Literature of Deep Learning for Graphs
**************************************
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`