPR 2018

FeaStNet: Feature-Steered Graph Convolutions for 3D Shape Analysis <https://arxiv.org/abs/1706.05206>_ | :authors:Nitika Verma, Edmond Boyer, Jakob Verbeek | :venue:CVPR 2018

Learning to Act Properly: Predicting and Explaining Affordances From Images <https://arxiv.org/abs/1712.07576>_ | :authors:Ching-Yao Chuang, Jiaman Li, Antonio Torralba, Sanja Fidler | :venue:CVPR 2018

Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling <https://arxiv.org/abs/1712.06760>_ | :authors:Yiru Shen, Chen Feng, Yaoqing Yang, Dong Tian | :venue:CVPR 2018

Deformable Shape Completion With Graph Convolutional Autoencoders <https://arxiv.org/abs/1712.00268>_ | :authors:Or Litany, Alex Bronstein, Michael Bronstein, Ameesh Makadia | :venue:CVPR 2018

Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images <https://arxiv.org/abs/1804.01654>_ | :authors:Nanyang Wang, Yinda Zhang, Zhuwen Li, Yanwei Fu, Wei Liu, Yu-Gang Jiang | :venue:ECCV 2018

Learning Human-Object Interactions by Graph Parsing Neural Networks <https://arxiv.org/abs/1808.07962>_ | :authors:Siyuan Qi, Wenguan Wang, Baoxiong Jia, Jianbing Shen, Song-Chun Zhu | :venue:ECCV 2018

Generating 3D Faces using Convolutional Mesh Autoencoders <https://arxiv.org/abs/1807.10267>_ | :authors:Anurag Ranjan, Timo Bolkart, Soubhik Sanyal, Michael J. Black | :venue:ECCV 2018

Learning SO(3) Equivariant Representations with Spherical CNNs <https://arxiv.org/abs/1711.06721>_ | :authors:Carlos Esteves, Christine Allen-Blanchette, Ameesh Makadia, Kostas Daniilidis | :venue:ECCV 2018

Neural Graph Matching Networks for Fewshot 3D Action Recognition <http://openaccess.thecvf.com/content_ECCV_2018/papers/Michelle_Guo_Neural_Graph_Matching_ECCV_2018_paper.pdf>_ | :authors:Michelle Guo, Edward Chou, De-An Huang, Shuran Song, Serena Yeung, Li Fei-Fei | :venue:ECCV 2018

Multi-Kernel Diffusion CNNs for Graph-Based Learning on Point Clouds <https://arxiv.org/abs/1809.05370>_ | :authors:Lasse Hansen, Jasper Diesel, Mattias P. Heinrich | :venue:ECCV 2018

Hierarchical Video Frame Sequence Representation with Deep Convolutional Graph Network <https://arxiv.org/abs/1906.00377>_ | :authors:Feng Mao, Xiang Wu, Hui Xue, Rong Zhang | :venue:ECCV 2018

Graph R-CNN for Scene Graph Generation <https://arxiv.org/abs/1808.00191>_ | :authors:Jianwei Yang, Jiasen Lu, Stefan Lee, Dhruv Batra, Devi Parikh | :venue:ECCV 2018

Exploring Visual Relationship for Image Captioning <https://arxiv.org/abs/1809.07041>_ | :authors:Ting Yao, Yingwei Pan, Yehao Li, Tao Mei | :venue:ECCV 2018

Beyond Grids: Learning Graph Representations for Visual Recognition <https://papers.nips.cc/paper/8135-beyond-grids-learning-graph-representations-for-visual-recognition>_ | :authors:Yin Li, Abhinav Gupta | :venue:NeurIPS 2018

Learning Conditioned Graph Structures for Interpretable Visual Question Answering <https://arxiv.org/abs/1806.07243>_ | :authors:Will Norcliffe-Brown, Efstathios Vafeias, Sarah Parisot | :venue:NeurIPS 2018

LinkNet: Relational Embedding for Scene Graph <https://arxiv.org/abs/1811.06410>_ | :authors:Sanghyun Woo, Dahun Kim, Donghyeon Cho, In So Kweon | :venue:NeurIPS 2018

Flexible Neural Representation for Physics Prediction <https://arxiv.org/abs/1806.08047>_ | :authors:Damian Mrowca, Chengxu Zhuang, Elias Wang, Nick Haber, Li Fei-Fei, Joshua B. Tenenbaum, Daniel L. K. Yamins | :venue:NeurIPS 2018

Learning Localized Generative Models for 3D Point Clouds via Graph Convolution <https://openreview.net/forum?id=SJeXSo09FQ>_ | :authors:Diego Valsesia, Giulia Fracastoro, Enrico Magli | :venue:ICLR 2019

Graph-Based Global Reasoning Networks <https://arxiv.org/abs/1811.12814>_ | :authors:Yunpeng Chen, Marcus Rohrbach, Zhicheng Yan, Shuicheng Yan, Jiashi Feng, Yannis Kalantidis | :venue:CVPR 2019

Deep Graph Laplacian Regularization for Robust Denoising of Real Images <https://arxiv.org/abs/1807.11637>_ | :authors:Jin Zeng, Jiahao Pang, Wenxiu Sun, Gene Cheung | :venue:CVPR 2019

Learning Context Graph for Person Search <https://arxiv.org/abs/1904.01830>_ | :authors:Yichao Yan, Qiang Zhang, Bingbing Ni, Wendong Zhang, Minghao Xu, Xiaokang Yang | :venue:CVPR 2019

Graphonomy: Universal Human Parsing via Graph Transfer Learning <https://arxiv.org/abs/1904.04536>_ | :authors:Ke Gong, Yiming Gao, Xiaodan Liang, Xiaohui Shen, Meng Wang, Liang Lin | :venue:CVPR 2019

Masked Graph Attention Network for Person Re-Identification <http://openaccess.thecvf.com/content_CVPRW_2019/papers/TRMTMCT/Bao_Masked_Graph_Attention_Network_for_Person_Re-Identification_CVPRW_2019_paper.pdf>_ for_Person_Re-Identification_CVPRW_2019_paper.html>_ | :authors:Liqiang Bao, Bingpeng Ma, Hong Chang, Xilin Chen | :venue:CVPR 2019`

Learning to Cluster Faces on an Affinity Graph <https://arxiv.org/abs/1904.02749>_ | :authors:Lei Yang, Xiaohang Zhan, Dapeng Chen, Junjie Yan, Chen Change Loy, Dahua Lin | :venue:CVPR 2019

Actional-Structural Graph Convolutional Networks for Skeleton-Based Action Recognition <https://arxiv.org/abs/1904.12659>_ | :authors:Maosen Li, Siheng Chen, Xu Chen, Ya Zhang, Yanfeng Wang, Qi Tian | :venue:CVPR 2019

Adaptively Connected Neural Networks <https://arxiv.org/abs/1904.03579>_ | :authors:Guangrun Wang, Keze Wang, Liang Lin | :venue:CVPR 2019

Reasoning Visual Dialogs with Structural and Partial Observations <https://arxiv.org/abs/1904.03579>_ | :authors:Zilong Zheng, Wenguan Wang, Siyuan Qi, Song-Chun Zhu | :venue:CVPR 2019

MeshCNN: A Network with an Edge <https://arxiv.org/pdf/1809.05910.pdf>_ | :authors:Rana Hanocka, Amir Hertz, Noa Fish, Raja Giryes, Shachar Fleishman, Daniel Cohen-Or | :venue:SIGGRAPH 2019 | :keywords:https://ranahanocka.github.io/MeshCNN/

Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learning <https://arxiv.org/pdf/1908.02441.pdf>_ | :authors:Jiwoong Park, Minsik Lee, Hyung Jin Chang, Kyuewang Lee, Jin Young Choi | :venue:ICCV 2019

Pixel2Mesh++: Multi-View 3D Mesh Generation via Deformation <https://arxiv.org/pdf/1908.01491.pdf>_ | :authors:Chao Wen, Yinda Zhang, Zhuwen Li, Yanwei Fu | :venue:ICCV 2019

Learning Trajectory Dependencies for Human Motion Prediction <https://arxiv.org/pdf/1908.05436.pdf>_ | :authors:Wei Mao, Miaomiao Liu, Mathieu Salzmann, Hongdong Li | :venue:ICCV 2019

Graph-Based Object Classification for Neuromorphic Vision Sensing <https://arxiv.org/pdf/1908.06648.pdf>_ | :authors:Yin Bi, Aaron Chadha, Alhabib Abbas, Eirina Bourtsoulatze, Yiannis Andreopoulos | :venue:ICCV 2019

Fashion Retrieval via Graph Reasoning Networks on a Similarity Pyramid <https://arxiv.org/pdf/1908.11754.pdf>_ | :authors:Zhanghui Kuang, Yiming Gao, Guanbin Li, Ping Luo, Yimin Chen, Liang Lin, Wayne Zhang | :venue:ICCV 2019

Understanding Human Gaze Communication by Spatio-Temporal Graph Reasoning <https://arxiv.org/pdf/1909.02144.pdf>_ | :authors:Lifeng Fan, Wenguan Wang, Siyuan Huang, Xinyu Tang, Song-Chun Zhu | :venue:ICCV 2019

Visual Semantic Reasoning for Image-Text Matching <https://arxiv.org/pdf/1909.02701.pdf>_ | :authors:Kunpeng Li, Yulun Zhang, Kai Li, Yuanyuan Li, Yun Fu | :venue:ICCV 2019

Graph Convolutional Networks for Temporal Action Localization <https://arxiv.org/pdf/1909.03252.pdf>_ | :authors:Runhao Zeng, Wenbing Huang, Mingkui Tan, Yu Rong, Peilin Zhao, Junzhou Huang, Chuang Gan | :venue:ICCV 2019

Semantically-Regularized Logic Graph Embeddings <https://arxiv.org/pdf/1909.01161.pdf>_ | :authors:Yaqi Xie, Ziwei Xu, Kuldeep Meel, Mohan S Kankanhalli, Harold Soh | :venue:NeurIPS 2019

Recommender Systems

Graph Convolutional Neural Networks for Web-Scale Recommender Systems <https://arxiv.org/pdf/1806.01973.pdf>_ | :authors:Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, Jure Leskovec | :venue:KDD 2018 | :keywords:PinSage

SocialGCN: An Efficient Graph Convolutional Network based Model for Social Recommendation <https://arxiv.org/pdf/1811.02815.pdf>_ | :authors:Le Wu, Peijie Sun, Richang Hong, Yanjie Fu, Xiting Wang, Meng Wang | :venue:AAAI 2018 | :keywords:GCN, Social recommendation

Session-based Social Recommendation via Dynamic Graph Attention Networks <https://arxiv.org/pdf/1902.09362.pdf>_ | :authors:Weiping Song, Zhiping Xiao, Yifan Wang, Laurent Charlin, Ming Zhang, Jian Tang | :venue:WSDM 2019 | :keywords:Social recommendation, session-based, GAT

Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems <https://arxiv.org/pdf/1903.10433.pdf>_ | :authors:Qitian Wu, Hengrui Zhang, Xiaofeng Gao, Peng He, Paul Weng, Han Gao, Guihai Chen | :venue:WWW 2019 | :keywords:Social recommendation, GAT

Graph Neural Networks for Social Recommendation <https://arxiv.org/pdf/1902.07243.pdf>_ | :authors:Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, Dawei Yin | :venue:WWW 2019 | :keywords:Social recommendation, GNN

Session-based Recommendation with Graph Neural Networks <https://arxiv.org/pdf/1811.00855.pdf>_ | :authors:Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, Tieniu Tan | :venue:AAAI 2019 | :keywords:Session-based recommendation, GNN

A Neural Influence Diffusion Model for Social Recommendation <https://arxiv.org/pdf/1904.10322.pdf>_ | :authors:Le Wu, Peijie Sun, Yanjie Fu, Richang Hong, Xiting Wang, Meng Wang | :venue:SIGIR 2019 | :keywords:Social Recommendation, diffusion

Neural Graph Collaborative Filtering <https://arxiv.org/pdf/1905.08108.pdf>_ | :authors:Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, Tat-Seng Chua | :venue:SIGIR 2019 | :keywords:Collaborative Filtering, GNN

Binarized Collaborative Filtering with Distilling Graph Convolutional Networks <https://arxiv.org/pdf/1906.01829.pdf>_ | :authors:Haoyu Wang, Defu Lian, Yong Ge | :venue:IJCAI 2019

IntentGC: A Scalable Graph Convolution Framework Fusing Heterogeneous Information for Recommendation <https://dl.acm.org/citation.cfm?id=3330686>_ | :authors:Jun Zhao, Zhou Zhou, Ziyu Guan, Wei Zhao, Wei Ning, Guang Qiu, Xiaofei He | :venue:KDD 2019

An End-to-End Neighborhood-based Interaction Model for Knowledge-enhanced Recommendation <https://arxiv.org/pdf/1908.04032.pdf>_ | :authors:Yanru Qu, Ting Bai, Weinan Zhang, Jianyun Nie, Jian Tang | :venue:KDD 2019 Workshop

Link Prediction

Link Prediction Based on Graph Neural Networks <https://papers.nips.cc/paper/7763-link-prediction-based-on-graph-neural-networks.pdf>_ | :authors:Muhan Zhang, Yixin Chen | :venue:NeurIPS 2018

Link Prediction via Subgraph Embedding-Based Convex Matrix Completion <http://iiis.tsinghua.edu.cn/~weblt/papers/link-prediction-subgraphembeddings.pdf>_ | :authors:Zhu Cao, Linlin Wang, Gerard de Melo | :venue:AAAI 2018

Graph Convolutional Matrix Completion <https://www.kdd.org/kdd2018/files/deep-learning-day/DLDay18_paper_32.pdf>_ | :authors:Rianne van den Berg, Thomas N. Kipf, Max Welling | :venue:KDD 2018 Workshop

Semi-Implicit Graph Variational Auto-Encoders <https://arxiv.org/pdf/1908.07078.pdf>_ | :authors:Arman Hasanzadeh, Ehsan Hajiramezanali, Nick Duffield , Krishna Narayanan, Mingyuan Zhou, Xiaoning Qian | :venue:NeurIPS 2019

Influence Prediction

DeepInf: Social Influence Prediction with Deep Learning <https://arxiv.org/pdf/1807.05560.pdf>_ | :authors:Jiezhong Qiu, Jian Tang, Hao Ma, Yuxiao Dong, Kuansan Wang, Jie Tang | :venue:KDD 2018

Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks <https://arxiv.org/pdf/1905.08865.pdf>_ | :authors:Namyong Park, Andrey Kan, Xin Luna Dong, Tong Zhao, Christos Faloutsos | :venue:KDD 2019

Neural Architecture Search

Graph HyperNetworks for Neural Architecture Search <https://openreview.net/pdf?id=rkgW0oA9FX>_ | :authors:Chris Zhang, Mengye Ren, Raquel Urtasun | :venue:ICLR 2019

D-VAE: A Variational Autoencoder for Directed Acyclic Graphs <https://arxiv.org/pdf/1904.11088.pdf>_ | :authors:Muhan Zhang, Shali Jiang, Zhicheng Cui, Roman Garnett, Yixin Chen | :venue:NeurIPS 2019

Reinforcement Learning

Action Schema Networks: Generalised Policies with Deep Learning <https://arxiv.org/pdf/1709.04271.pdf>_ | :authors:Sam Toyer, Felipe Trevizan, Sylvie Thiebaux, Lexing Xie | :venue:AAAI 2018

NerveNet: Learning Structured Policy with Graph Neural Networks <https://openreview.net/pdf?id=S1sqHMZCb>_ | :authors:Tingwu Wang, Renjie Liao, Jimmy Ba, Sanja Fidler | :venue:ICLR 2018

Graph Networks as Learnable Physics Engines for Inference and Control <https://arxiv.org/pdf/1806.01242.pdf>_ | :authors:Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg, Josh Merel, Martin Riedmiller | :venue:ICML 2018

Learning Policy Representations in Multiagent Systems <https://arxiv.org/pdf/1806.06464.pdf>_ | :authors:Aditya Grover, Maruan Al-Shedivat, Jayesh K. Gupta, Yura Burda, Harrison Edwards | :venue:ICML 2018

Relational recurrent neural networks <https://papers.nips.cc/paper/7960-relational-recurrent-neural-networks.pdf>_ | :authors:Adam Santoro, Ryan Faulkner, David Raposo, Jack Rae, Mike Chrzanowski,Théophane Weber, Daan Wierstra, Oriol Vinyals, Razvan Pascanu, Timothy Lillicrap | :venue:NeurIPS 2018

Transfer of Deep Reactive Policies for MDP Planning <http://www.cse.iitd.ac.in/~mausam/papers/nips18.pdf>_ | :authors:Aniket Bajpai, Sankalp Garg, Mausam | :venue:NeurIPS 2018

Neural Graph Evolution: Towards Efficient Automatic Robot Design <https://openreview.net/pdf?id=BkgWHnR5tm>_ | :authors:Tingwu Wang, Yuhao Zhou, Sanja Fidler, Jimmy Ba | :venue:ICLR 2019

No Press Diplomacy: Modeling Multi-Agent Gameplay <https://arxiv.org/pdf/1909.02128.pdf>_ | :authors:Philip Paquette, Yuchen Lu, Steven Bocco, Max O. Smith, Satya Ortiz-Gagne, Jonathan K. Kummerfeld, Satinder Singh, Joelle Pineau, Aaron Courville | :venue:NeurIPS 2019

Combinatorial Optimization

Learning Combinatorial Optimization Algorithms over Graphs <https://arxiv.org/abs/1704.01665>_ | :authors:Hanjun Dai, Elias B. Khalil, Yuyu Zhang, Bistra Dilkina, Le Song | :venue:NeurIPS 2017

Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search <https://arxiv.org/abs/1810.10659>_ | :authors:Zhuwen Li, Qifeng Chen, Vladlen Koltun | :venue:NeurIPS 2018

Reinforcement Learning for Solving the Vehicle Routing Problem <https://arxiv.org/abs/1802.04240>_ | :authors:Mohammadreza Nazari, Afshin Oroojlooy, Lawrence V. Snyder, Martin Takáč | :venue:NeurIPS 2018

Attention, Learn to Solve Routing Problems! <https://arxiv.org/abs/1803.08475>_ | :authors:Wouter Kool, Herke van Hoof, Max Welling | :venue:ICLR 2019

Learning a SAT Solver from Single-Bit Supervision <https://arxiv.org/abs/1802.03685>_ | :authors:Daniel Selsam, Matthew Lamm, Benedikt Bünz, Percy Liang, Leonardo de Moura, David L. Dill | :venue:ICLR 2019

An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem <https://arxiv.org/abs/1906.01227>_ | :authors:Chaitanya K. Joshi, Thomas Laurent, Xavier Bresson | :venue:arXiv 2019

Approximation Ratios of Graph Neural Networks for Combinatorial Problems <https://arxiv.org/pdf/1905.10261.pdf>_ | :authors:Ryoma Sato, Makoto Yamada, Hisashi Kashima | :venue:NeurIPS 2019

Exact Combinatorial Optimization with Graph Convolutional Neural Networks <https://arxiv.org/pdf/1906.01629.pdf>_ | :authors:Maxime Gasse, Didier Chételat, Nicola Ferroni, Laurent Charlin, Andrea Lodi | :venue:NeurIPS 2019

On Learning Paradigms for the Travelling Salesman Problem <https://arxiv.org/pdf/1910.07210.pdf>_ | :authors:Chaitanya K. Joshi, Thomas Laurent, Xavier Bresson | :venue:NeurIPS 2019 Workshop

Adversarial Attack and Robustness

Adversarial Attack on Graph Structured Data <https://arxiv.org/abs/1806.02371>_ | :authors:Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, Le Song | :venue:ICML 2018

Adversarial Attacks on Neural Networks for Graph Data <https://arxiv.org/abs/1805.07984>_ | :authors:Daniel Zügner, Amir Akbarnejad, Stephan Günnemann | :venue:KDD 2018

Adversarial Attacks on Graph Neural Networks via Meta Learning <https://arxiv.org/abs/1902.08412>_ | :authors:Daniel Zügner, Stephan Günnemann | :venue:ICLR 2019

Robust Graph Convolutional Networks Against Adversarial Attacks <http://pengcui.thumedialab.com/papers/RGCN.pdf>_ | :authors:Dingyuan Zhu, Ziwei Zhang, Peng Cui, Wenwu Zhu | :venue:KDD 2019

Certifiable Robustness and Robust Training for Graph Convolutional Networks <https://arxiv.org/pdf/1906.12269.pdf>_ | :authors:Daniel Zügner, Stephan Günnemann | :venue:KDD 2019

Graph Matching

REGAL: Representation Learning-based Graph Alignment <https://arxiv.org/pdf/1802.06257.pdf>_ | :authors:Mark Heimann, Haoming Shen, Tara Safavi, Danai Koutra | :venue:CIKM 2018

Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks <https://www.aclweb.org/anthology/D18-1032.pdf>_ | :authors:Zhichun Wang, Qingsong Lv, Xiaohan Lan, Yu Zhang | :venue:EMNLP 2018

Learning Combinatorial Embedding Networks for Deep Graph Matching <http://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Learning_Combinatorial_Embedding_Networks_for_Deep_Graph_Matching_ICCV_2019_paper.pdf>_ | :authors:Runzhong Wang, Junchi Yan, Xiaokang Yang | :venue:ICCV 2019

Deep Graph Matching Consensus <https://openreview.net/pdf?id=HyeJf1HKvS>_ | :authors:Matthias Fey, Jan E. Lenssen, Christopher Morris, Jonathan Masci, Nils M. Kriege | :venue:ICLR 2020

Meta Learning and Few-shot Learning

Few-Shot Learning with Graph Neural Networks <https://arxiv.org/abs/1711.04043>_ | :authors:Victor Garcia, Joan Bruna | :venue:ICLR 2018

Learning Steady-States of Iterative Algorithms over Graphs <http://proceedings.mlr.press/v80/dai18a.html>_ | :authors:Hanjun Dai, Zornitsa Kozareva, Bo Dai, Alex Smola, Le Song | :venue:ICML 2018

Learning to Propagate for Graph Meta-Learning <https://arxiv.org/pdf/1909.05024.pdf>_ | :authors:Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang | :venue:NeurIPS 2019

Few-Shot Learning on Graphs via Super-Classes based on Graph Spectral Measures <https://openreview.net/forum?id=Bkeeca4Kvr>_ | :authors:Jatin Chauhan, Deepak Nathani, Manohar Kaul | :venue:ICLR 2020

Automated Relational Meta-learning <https://openreview.net/pdf?id=rklp93EtwH>_ | :authors:Huaxiu Yao, Xian Wu, Zhiqiang Tao, Yaliang Li, Bolin Ding, Ruirui Li, Zhenhui Li | :venue:ICLR 2020

Structure Learning

Neural Relational Inference for Interacting Systems <https://arxiv.org/abs/1802.04687>_ | :authors:Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, Richard Zemel | :venue:ICML 2018

Brain Signal Classification via Learning Connectivity Structure <https://arxiv.org/abs/1905.11678>_ | :authors:Soobeom Jang, Seong-Eun Moon, Jong-Seok Lee | :venue:arXiv 2019

A Flexible Generative Framework for Graph-based Semi-supervised Learning <https://arxiv.org/abs/1905.10769>_ | :authors:Jiaqi Ma, Weijing Tang, Ji Zhu, Qiaozhu Mei | :venue:NeurIPS 2019

Joint embedding of structure and features via graph convolutional networks <https://arxiv.org/abs/1905.08636>_ | :authors:Sébastien Lerique, Jacob Levy Abitbol, Márton Karsai | :venue:arXiv 2019

Variational Spectral Graph Convolutional Networks <https://arxiv.org/abs/1906.01852>_ | :authors:Louis Tiao, Pantelis Elinas, Harrison Nguyen, Edwin V. Bonilla | :venue:arXiv 2019

Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning <https://arxiv.org/abs/1805.10002>_ | :authors:Yanbin Liu, Juho Lee, Minseop Park, Saehoon Kim, Eunho Yang, Sung Ju Hwang, Yi Yang | :venue:ICLR 2019

Graph Learning Network: A Structure Learning Algorithm <https://arxiv.org/abs/1905.12665>_ | :authors:Darwin Saire Pilco, Adín Ramírez Rivera | :venue:ICML 2019 Workshop

Learning Discrete Structures for Graph Neural Networks <https://arxiv.org/abs/1903.11960>_ | :authors:Luca Franceschi, Mathias Niepert, Massimiliano Pontil, Xiao He | :venue:ICML 2019

Graphite: Iterative Generative Modeling of Graphs <https://arxiv.org/abs/1803.10459>_ | :authors:Aditya Grover, Aaron Zweig, Stefano Ermon | :venue:ICML 2019

Bioinformatics and Chemistry

Protein Interface Prediction using Graph Convolutional Networks <https://papers.nips.cc/paper/7231-protein-interface-prediction-using-graph-convolutional-networks.pdf>_ | :authors:Alex Fout, Jonathon Byrd, Basir Shariat, Asa Ben-Hur | :venue:NeurIPS 2017

Modeling Polypharmacy Side Effects with Graph Convolutional Networks <https://arxiv.org/abs/1802.00543>_ | :authors:Marinka Zitnik, Monica Agrawal, Jure Leskovec | :venue:Bioinformatics 2018

NeoDTI: Neural Integration of Neighbor Information from a Heterogeneous Network for Discovering New Drug–target Interactions <https://academic.oup.com/bioinformatics/article-abstract/35/1/104/5047760?redirectedFrom=fulltext>_ | :authors:Fangping Wan, Lixiang Hong, An Xiao, Tao Jiang, Jianyang Zeng | :venue:Bioinformatics 2018

SELFIES: a Robust Representation of Semantically Constrained Graphs with an Example Application in Chemistry <https://arxiv.org/pdf/1905.13741.pdf>_ | :authors:Mario Krenn, Florian Häse, AkshatKumar Nigam, Pascal Friederich, Alán Aspuru-Guzik | :venue:arXiv 2019

Drug-Drug Adverse Effect Prediction with Graph Co-Attention <https://arxiv.org/pdf/1905.00534.pdf>_ | :authors:Andreea Deac, Yu-Hsiang Huang, Petar Veličković, Pietro Liò, Jian Tang | :venue:ICML 2019 Workshop

GCN-MF: Disease-Gene Association Identification By Graph Convolutional Networks and Matrix Factorization <https://www.kdd.org/kdd2019/accepted-papers/view/gcn-mf-disease-gene-association-identification-by-graph-convolutional-netwo>_ | :authors:Peng Han, Peng Yang, Peilin Zhao, Shuo Shang, Yong Liu, Jiayu Zhou, Xin Gao, Panos Kalnis | :venue:KDD 2019

Detecting drug-drug interactions using artificial neural networks and classic graph similarity measures <https://arxiv.org/pdf/1903.04571.pdf>_ | :authors:Guy Shtar, Lior Rokach, Bracha Shapira | :venue:arXiv 2019

PGCN: Disease gene prioritization by disease and gene embedding through graph convolutional neural networks <https://www.biorxiv.org/content/biorxiv/early/2019/01/28/532226.full.pdf>_ | :authors:Yu Li, Hiroyuki Kuwahara, Peng Yang, Le Song, Xin Gao | :venue:bioRxiv 2019

Identifying Protein-Protein Interaction using Tree LSTM and Structured Attention <https://ieeexplore.ieee.org/abstract/document/8665584>_ | :authors:Mahtab Ahmed, Jumayel Islam, Muhammad Rifayat Samee, Robert E. Mercer | :venue:ICSC 2019

GCN-MF: Disease-Gene Association Identification By Graph Convolutional Networks and Matrix Factorization <https://dl.acm.org/citation.cfm?id=3330912>_ | :authors:Peng Han, Peng Yang, Peilin Zhao, Shuo Shang, Yong Liu, Jiayu Zhou, Xin Gao, Panos Kalnis | :venue:KDD 2019

Towards perturbation prediction of biological networks using deep learning <https://www.nature.com/articles/s41598-019-48391-y>_ | :authors:Diya Li, Jianxi Gao | :venue:Nature 2019

Directional Message Passing for Molecular Graphs <https://openreview.net/pdf?id=B1eWbxStPH>_ | :authors:Johannes Klicpera, Janek Groß, Stephan Günnemann | :venue:ICLR 2020

Graph Algorithms

Neural Execution of Graph Algorithms <https://openreview.net/pdf?id=SkgKO0EtvS>_ | :authors:Petar Veličković, Rex Ying, Matilde Padovano, Raia Hadsell, Charles Blundell | :venue:ICLR 2020

Theorem Proving

Premise Selection for Theorem Proving by Deep Graph Embedding <https://arxiv.org/abs/1709.09994>_ | :authors:Mingzhe Wang, Yihe Tang, Jian Wang, Jia Deng | :venue:NeurIPS 2017

Graph Generation

GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models <https://arxiv.org/abs/1802.08773>_ | :authors:Jiaxuan You, Rex Ying, Xiang Ren, William L. Hamilton, Jure Leskovec | :venue:ICML 2018

NetGAN: Generating Graphs via Random Walks <https://arxiv.org/abs/1803.00816>_ | :authors:Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, Stephan Günnemann | :venue:ICML 2018

Learning Deep Generative Models of Graphs <https://arxiv.org/abs/1803.03324>_ | :authors:Yujia Li, Oriol Vinyals, Chris Dyer, Razvan Pascanu, Peter Battaglia | :venue:ICML 2018

Junction Tree Variational Autoencoder for Molecular Graph Generation <https://arxiv.org/abs/1802.04364>_ | :authors:Wengong Jin, Regina Barzilay, Tommi Jaakkola | :venue:ICML 2018

MolGAN: An implicit generative model for small molecular graphs <https://arxiv.org/abs/1805.11973>_ | :authors:Nicola De Cao, Thomas Kipf | :venue:arXiv 2018

Generative Modeling for Protein Structures <https://papers.nips.cc/paper/7978-generative-modeling-for-protein-structures.pdf>_ | :authors:Namrata Anand, Po-Ssu Huang | :venue:NeurIPS 2018

Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders <https://arxiv.org/abs/1809.02630>_ | :authors:Tengfei Ma, Jie Chen, Cao Xiao | :venue:NeurIPS 2018

Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation <https://arxiv.org/abs/1806.02473>_ | :authors:Jiaxuan You, Bowen Liu, Rex Ying, Vijay Pande, Jure Leskovec | :venue:NeurIPS 2018

Constrained Graph Variational Autoencoders for Molecule Design <https://arxiv.org/abs/1805.09076>_ | :authors:Qi Liu, Miltiadis Allamanis, Marc Brockschmidt, Alexander L. Gaunt | :venue:NeurIPS 2018

Learning Multimodal Graph-to-Graph Translation for Molecule Optimization <https://arxiv.org/abs/1812.01070>_ | :authors:Wengong Jin, Kevin Yang, Regina Barzilay, Tommi Jaakkola | :venue:ICLR 2019

Generative Code Modeling with Graphs <https://openreview.net/forum?id=Bke4KsA5FX>_ | :authors:Marc Brockschmidt, Miltiadis Allamanis, Alexander L. Gaunt, Oleksandr Polozov | :venue:ICLR 2019

DAG-GNN: DAG Structure Learning with Graph Neural Networks <https://arxiv.org/abs/1904.10098>_ | :authors:Yue Yu, Jie Chen, Tian Gao, Mo Yu | :venue:ICML 2019

Graph to Graph: a Topology Aware Approach for Graph Structures Learning and Generation <http://proceedings.mlr.press/v89/sun19c.html>_ | :authors:Mingming Sun, Ping Li | :venue:AISTATS 2019

Graph Normalizing Flows <https://arxiv.org/abs/1905.13177>_ | :authors:Jenny Liu, Aviral Kumar, Jimmy Ba, Jamie Kiros, Kevin Swersky | :venue:NeurIPS 2019

Conditional Structure Generation through Graph Variational Generative Adversarial Nets <http://jiyang3.web.engr.illinois.edu/files/condgen.pdf>_ | :authors:Carl Yang, Peiye Zhuang, Wenhan Shi, Alan Luu, Pan Li | :venue:NeurIPS 2019

Efficient Graph Generation with Graph Recurrent Attention Networks <https://arxiv.org/pdf/1910.00760.pdf>_ | :authors:Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, Charlie Nash, William L. Hamilton, David Duvenaud, Raquel Urtasun, Richard Zemel | :venue:NeurIPS 2019

GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation <https://openreview.net/pdf?id=S1esMkHYPr>_ | :authors:Chence Shi, Minkai Xu, Zhaocheng Zhu, Weinan Zhang, Ming Zhang, Jian Tang | :venue:ICLR 2020

Graph Layout and High-dimensional Data Visualization

Visualizing Data using t-SNE <http://www.jmlr.org/papers/volume9/vandermaaten08a/vandermaaten08a.pdf>_ | :authors:Laurens van der Maaten, Geoffrey Hinton | :venue:JMLR 2008

Visualizing non-metric similarities in multiple maps <https://link.springer.com/content/pdf/10.1007/s10994-011-5273-4.pdf>_ | :authors:Laurens van der Maaten, Geoffrey Hinton | :venue:ML 2012

Visualizing Large-scale and High-dimensional Data <https://arxiv.org/pdf/1602.00370>_ | :authors:Jian Tang, Jingzhou Liu, Ming Zhang, Qiaozhu Mei | :venue:WWW 2016

GraphTSNE: A Visualization Technique for Graph-Structured Data <https://arxiv.org/pdf/1904.06915.pdf>_ | :authors:Yao Yang Leow, Thomas Laurent, Xavier Bresson | :venue:ICLR 2019 Workshop

Graph Representation Learning Systems

GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding <https://arxiv.org/pdf/1903.00757>_ | :authors:Zhaocheng Zhu, Shizhen Xu, Meng Qu, Jian Tang | :venue:WWW 2019

PyTorch-BigGraph: A Large-scale Graph Embedding System <https://arxiv.org/pdf/1903.12287>_ | :authors:Adam Lerer, Ledell Wu, Jiajun Shen, Timothee Lacroix, Luca Wehrstedt, Abhijit Bose, Alex Peysakhovich | :venue:SysML 2019

AliGraph: A Comprehensive Graph Neural Network Platform <https://arxiv.org/pdf/1902.08730>_ | :authors:Rong Zhu, Kun Zhao, Hongxia Yang, Wei Lin, Chang Zhou, Baole Ai, Yong Li, Jingren Zhou | :venue:VLDB 2019

Deep Graph Library <https://www.dgl.ai>_ | :authors:DGL Team

AmpliGraph <https://github.com/Accenture/AmpliGraph>_ | :authors:Luca Costabello, Sumit Pai, Chan Le Van, Rory McGrath, Nicholas McCarthy, Pedro Tabacof

Euler <https://github.com/alibaba/euler>_ | :authors:Alimama Engineering Platform Team, Alimama Search Advertising Algorithm Team

Datasets

ATOMIC: an atlas of machine commonsense for if-then reasoning <https://wvvw.aaai.org/ojs/index.php/AAAI/article/download/4160/4038>_ | :authors:Maarten Sap, Ronan Le Bras, Emily Allaway, Chandra Bhagavatula, Nicholas Lourie, Hannah Rashkin, Brendan Roof, Noah A. Smith, Yejin Choi | :venue:AAAI 2019