{"id":13398965,"url":"https://github.com/DeepGraphLearning/LiteratureDL4Graph","last_synced_at":"2025-03-14T03:30:58.973Z","repository":{"id":40668754,"uuid":"192863535","full_name":"DeepGraphLearning/LiteratureDL4Graph","owner":"DeepGraphLearning","description":"A comprehensive collection of recent papers on graph deep learning","archived":false,"fork":false,"pushed_at":"2020-12-20T23:21:04.000Z","size":191,"stargazers_count":3064,"open_issues_count":5,"forks_count":565,"subscribers_count":191,"default_branch":"master","last_synced_at":"2024-07-31T19:17:32.751Z","etag":null,"topics":["arxiv","deep-learning","machine-learning","papers"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/DeepGraphLearning.png","metadata":{"files":{"readme":"README.rst","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2019-06-20T06:38:05.000Z","updated_at":"2024-07-31T17:49:55.000Z","dependencies_parsed_at":"2022-08-03T01:30:24.571Z","dependency_job_id":null,"html_url":"https://github.com/DeepGraphLearning/LiteratureDL4Graph","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DeepGraphLearning%2FLiteratureDL4Graph","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DeepGraphLearning%2FLiteratureDL4Graph/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DeepGraphLearning%2FLiteratureDL4Graph/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DeepGraphLearning%2FLiteratureDL4Graph/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/DeepGraphLearning","download_url":"https://codeload.github.com/DeepGraphLearning/LiteratureDL4Graph/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":221432671,"owners_count":16820054,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["arxiv","deep-learning","machine-learning","papers"],"created_at":"2024-07-30T19:00:33.041Z","updated_at":"2025-03-14T03:30:58.950Z","avatar_url":"https://github.com/DeepGraphLearning.png","language":null,"readme":"Literature of Deep Learning for Graphs\n**************************************\n\nThis is a paper list about deep learning for graphs.\n\n.. raw:: html\n\n    \u003cdiv\u003e\u003ca href=\"README.rst\"\u003eSort by topic\u003c/a\u003e\u003c/div\u003e\n    \u003cdiv\u003e\u003ca href=\"BYVENUE.rst\"\u003eSort by venue\u003c/a\u003e\u003c/div\u003e\n\n.. contents::\n    :local:\n    :depth: 2\n\n.. sectnum::\n    :depth: 2\n\n.. role:: authors(emphasis)\n\n.. role:: venue(strong)\n\n.. role:: keywords(emphasis)\n\nNode Representation Learning\n============================\n\nUnsupervised Node Representation Learning\n-----------------------------------------\n\n`DeepWalk: Online Learning of Social Representations\n\u003chttps://arxiv.org/pdf/1403.6652\u003e`_\n    | :authors:`Bryan Perozzi, Rami Al-Rfou, Steven Skiena`\n    | :venue:`KDD 2014`\n    | :keywords:`Node classification, Random walk, Skip-gram`\n\n`LINE: Large-scale Information Network Embedding\n\u003chttps://arxiv.org/pdf/1503.03578\u003e`_\n    | :authors:`Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, Qiaozhu Mei`\n    | :venue:`WWW 2015`\n    | :keywords:`First-order, Second-order, Node classification`\n\n`GraRep: Learning Graph Representations with Global Structural Information\n\u003chttps://dl.acm.org/citation.cfm?id=2806512\u003e`_\n    | :authors:`Shaosheng Cao, Wei Lu, Qiongkai Xu`\n    | :venue:`CIKM 2015`\n    | :keywords:`High-order, SVD`\n\n`node2vec: Scalable Feature Learning for Networks\n\u003chttps://arxiv.org/pdf/1607.00653\u003e`_\n    | :authors:`Aditya Grover, Jure Leskovec`\n    | :venue:`KDD 2016`\n    | :keywords:`Breadth-first Search, Depth-first Search, Node Classification, Link Prediction`\n\n`Variational Graph Auto-Encoders\n\u003chttps://arxiv.org/abs/1611.07308\u003e`_\n    | :authors:`Thomas N. Kipf, Max Welling`\n    | :venue:`arXiv 2016`\n\n`Scalable Graph Embedding for Asymmetric Proximity\n\u003chttps://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14696\u003e`_\n    | :authors:`Chang Zhou, Yuqiong Liu, Xiaofei Liu, Zhongyi Liu, Jun Gao`\n    | :venue:`AAAI 2017`\n\n`Fast Network Embedding Enhancement via High Order Proximity Approximation\n\u003chttps://www.ijcai.org/proceedings/2017/544\u003e`_\n    | :authors:`Cheng Yang, Maosong Sun, Zhiyuan Liu, Cunchao Tu`\n    | :venue:`IJCAI 2017`\n\n`struc2vec: Learning Node Representations from Structural Identity\n\u003chttps://arxiv.org/pdf/1704.03165\u003e`_\n    | :authors:`Leonardo F. R. Ribeiro, Pedro H. P. Savarese, Daniel R. Figueiredo`\n    | :venue:`KDD 2017`\n    | :keywords:`Structural Identity`\n\n`Poincaré Embeddings for Learning Hierarchical Representations\n\u003chttps://arxiv.org/pdf/1705.08039\u003e`_\n    | :authors:`Maximilian Nickel, Douwe Kiela`\n    | :venue:`NIPS 2017`\n\n`VERSE: Versatile Graph Embeddings from Similarity Measures\n\u003chttps://arxiv.org/pdf/1803.04742\u003e`_\n    | :authors:`Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Emmanuel Müller`\n    | :venue:`WWW 2018`\n\n`Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec\n\u003chttps://arxiv.org/pdf/1710.02971\u003e`_\n    | :authors:`Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, Jie Tang`\n    | :venue:`WSDM 2018`\n\n`Learning Structural Node Embeddings via Diffusion Wavelets\n\u003chttps://arxiv.org/pdf/1710.10321\u003e`_\n    | :authors:`Claire Donnat, Marinka Zitnik, David Hallac, Jure Leskovec`\n    | :venue:`KDD 2018`\n\n`Adversarial Network Embedding\n\u003chttps://arxiv.org/pdf/1711.07838\u003e`_\n    | :authors:`Quanyu Dai, Qiang Li, Jian Tang, Dan Wang`\n    | :venue:`AAAI 2018`\n\n`GraphGAN: Graph Representation Learning with Generative Adversarial Nets\n\u003chttps://arxiv.org/pdf/1711.08267\u003e`_\n    | :authors:`Hongwei Wang, Jia Wang, Jialin Wang, Miao Zhao, Weinan Zhang, Fuzheng Zhang, Xing Xie, Minyi Guo`\n    | :venue:`AAAI 2018`\n\n`A General View for Network Embedding as Matrix Factorization\n\u003chttps://dl.acm.org/citation.cfm?id=3291029\u003e`_\n    | :authors:`Xin Liu, Tsuyoshi Murata, Kyoung-Sook Kim, Chatchawan Kotarasu, Chenyi Zhuang`\n    | :venue:`WSDM 2019`\n\n`Deep Graph Infomax\n\u003chttps://arxiv.org/pdf/1809.10341\u003e`_\n    | :authors:`Petar Veličković, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, R Devon Hjelm`\n    | :venue:`ICLR 2019`\n\n`NetSMF: Large-Scale Network Embedding as Sparse Matrix Factorization\n\u003chttp://keg.cs.tsinghua.edu.cn/jietang/publications/www19-Qiu-et-al-NetSMF-Large-Scale-Network-Embedding.pdf\u003e`_\n    | :authors:`Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Chi Wang, Kuansan Wang, Jie Tang`\n    | :venue:`WWW 2019`\n\n`Adversarial Training Methods for Network Embedding\n\u003chttps://dl.acm.org/citation.cfm?id=3313445\u003e`_\n    | :authors:`Quanyu Dai, Xiao Shen, Liang Zhang, Qiang Li, Dan Wang`\n    | :venue:`WWW 2019`\n\n`vGraph: A Generative Model for Joint Community Detection and Node Representation Learning\n\u003chttps://arxiv.org/pdf/1906.07159.pdf\u003e`_\n    | :authors:`Fan-Yun Sun, Meng Qu, Jordan Hoffmann, Chin-Wei Huang, Jian Tang`\n    | :venue:`NeurIPS 2019`\n\n`ProGAN: Network Embedding via Proximity Generative Adversarial Network\n\u003chttps://dl.acm.org/citation.cfm?id=3330866\u003e`_\n    | :authors:`Hongchang Gao, Jian Pei, Heng Huang`\n    | :venue:`KDD 2019`\n\n`GraphZoom: A Multi-level Spectral Approach for Accurate and Scalable Graph Embedding\n\u003chttps://openreview.net/pdf?id=r1lGO0EKDH\u003e`_\n\t| :authors:`Chenhui Deng, Zhiqiang Zhao, Yongyu Wang, Zhiru Zhang, Zhuo Feng`\n\t| :venue:`ICLR 2020`\n\nNode Representation Learning in Heterogeneous Graphs\n----------------------------------------------------\n\n`Learning Latent Representations of Nodes for Classifying in Heterogeneous Social Networks\n\u003chttps://dl.acm.org/citation.cfm?id=2556225\u003e`_\n    | :authors:`Yann Jacob, Ludovic Denoyer, Patrick Gallinari`\n    | :venue:`WSDM 2014`\n\n`PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks\n\u003chttps://arxiv.org/pdf/1508.00200\u003e`_\n    | :authors:`Jian Tang, Meng Qu, Qiaozhu Mei`\n    | :venue:`KDD 2015`\n    | :keywords:`Text Embedding, Heterogeneous Text Graphs`\n\n`Heterogeneous Network Embedding via Deep Architectures\n\u003chttps://dl.acm.org/citation.cfm?id=2783296\u003e`_\n    | :authors:`Shiyu Chang, Wei Han, Jiliang Tang, Guo-Jun Qi, Charu C. Aggarwal, Thomas S. Huang`\n    | :venue:`KDD 2015`\n\n`Network Representation Learning with Rich Text Information\n\u003chttps://www.aaai.org/ocs/index.php/IJCAI/IJCAI15/paper/view/11098\u003e`_\n    | :authors:`Cheng Yang, Zhiyuan Liu, Deli Zhao, Maosong Sun, Edward Chang`\n    | :venue:`AAAI 2015`\n\n`Max-Margin DeepWalk: Discriminative Learning of Network Representation\n\u003chttps://www.ijcai.org/Proceedings/16/Papers/547.pdf\u003e`_\n    | :authors:`Cunchao Tu, Weicheng Zhang, Zhiyuan Liu, Maosong Sun`\n    | :venue:`IJCAI 2016`\n\n`metapath2vec: Scalable Representation Learning for Heterogeneous Networks\n\u003chttps://dl.acm.org/citation.cfm?id=3098036\u003e`_\n    | :authors:`Yuxiao Dong, Nitesh V. Chawla, Ananthram Swami`\n    | :venue:`KDD 2017`\n\n`Meta-Path Guided Embedding for Similarity Search in Large-Scale Heterogeneous Information Networks\n\u003chttps://arxiv.org/pdf/1610.09769\u003e`_\n    | :authors:`Jingbo Shang, Meng Qu, Jialu Liu, Lance M. Kaplan, Jiawei Han, Jian Peng`\n    | :venue:`arXiv 2016`\n\n`HIN2Vec: Explore Meta-paths in Heterogeneous Information Networks for Representation Learning\n\u003chttps://dl.acm.org/citation.cfm?id=3132953\u003e`_\n    | :authors:`Tao-yang Fu, Wang-Chien Lee, Zhen Lei`\n    | :venue:`CIKM 2017`\n\n`An Attention-based Collaboration Framework for Multi-View Network Representation Learning\n\u003chttps://arxiv.org/pdf/1709.06636\u003e`_\n    | :authors:`Meng Qu, Jian Tang, Jingbo Shang, Xiang Ren, Ming Zhang, Jiawei Han`\n    | :venue:`CIKM 2017`\n\n`Multi-view Clustering with Graph Embedding for Connectome Analysis\n\u003chttps://dl.acm.org/citation.cfm?id=3132909\u003e`_\n    | :authors:`Guixiang Ma, Lifang He, Chun-Ta Lu, Weixiang Shao, Philip S. Yu, Alex D. Leow, Ann B. Ragin`\n    | :venue:`CIKM 2017`\n\n`Attributed Signed Network Embedding\n\u003chttps://dl.acm.org/citation.cfm?id=3132847.3132905\u003e`_\n    | :authors:`Suhang Wang, Charu Aggarwal, Jiliang Tang, Huan Liu`\n    | :venue:`CIKM 2017`\n\n`CANE: Context-Aware Network Embedding for Relation Modeling\n\u003chttps://aclweb.org/anthology/papers/P/P17/P17-1158/\u003e`_\n    | :authors:`Cunchao Tu, Han Liu, Zhiyuan Liu, Maosong Sun`\n    | :venue:`ACL 2017`\n\n`PME: Projected Metric Embedding on Heterogeneous Networks for Link Prediction\n\u003chttps://dl.acm.org/citation.cfm?id=3219986\u003e`_\n    | :authors:`Hongxu Chen, Hongzhi Yin, Weiqing Wang, Hao Wang, Quoc Viet Hung Nguyen, Xue Li`\n    | :venue:`KDD 2018`\n\n`BiNE: Bipartite Network Embedding\n\u003chttps://dl.acm.org/citation.cfm?id=3209978.3209987\u003e`_\n    | :authors:`Ming Gao, Leihui Chen, Xiangnan He, Aoying Zhou`\n    | :venue:`SIGIR 2018`\n\n`StarSpace: Embed All The Things\n\u003chttps://arxiv.org/pdf/1709.03856\u003e`_\n    | :authors:`Ledell Wu, Adam Fisch, Sumit Chopra, Keith Adams, Antoine Bordes, Jason Weston`\n    | :venue:`AAAI 2018`\n\n`Exploring Expert Cognition for Attributed Network Embedding\n\u003chttps://dl.acm.org/citation.cfm?id=3159655\u003e`_\n    | :authors:`Xiao Huang, Qingquan Song, Jundong Li, Xia Hu`\n    | :venue:`WSDM 2018`\n\n`SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction\n\u003chttps://arxiv.org/pdf/1712.00732\u003e`_\n    | :authors:`Hongwei Wang, Fuzheng Zhang, Min Hou, Xing Xie, Minyi Guo, Qi Liu`\n    | :venue:`WSDM 2018`\n\n`Multidimensional Network Embedding with Hierarchical Structures\n\u003chttps://dl.acm.org/citation.cfm?id=3159680\u003e`_\n    | :authors:`Yao Ma, Zhaochun Ren, Ziheng Jiang, Jiliang Tang, Dawei Yin`\n    | :venue:`WSDM 2018`\n\n`Curriculum Learning for Heterogeneous Star Network Embedding via Deep Reinforcement Learning\n\u003chttps://dl.acm.org/citation.cfm?id=3159711\u003e`_\n    | :authors:`Meng Qu, Jian Tang, Jiawei Han`\n    | :venue:`WSDM 2018`\n\n`Generative Adversarial Network based Heterogeneous Bibliographic Network Representation for Personalized Citation Recommendation\n\u003chttps://www.semanticscholar.org/paper/Generative-Adversarial-Network-Based-Heterogeneous-Cai-Han/1596d6487012696ba400fb69904a2c372a08a2be\u003e`_\n    | :authors:`Xiaoyan Cai, Junwei Han, Libin Yang`\n    | :venue:`AAAI 2018`\n\n`ANRL: Attributed Network Representation Learning via Deep Neural Networks\n\u003chttps://www.ijcai.org/proceedings/2018/438\u003e`_\n    | :authors:`Zhen Zhang, Hongxia Yang, Jiajun Bu, Sheng Zhou, Pinggang Yu, Jianwei Zhang, Martin Ester, Can Wang`\n    | :venue:`IJCAI 2018`\n\n`Efficient Attributed Network Embedding via Recursive Randomized Hashing\n\u003chttps://www.ijcai.org/proceedings/2018/397\u003e`_\n    | :authors:`Wei Wu, Bin Li, Ling Chen, Chengqi Zhang`\n    | :venue:`IJCAI 2018`\n\n`Deep Attributed Network Embedding\n\u003chttps://www.ijcai.org/proceedings/2018/467\u003e`_\n    | :authors:`Hongchang Gao, Heng Huang`\n    | :venue:`IJCAI 2018`\n\n`Co-Regularized Deep Multi-Network Embedding\n\u003chttps://dl.acm.org/citation.cfm?id=3186113\u003e`_\n    | :authors:`Jingchao Ni, Shiyu Chang, Xiao Liu, Wei Cheng, Haifeng Chen, Dongkuan Xu, Xiang Zhang`\n    | :venue:`WWW 2018`\n\n`Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks\n\u003chttps://arxiv.org/pdf/1807.03490\u003e`_\n    | :authors:`Yu Shi, Qi Zhu, Fang Guo, Chao Zhang, Jiawei Han`\n    | :venue:`KDD 2018`\n\n`Meta-Graph Based HIN Spectral Embedding: Methods, Analyses, and Insights\n\u003chttps://www.semanticscholar.org/paper/Meta-Graph-Based-HIN-Spectral-Embedding%3A-Methods%2C-Yang-Feng/4d5f4d6785d550383e3f3afb04c3015bf0d28405\u003e`_\n    | :authors:`Carl Yang, Yichen Feng, Pan Li, Yu Shi, Jiawei Han`\n    | :venue:`ICDM 2018`\n\n`SIDE: Representation Learning in Signed Directed Networks\n\u003chttps://dl.acm.org/citation.cfm?id=3186117\u003e`_\n    | :authors:`Junghwan Kim, Haekyu Park, Ji-Eun Lee, U Kang`\n    | :venue:`WWW 2018`\n\n`Learning Network-to-Network Model for Content-rich Network Embedding\n\u003chttps://dl.acm.org/citation.cfm?id=3330924\u003e`_\n    | :authors:`\tZhicheng He, Jie Liu, Na Li, Yalou Huang`\n    | :venue:`KDD 2019`\n\nNode Representation Learning in Dynamic Graphs\n----------------------------------------------\n\n`Know-evolve: Deep temporal reasoning for dynamic knowledge graphs\n\u003chttps://arxiv.org/pdf/1705.05742.pdf\u003e`_\n    | :authors:`Rakshit Trivedi, Hanjun Dai, Yichen Wang, Le Song`\n    | :venue:`ICML 2017`\n\n`Dyngem: Deep embedding method for dynamic graphs\n\u003chttps://arxiv.org/pdf/1805.11273.pdf\u003e`_\n    | :authors:`Palash Goyal, Nitin Kamra, Xinran He, Yan Liu`\n    | :venue:`ICLR 2017 Workshop`\n\n`Attributed network embedding for learning in a dynamic environment\n\u003chttps://arxiv.org/pdf/1706.01860.pdf\u003e`_\n    | :authors:`Jundong Li, Harsh Dani, Xia Hu, Jiliang Tang, Yi Chang, Huan Liu`\n    | :venue:`CIKM 2017`\n\n`Dynamic Network Embedding by Modeling Triadic Closure Process\n\u003chttp://yangy.org/works/dynamictriad/dynamic_triad.pdf\u003e`_\n    | :authors:`Lekui Zhou, Yang Yang, Xiang Ren, Fei Wu, Yueting Zhuang`\n    | :venue:`AAAI 2018`\n\n`DepthLGP: Learning Embeddings of Out-of-Sample Nodes in Dynamic Networks\n\u003chttps://pdfs.semanticscholar.org/9499/b38866b1eb87ae43fa5be02f9d08cd3c20a8.pdf?_ga=2.6780794.935636364.1561139530-1831876308.1523264869\u003e`_\n    | :authors:`Jianxin Ma, Peng Cui, Wenwu Zhu`\n    | :venue:`AAAI 2018`\n\n`TIMERS: Error-Bounded SVD Restart on Dynamic Networks\n\u003chttps://arxiv.org/pdf/1711.09541.pdf\u003e`_\n    | :authors:`Ziwei Zhang, Peng Cui, Jian Pei, Xiao Wang, Wenwu Zhu`\n    | :venue:`AAAI 2018`\n\n`Dynamic Embeddings for User Profiling in Twitter\n\u003chttps://dl.acm.org/citation.cfm?id=3219819.3220043\u003e`_\n    | :authors:`Shangsong Liang, Xiangliang Zhang, Zhaochun Ren, Evangelos Kanoulas`\n    | :venue:`KDD 2018`\n\n`Dynamic Network Embedding : An Extended Approach for Skip-gram based Network Embedding\n\u003chttps://www.ijcai.org/proceedings/2018/0288.pdf\u003e`_\n    | :authors:`Lun Du, Yun Wang, Guojie Song, Zhicong Lu, Junshan Wang`\n    | :venue:`IJCAI 2018`\n\n`DyRep: Learning Representations over Dynamic Graphs\n\u003chttps://openreview.net/pdf?id=HyePrhR5KX\u003e`_\n    | :authors:`Rakshit Trivedi, Mehrdad Farajtabar, Prasenjeet Biswal, Hongyuan Zha`\n    | :venue:`ICLR 2019`\n\n`Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks\n\u003chttps://cs.stanford.edu/~srijan/pubs/jodie-kdd2019.pdf\u003e`_\n    | :authors:`Srijan Kumar, Xikun Zhang, Jure Leskovec`\n    | :venue:`KDD 2019`\n\n`Variational Graph Recurrent Neural Networks\n\u003chttps://arxiv.org/pdf/1908.09710.pdf\u003e`_\n    | :authors:`Ehsan Hajiramezanali, Arman Hasanzadeh, Nick Duffield, Krishna R Narayanan, Mingyuan Zhou, Xiaoning Qian`\n    | :venue:`NeurIPS 2019`\n\n`Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks\n\u003chttps://arxiv.org/pdf/1907.03395.pdf\u003e`_\n    | :authors:`Vineet Kosaraju, Amir Sadeghian, Roberto Martín-Martín, Ian Reid, S. Hamid Rezatofighi, Silvio Savarese`\n    | :venue:`NeurIPS 2019`\n\nKnowledge Graph Embedding\n=========================\n\n`A Three-Way Model for Collective Learning on Multi-Relational Data.\n\u003chttp://www.icml-2011.org/papers/438_icmlpaper.pdf\u003e`_\n    | :authors:`Maximilian Nickel, Volker Tresp, Hans-Peter Kriegel`\n    | :venue:`ICML 2011`\n\n`Translating Embeddings for Modeling Multi-relational Data\n\u003chttps://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data.pdf\u003e`_\n    | :authors:`Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, Oksana Yakhnenko`\n    | :venue:`NIPS 2013`\n\n`Knowledge Graph Embedding by Translating on Hyperplanes\n\u003chttps://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/viewFile/8531/8546\u003e`_\n    | :authors:`Zhen Wang, Jianwen Zhang, Jianlin Feng, Zheng Chen`\n    | :venue:`AAAI 2014`\n\n`Reducing the Rank of Relational Factorization Models by Including Observable Patterns\n\u003chttp://papers.nips.cc/paper/5448-reducing-the-rank-in-relational-factorization-models-by-including-observable-patterns.pdf\u003e`_\n    | :authors:`Maximilian Nickel, Xueyan Jiang, Volker Tresp`\n    | :venue:`NIPS 2014`\n\n`Learning Entity and Relation Embeddings for Knowledge Graph Completion\n\u003chttps://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/viewFile/9571/9523\u003e`_\n    | :authors:`Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, Xuan Zhu`\n    | :venue:`AAAI 2015`\n\n`A Review of Relational Machine Learning for Knowledge Graph\n\u003chttps://arxiv.org/pdf/1503.00759.pdf\u003e`_\n    | :authors:`Maximilian Nickel, Kevin Murphy, Volker Tresp, Evgeniy Gabrilovich`\n    | :venue:`IEEE 2015`\n\n`Knowledge Graph Embedding via Dynamic Mapping Matrix\n\u003chttps://www.aclweb.org/anthology/P15-1067\u003e`_\n    | :authors:`Guoliang Ji, Shizhu He, Liheng Xu, Kang Liu, Jun Zha`\n    | :venue:`ACL 2015`\n\n`Modeling Relation Paths for Representation Learning of Knowledge Bases\n\u003chttps://arxiv.org/pdf/1506.00379\u003e`_\n    | :authors:`Yankai Lin, Zhiyuan Liu, Huanbo Luan, Maosong Sun, Siwei Rao, Song Liu`\n    | :venue:`EMNLP 2015`\n\n`Embedding Entities and Relations for Learning and Inference in Knowledge Bases\n\u003chttps://arxiv.org/pdf/1412.6575\u003e`_\n    | :authors:`Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, Li Deng`\n    | :venue:`ICLR 2015`\n\n`Holographic Embeddings of Knowledge Graphs\n\u003chttps://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/viewPDFInterstitial/12484/11828\u003e`_\n    | :authors:`Maximilian Nickel, Lorenzo Rosasco, Tomaso Poggio`\n    | :venue:`AAAI 2016`\n\n`Complex Embeddings for Simple Link Prediction\n\u003chttp://www.jmlr.org/proceedings/papers/v48/trouillon16.pdf\u003e`_\n    | :authors:`Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, Guillaume Bouchard`\n    | :venue:`ICML 2016`\n\n`Modeling Relational Data with Graph Convolutional Networks\n\u003chttps://arxiv.org/pdf/1703.06103\u003e`_\n    | :authors:`Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, Max Welling`\n    | :venue:`arXiv 2017`\n\n`Fast Linear Model for Knowledge Graph Embeddings\n\u003chttps://arxiv.org/pdf/1710.10881\u003e`_\n    | :authors:`Armand Joulin, Edouard Grave, Piotr Bojanowski, Maximilian Nickel, Tomas Mikolov`\n    | :venue:`arXiv 2017`\n\n`Convolutional 2D Knowledge Graph Embeddings\n\u003chttps://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/download/17366/15884\u003e`_\n    | :authors:`Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, Sebastian Riedel`\n    | :venue:`AAAI 2018`\n\n`Knowledge Graph Embedding With Iterative Guidance From Soft Rules\n\u003chttps://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/download/16369/16011\u003e`_\n    | :authors:`Shu Guo, Quan Wang, Lihong Wang, Bin Wang, Li Guo`\n    | :venue:`AAAI 2018`\n\n`KBGAN: Adversarial Learning for Knowledge Graph Embeddings\n\u003chttps://arxiv.org/abs/1711.04071\u003e`_\n    | :authors:`Liwei Cai, William Yang Wang`\n    | :venue:`NAACL 2018`\n\n`Improving Knowledge Graph Embedding Using Simple Constraints\n\u003chttps://arxiv.org/abs/1805.02408\u003e`_\n    | :authors:`Boyang Ding, Quan Wang, Bin Wang, Li Guo`\n    | :venue:`ACL 2018`\n\n`SimplE Embedding for Link Prediction in Knowledge Graphs\n\u003chttps://arxiv.org/abs/1802.04868\u003e`_\n    | :authors:`Seyed Mehran Kazemi, David Poole`\n    | :venue:`NeurIPS 2018`\n\n`A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network\n\u003chttps://aclweb.org/anthology/papers/N/N18/N18-2053/\u003e`_\n    | :authors:`Dai Quoc Nguyen, Tu Dinh Nguyen, Dat Quoc Nguyen, Dinh Phung`\n    | :venue:`NAACL 2018`\n\n`Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning\n\u003chttps://arxiv.org/abs/1903.08948\u003e`_\n    | :authors:`Wen Zhang, Bibek Paudel, Liang Wang, Jiaoyan Chen, Hai Zhu, Wei Zhang, Abraham Bernstein, Huajun Chen`\n    | :venue:`WWW 2019`\n\n`RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space\n\u003chttps://arxiv.org/abs/1902.10197\u003e`_\n    | :authors:`Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, Jian Tang`\n    | :venue:`ICLR 2019`\n\n`Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs\n\u003chttps://arxiv.org/abs/1906.01195\u003e`_\n    | :authors:`Deepak Nathani, Jatin Chauhan, Charu Sharma, Manohar Kaul`\n    | :venue:`ACL 2019`\n\n`Probabilistic Logic Neural Networks for Reasoning\n\u003chttps://arxiv.org/pdf/1906.08495.pdf\u003e`_\n    | :authors:`Meng Qu, Jian Tang`\n    | :venue:`NeurIPS 2019`\n\n`Quaternion Knowledge Graph Embeddings\n\u003chttps://arxiv.org/pdf/1904.10281.pdf\u003e`_\n    | :authors:`Shuai Zhang, Yi Tay, Lina Yao, Qi Liu`\n    | :venue:`NeurIPS 2019`\n\n`Quantum Embedding of Knowledge for Reasoning\n\u003chttps://papers.nips.cc/paper/8797-quantum-embedding-of-knowledge-for-reasoning.pdf\u003e`_\n\t| :authors:`Dinesh Garg, Santosh K. Srivastava, Hima Karanam`\n\t| :venue:`NeurIPS 2019`\n\t\n`Multi-relational Poincaré Graph Embeddings\n\u003chttps://arxiv.org/pdf/1905.09791.pdf\u003e`_\n    | :authors:`Ivana Balaževic, Carl Allen, Timothy Hospedales`\n    | :venue:`NeurIPS 2019`\n\n`Dynamically Pruned Message Passing Networks for Large-scale Knowledge Graph Reasoning\n\u003chttps://openreview.net/forum?id=rkeuAhVKvB\u003e`_\n\t| :authors:`Xiaoran Xu, Wei Feng, Yunsheng Jiang, Xiaohui Xie, Zhiqing Sun, Zhi-Hong Deng`\n\t| :venue:`ICLR 2020`\n\t\nGraph Neural Networks\n=====================\n\n`Revisiting Semi-supervised Learning with Graph Embeddings\n\u003chttps://arxiv.org/pdf/1603.08861\u003e`_\n    | :authors:`Zhilin Yang, William W. Cohen, Ruslan Salakhutdinov`\n    | :venue:`ICML 2016`\n\n`Semi-Supervised Classification with Graph Convolutional Networks\n\u003chttps://arxiv.org/pdf/1609.02907\u003e`_\n    | :authors:`Thomas N. Kipf, Max Welling`\n    | :venue:`ICLR 2017`\n\n`Neural Message Passing for Quantum Chemistry\n\u003chttps://arxiv.org/pdf/1704.01212\u003e`_\n    | :authors:`Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, George E. Dahl`\n    | :venue:`ICML 2017`\n\n`Motif-Aware Graph Embeddings\n\u003chttp://gearons.org/assets/docs/motif-aware-graph-final.pdf\u003e`_\n    | :authors:`Hoang Nguyen, Tsuyoshi Murata`\n    | :venue:`IJCAI 2017`\n\n`Learning Graph Representations with Embedding Propagation\n\u003chttps://arxiv.org/pdf/1710.03059\u003e`_\n    | :authors:`Alberto Garcia-Duran, Mathias Niepert`\n    | :venue:`NIPS 2017`\n\n`Inductive Representation Learning on Large Graphs\n\u003chttps://arxiv.org/pdf/1706.02216\u003e`_\n    | :authors:`William L. Hamilton, Rex Ying, Jure Leskovec`\n    | :venue:`NIPS 2017`\n\n`Graph Attention Networks\n\u003chttps://arxiv.org/pdf/1710.10903\u003e`_\n    | :authors:`Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, Yoshua Bengio`\n    | :venue:`ICLR 2018`\n\n`FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling\n\u003chttps://arxiv.org/pdf/1801.10247\u003e`_\n    | :authors:`Jie Chen, Tengfei Ma, Cao Xiao`\n    | :venue:`ICLR 2018`\n\n`Representation Learning on Graphs with Jumping Knowledge Networks\n\u003chttps://arxiv.org/pdf/1806.03536\u003e`_\n    | :authors:`Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, Stefanie Jegelka`\n    | :venue:`ICML 2018`\n\n`Stochastic Training of Graph Convolutional Networks with Variance Reduction\n\u003chttps://arxiv.org/pdf/1710.10568\u003e`_\n    | :authors:`Jianfei Chen, Jun Zhu, Le Song`\n    | :venue:`ICML 2018`\n\n`Large-Scale Learnable Graph Convolutional Networks\n\u003chttps://arxiv.org/pdf/1808.03965\u003e`_\n    | :authors:`Hongyang Gao, Zhengyang Wang, Shuiwang Ji`\n    | :venue:`KDD 2018`\n\n`Adaptive Sampling Towards Fast Graph Representation Learning\n\u003chttps://papers.nips.cc/paper/7707-adaptive-sampling-towards-fast-graph-representation-learning.pdf\u003e`_\n    | :authors:`Wenbing Huang, Tong Zhang, Yu Rong, Junzhou Huang`\n    | :venue:`NeurIPS 2018`\n\n`Hierarchical Graph Representation Learning with Differentiable Pooling\n\u003chttps://arxiv.org/pdf/1806.08804\u003e`_\n    | :authors:`Rex Ying, Jiaxuan You, Christopher Morris, Xiang Ren, William L. Hamilton, Jure Leskovec`\n    | :venue:`NeurIPS 2018`\n\n`Bayesian Semi-supervised Learning with Graph Gaussian Processes\n\u003chttps://papers.nips.cc/paper/7440-bayesian-semi-supervised-learning-with-graph-gaussian-processes.pdf\u003e`_\n    | :authors:`Yin Cheng Ng, Nicolò Colombo, Ricardo Silva`\n    | :venue:`NeurIPS 2018`\n\n`Pitfalls of Graph Neural Network Evaluation\n\u003chttps://arxiv.org/pdf/1811.05868\u003e`_\n    | :authors:`Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, Stephan Günnemann`\n    | :venue:`arXiv 2018`\n\n`Heterogeneous Graph Attention Network\n\u003chttps://arxiv.org/pdf/1903.07293\u003e`_\n    | :authors:`Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Peng Cui, P. Yu, Yanfang Ye`\n    | :venue:`WWW 2019`\n\n`Bayesian graph convolutional neural networks for semi-supervised classification\n\u003chttps://arxiv.org/pdf/1811.11103.pdf\u003e`_\n    | :authors:`Yingxue Zhang, Soumyasundar Pal, Mark Coates, Deniz Üstebay`\n    | :venue:`AAAI 2019`\n\n`How Powerful are Graph Neural Networks?\n\u003chttps://arxiv.org/pdf/1810.00826\u003e`_\n    | :authors:`Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka`\n    | :venue:`ICLR 2019`\n\n`LanczosNet: Multi-Scale Deep Graph Convolutional Networks\n\u003chttps://arxiv.org/pdf/1901.01484\u003e`_\n    | :authors:`Renjie Liao, Zhizhen Zhao, Raquel Urtasun, Richard S. Zemel`\n    | :venue:`ICLR 2019`\n\n`Graph Wavelet Neural Network\n\u003chttps://arxiv.org/pdf/1904.07785\u003e`_\n    | :authors:`Bingbing Xu, Huawei Shen, Qi Cao, Yunqi Qiu, Xueqi Cheng`\n    | :venue:`ICLR 2019`\n\n`Supervised Community Detection with Line Graph Neural Networks\n\u003chttps://openreview.net/pdf?id=H1g0Z3A9Fm\u003e`_\n    | :authors:`Zhengdao Chen, Xiang Li, Joan Bruna`\n    | :venue:`ICLR 2019`\n\n`Predict then Propagate: Graph Neural Networks meet Personalized PageRank\n\u003chttps://arxiv.org/pdf/1810.05997\u003e`_\n    | :authors:`Johannes Klicpera, Aleksandar Bojchevski, Stephan Günnemann`\n    | :venue:`ICLR 2019`\n\n`Invariant and Equivariant Graph Networks\n\u003chttps://arxiv.org/pdf/1812.09902\u003e`_\n    | :authors:`Haggai Maron, Heli Ben-Hamu, Nadav Shamir, Yaron Lipman`\n    | :venue:`ICLR 2019`\n\n`Capsule Graph Neural Network\n\u003chttps://openreview.net/pdf?id=Byl8BnRcYm\u003e`_\n    | :authors:`Zhang Xinyi, Lihui Chen`\n    | :venue:`ICLR 2019`\n\n`MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing\n\u003chttps://arxiv.org/pdf/1905.00067\u003e`_\n    | :authors:`Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, Aram Galstyan`\n    | :venue:`ICML 2019`\n\n`Graph U-Nets\n\u003chttps://arxiv.org/pdf/1905.05178\u003e`_\n    | :authors:`Hongyang Gao, Shuiwang Ji`\n    | :venue:`ICML 2019`\n\n`Disentangled Graph Convolutional Networks\n\u003chttp://proceedings.mlr.press/v97/ma19a/ma19a.pdf\u003e`_\n    | :authors:`Jianxin Ma, Peng Cui, Kun Kuang, Xin Wang, Wenwu Zhu`\n    | :venue:`ICML 2019`\n\n`GMNN: Graph Markov Neural Networks\n\u003chttps://arxiv.org/pdf/1905.06214\u003e`_\n    | :authors:`Meng Qu, Yoshua Bengio, Jian Tang`\n    | :venue:`ICML 2019`\n\n`Simplifying Graph Convolutional Networks\n\u003chttps://arxiv.org/pdf/1902.07153\u003e`_\n    | :authors:`Felix Wu, Tianyi Zhang, Amauri Holanda de Souza Jr., Christopher Fifty, Tao Yu, Kilian Q. Weinberger`\n    | :venue:`ICML 2019`\n\n`Position-aware Graph Neural Networks\n\u003chttps://arxiv.org/pdf/1906.04817\u003e`_\n    | :authors:`Jiaxuan You, Rex Ying, Jure Leskovec`\n    | :venue:`ICML 2019`\n\n`Self-Attention Graph Pooling\n\u003chttps://arxiv.org/pdf/1904.08082\u003e`_\n    | :authors:`Junhyun Lee, Inyeop Lee, Jaewoo Kang`\n    | :venue:`ICML 2019`\n\n`Relational Pooling for Graph Representations\n\u003chttps://arxiv.org/pdf/1903.02541\u003e`_\n    | :authors:`Ryan L. Murphy, Balasubramaniam Srinivasan, Vinayak Rao, Bruno Ribeiro`\n    | :venue:`ICML 2019`\n\n`Graph Representation Learning via Hard and Channel-Wise Attention Networks\n\u003chttps://arxiv.org/pdf/1907.04652.pdf\u003e`_\n    | :authors:`Hongyang Gao, Shuiwang Ji`\n    | :venue:`KDD 2019`\n\n`Conditional Random Field Enhanced Graph Convolutional Neural Networks\n\u003chttps://www.kdd.org/kdd2019/accepted-papers/view/conditional-random-field-enhanced-graph-convolutional-neural-networks\u003e`_\n    | :authors:`Hongchang Gao, Jian Pei, Heng Huang`\n    | :venue:`KDD 2019`\n\n`Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks\n\u003chttps://arxiv.org/abs/1905.07953\u003e`_\n    | :authors:`Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, Cho-Jui Hsieh`\n    | :venue:`KDD 2019`\n\n`DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification\n\u003chttps://arxiv.org/abs/1906.02319\u003e`_\n    | :authors:`Jun Wu, Jingrui He, Jiejun Xu`\n    | :venue:`KDD 2019`\n\n`HetGNN: Heterogeneous Graph Neural Network\n\u003chttps://www.kdd.org/kdd2019/accepted-papers/view/hetgnn-heterogeneous-graph-neural-network\u003e`_\n    | :authors:`Chuxu Zhang, Dongjin Song, Chao Huang, Ananthram Swami, Nitesh V. Chawla`\n    | :venue:`KDD 2019`\n\n`Graph Recurrent Networks with Attributed Random Walks\n\u003chttps://dl.acm.org/citation.cfm?id=3292500.3330941\u003e`_\n    | :authors:`Xiao Huang, Qingquan Song, Yuening Li, Xia Hu`\n    | :venue:`KDD 2019`\n\n`Graph Convolutional Networks with EigenPooling\n\u003chttps://arxiv.org/abs/1904.13107\u003e`_\n    | :authors:`Yao Ma, Suhang Wang, Charu Aggarwal, Jiliang Tang`\n    | :venue:`KDD 2019`\n\n`DFNets: Spectral CNNs for Graphs with Feedback-Looped Filters\n\u003chttp://users.cecs.anu.edu.au/~u5170295/papers/nips-wijesinghe-2019.pdf\u003e`_\n    | :authors:`Asiri Wijesinghe, Qing Wang`\n    | :venue:`NeurIPS 2019`\n\n`Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology\n\u003chttps://arxiv.org/pdf/1907.05008.pdf\u003e`_\n    | :authors:`Nima Dehmamy, Albert-László Barabási, Rose Yu`\n    | :venue:`NeurIPS 2019`\n\n`A Flexible Generative Framework for Graph-based Semi-supervised Learning\n\u003chttps://arxiv.org/pdf/1905.10769.pdf\u003e`_\n    | :authors:`Jiaqi Ma, Weijing Tang, Ji Zhu, Qiaozhu Mei`\n    | :venue:`NeurIPS 2019`\n\n`Rethinking Kernel Methods for Node Representation Learning on Graphs\n\u003chttps://arxiv.org/pdf/1910.02548.pdf\u003e`_\n    | :authors:`Yu Tian, Long Zhao, Xi Peng, Dimitris N. Metaxas`\n    | :venue:`NeurIPS 2019`\n\n`Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks\n\u003chttps://arxiv.org/pdf/1906.02174.pdf\u003e`_\n    | :authors:`Sitao Luan, Mingde Zhao, Xiao-Wen Chang, Doina Precup`\n    | :venue:`NeurIPS 2019`\n\n`N-Gram Graph: A Simple Unsupervised Representation for Molecules\n\u003chttps://arxiv.org/pdf/1806.09206.pdf\u003e`_\n    | :authors:`Shengchao Liu, Thevaa Chandereng, Yingyu Liang`\n    | :venue:`NeurIPS 2019`\n\n`DeepGCNs: Can GCNs Go as Deep as CNNs?\n\u003chttps://arxiv.org/pdf/1904.03751.pdf\u003e`_\n    | :authors:`Guohao Li, Matthias Muller, Ali Thabet, Bernard Ghanem`\n    | :venue:`ICCV 2019`\n\n`Continuous Graph Neural Networks\n\u003chttps://arxiv.org/pdf/1912.00967.pdf\u003e`_\n    | :authors:`Louis-Pascal A. C. Xhonneux, Meng Qu, Jian Tang`\n    | :venue:`arXiv 2019`\n\n`Curvature Graph Network\n\u003chttps://openreview.net/pdf?id=BylEqnVFDB\u003e`_\n\t| :authors:`Ze Ye, Kin Sum Liu, Tengfei Ma, Jie Gao, Chao Chen`\n\t| :venue:`ICLR 2020`\n\n`Memory-based Graph Networks\n\u003chttps://openreview.net/pdf?id=r1laNeBYPB\u003e`_\n\t| :authors:`Amir hosein Khasahmadi, Kaveh Hassani, Parsa Moradi, Leo Lee, Quaid Morris`\n\t| :venue:`ICLR 2020`\n\t\n`Strategies for Pre-training Graph Neural Networks\n\u003chttps://openreview.net/pdf?id=HJlWWJSFDH\u003e`_\n\t| :authors:`Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, Jure Leskovec`\n\t| :venue:`ICLR 2020`\n\nApplications of Graph Deep Learning\n=================================\n\nNatural Language Processing\n---------------------------\n\n`Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling\n\u003chttps://www.aclweb.org/anthology/D17-1159\u003e`_\n    | :authors:`Diego Marcheggiani, Ivan Titov`\n    | :venue:`EMNLP 2017`\n\n`Graph Convolutional Encoders for Syntax-aware Neural Machine Translation\n\u003chttps://www.aclweb.org/anthology/D17-1209\u003e`_\n    | :authors:`Joost Bastings, Ivan Titov, Wilker Aziz, Diego Marcheggiani, Khalil Sima’an`\n    | :venue:`EMNLP 2017`\n\n`Graph-based Neural Multi-Document Summarization\n\u003chttps://www.aclweb.org/anthology/K17-1045\u003e`_\n    | :authors:`Michihiro Yasunaga, Rui Zhang, Kshitijh Meelu, Ayush Pareek, Krishnan Srinivasan, Dragomir Radev`\n    | :venue:`CoNLL 2017`\n\n`QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension\n\u003chttps://arxiv.org/pdf/1804.09541.pdf\u003e`_\n    | :authors:`Adams Wei Yu, David Dohan, Minh-Thang Luong, Rui Zhao, Kai Chen, Mohammad Norouzi, Quoc V. Le`\n    | :venue:`ICLR 2018`\n\n`A Structured Self-attentive Sentence Embedding\n\u003chttps://arxiv.org/pdf/1703.03130.pdf\u003e`_\n    | :authors:`Zhouhan Lin, Minwei Feng, Cicero Nogueira dos Santos, Mo Yu, Bing Xiang, Bowen Zhou, Yoshua Bengio`\n    | :venue:`ICLR 2018`\n\n`Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering\n\u003chttps://aclweb.org/anthology/C18-1280\u003e`_\n    | :authors:`Daniil Sorokin, Iryna Gurevych`\n    | :venue:`COLING 2018`\n\n`Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks\n\u003chttps://www.aclweb.org/anthology/N18-2078\u003e`_\n    | :authors:`Diego Marcheggiani, Joost Bastings, Ivan Titov`\n    | :venue:`NAACL 2018`\n\n`Linguistically-Informed Self-Attention for Semantic Role Labeling\n\u003chttps://www.aclweb.org/anthology/D18-1548\u003e`_\n    | :authors:`Emma Strubell, Patrick Verga, Daniel Andor, David Weiss, Andrew McCallum`\n    | :venue:`EMNLP 2018`\n\n`Graph Convolution over Pruned Dependency Trees Improves Relation Extraction\n\u003chttps://aclweb.org/anthology/D18-1244\u003e`_\n    | :authors:`Yuhao Zhang, Peng Qi, Christopher D. Manning`\n    | :venue:`EMNLP 2018`\n\n`A Graph-to-Sequence Model for AMR-to-Text Generation\n\u003chttps://www.aclweb.org/anthology/P18-1150\u003e`_\n    | :authors:`Linfeng Song, Yue Zhang, Zhiguo Wang, Daniel Gildea`\n    | :venue:`ACL 2018`\n\n`Graph-to-Sequence Learning using Gated Graph Neural Networks\n\u003chttps://www.aclweb.org/anthology/P18-1026\u003e`_\n    | :authors:`Daniel Beck, Gholamreza Haffari, Trevor Cohn`\n    | :venue:`ACL 2018`\n\n`Graph Convolutional Networks for Text Classification\n\u003chttps://arxiv.org/pdf/1809.05679.pdf\u003e`_\n    | :authors:`Liang Yao, Chengsheng Mao, Yuan Luo`\n    | :venue:`AAAI 2019`\n\n`Differentiable Perturb-and-Parse: Semi-Supervised Parsing with a Structured Variational Autoencoder\n\u003chttps://openreview.net/pdf?id=BJlgNh0qKQ\u003e`_\n    | :authors:`Caio Corro, Ivan Titov`\n    | :venue:`ICLR 2019`\n\n`Structured Neural Summarization\n\u003chttps://arxiv.org/pdf/1811.01824.pdf\u003e`_\n    | :authors:`Patrick Fernandes, Miltiadis Allamanis, Marc Brockschmid`\n    | :venue:`ICLR 2019`\n\n`Multi-task Learning over Graph Structures\n\u003chttps://arxiv.org/pdf/1811.10211.pdf\u003e`_\n    | :authors:`Pengfei Liu, Jie Fu, Yue Dong, Xipeng Qiu, Jackie Chi Kit Cheung`\n    | :venue:`AAAI 2019`\n\n`Imposing Label-Relational Inductive Bias for Extremely Fine-Grained Entity Typing\n\u003chttps://arxiv.org/pdf/1903.02591.pdf\u003e`_\n    | :authors:`Wenhan Xiong, Jiawei Wu, Deren Lei, Mo Yu, Shiyu Chang, Xiaoxiao Guo, William Yang Wang`\n    | :venue:`NAACL 2019`\n\n`Single Document Summarization as Tree Induction\n\u003chttps://www.aclweb.org/anthology/N19-1173\u003e`_\n    | :authors:`Yang Liu, Ivan Titov, Mirella Lapata`\n    | :venue:`NAACL 2019`\n\n`Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks\n\u003chttps://arxiv.org/pdf/1903.01306.pdf\u003e`_\n    | :authors:`Ningyu Zhang, Shumin Deng, Zhanlin Sun, Guanying Wang, Xi Chen, Wei Zhang, Huajun Chen`\n    | :venue:`NAACL 2019`\n\n`Graph Neural Networks with Generated Parameters for Relation Extraction\n\u003chttps://arxiv.org/pdf/1902.00756.pdf\u003e`_\n    | :authors:`Hao Zhu, Yankai Lin, Zhiyuan Liu, Jie Fu, Tat-seng Chua, Maosong Sun`\n    | :venue:`ACL 2019`\n\n`Dynamically Fused Graph Network for Multi-hop Reasoning\n\u003chttps://arxiv.org/pdf/1905.06933.pdf\u003e`_\n    | :authors:`Yunxuan Xiao, Yanru Qu, Lin Qiu, Hao Zhou, Lei Li, Weinan Zhang, Yong Yu`\n    | :venue:`ACL 2019`\n\n`Encoding Social Information with Graph Convolutional Networks for Political Perspective Detection\nin News Media\n\u003chttps://www.cs.purdue.edu/homes/dgoldwas//downloads/papers/LiG_acl_2019.pdf\u003e`_\n    | :authors:`Chang Li, Dan Goldwasser`\n    | :venue:`ACL 2019`\n\n`Attention Guided Graph Convolutional Networks for Relation Extraction\n\u003chttps://arxiv.org/pdf/1906.07510.pdf\u003e`_\n    | :authors:`Zhijiang Guo, Yan Zhang, Wei Lu`\n    | :venue:`ACL 2019`\n\n`Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks\n\u003chttps://arxiv.org/pdf/1809.04283.pdf\u003e`_\n    | :authors:`Shikhar Vashishth, Manik Bhandari, Prateek Yadav, Piyush Rai, Chiranjib Bhattacharyya, Partha Talukdar`\n    | :venue:`ACL 2019`\n\n`GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction\n\u003chttps://tsujuifu.github.io/pubs/acl19_graph-rel.pdf\u003e`_\n    | :authors:`Tsu-Jui Fu, Peng-Hsuan Li, Wei-Yun Ma`\n    | :venue:`ACL 2019`\n\n`Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs\n\u003chttps://arxiv.org/pdf/1905.07374.pdf\u003e`_\n    | :authors:`Ming Tu, Guangtao Wang, Jing Huang, Yun Tang, Xiaodong He, Bowen Zhou`\n    | :venue:`ACL 2019`\n\n`Cognitive Graph for Multi-Hop Reading Comprehension at Scale\n\u003chttps://arxiv.org/pdf/1905.05460.pdf\u003e`_\n    | :authors:`Ming Ding, Chang Zhou, Qibin Chen, Hongxia Yang, Jie Tang`\n    | :venue:`ACL 2019`\n\n`Coherent Comment Generation for Chinese Articles with a Graph-to-Sequence Model\n\u003chttps://arxiv.org/pdf/1906.01231.pdf\u003e`_\n    | :authors:`Wei Li, Jingjing Xu, Yancheng He, Shengli Yan, Yunfang Wu, Xu Sun`\n    | :venue:`ACL 2019`\n\n`Matching Article Pairs with Graphical Decomposition and Convolutions\n\u003chttps://arxiv.org/pdf/1802.07459.pdf\u003e`_\n    | :authors:`Bang Liu, Di Niu, Haojie Wei, Jinghong Lin, Yancheng He, Kunfeng Lai, Yu Xu`\n    | :venue:`ACL 2019`\n\n`Embedding Imputation with Grounded Language Information\n\u003chttps://arxiv.org/pdf/1906.03753.pdf\u003e`_\n    | :authors:`Ziyi Yang, Chenguang Zhu, Vin Sachidananda, Eric Darve`\n    | :venue:`ACL 2019`\n\n`Encoding Social Information with Graph Convolutional Networks forPolitical Perspective Detection in News Media\n\u003chttps://www.aclweb.org/anthology/P19-1247.pdf\u003e`_\n    | :authors:`Chang Li, Dan Goldwasser`\n    | :venue:`ACL 2019`\n\n`A Neural Multi-digraph Model for Chinese NER with Gazetteers\n\u003chttps://www.aclweb.org/anthology/P19-1141.pdf\u003e`_\n    | :authors:`Ruixue Ding, Pengjun Xie, Xiaoyan Zhang, Wei Lu, Linlin Li, Luo Si`\n    | :venue:`ACL 2019`\n\n`Tree Communication Models for Sentiment Analysis\n\u003chttps://www.aclweb.org/anthology/P19-1342.pdf\u003e`_\n    | :authors:`Yuan Zhang, Yue Zhang`\n    | :venue:`ACL 2019`\n\n`A2N: Attending to Neighbors for Knowledge Graph Inference\n\u003chttps://www.aclweb.org/anthology/P19-1431.pdf\u003e`_\n    | :authors:`Trapit Bansal, Da-Cheng Juan, Sujith Ravi, Andrew McCallum`\n    | :venue:`ACL 2019`\n\n`Textbook Question Answering with Multi-modal Context Graph Understanding and Self-supervised Open-set Comprehension\n\u003chttps://www.aclweb.org/anthology/P19-1347.pdf\u003e`_\n    | :authors:`Daesik Kim, Seonhoon Kim, Nojun Kwak`\n    | :venue:`ACL 2019`\n\n`Look Again at the Syntax: Relational Graph Convolutional Network for Gendered Ambiguous Pronoun Resolution\n\u003chttps://arxiv.org/pdf/1905.08868.pdf\u003e`_\n    | :authors:`Yinchuan Xu, Junlin Yang`\n    | :venue:`ACL 2019 Workshop`\n    | :keywords:`https://github.com/ianycxu/RGCN-with-BERT`\n\n`Learning Graph Pooling and Hybrid Convolutional Operations for Text Representations\n\u003chttps://arxiv.org/pdf/1901.06965.pdf\u003e`_\n    | :authors:`Hongyang Gao, Yongjun Chen, Shuiwang Ji`\n    | :venue:`WWW 2019`\n\n`Learning to Create Sentence Semantic Relation Graphs for Multi-Document Summarization\n\u003chttps://arxiv.org/pdf/1909.12231.pdf\u003e`_\n    | :authors:`Diego Antognini, Boi Faltings`\n    | :venue:`EMNLP 2019`\n\n`Dependency-Guided LSTM-CRF for Named Entity Recognition\n\u003chttps://arxiv.org/pdf/1909.10148.pdf\u003e`_\n    | :authors:`Zhanming Jie, Wei Lu`\n    | :venue:`EMNLP 2019`\n\n`Modeling Conversation Structure and Temporal Dynamics for Jointly Predicting Rumor Stance and Veracity\n\u003chttps://arxiv.org/pdf/1909.08211.pdf\u003e`_\n    | :authors:`Penghui Wei, Nan Xu, Wenji Mao`\n    | :venue:`EMNLP 2019`\n\n`DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation\n\u003chttps://arxiv.org/pdf/1908.11540.pdf\u003e`_\n    | :authors:`Deepanway Ghosal, Navonil Majumder, Soujanya Poria, Niyati Chhaya, Alexander Gelbukh`\n    | :venue:`EMNLP 2019`\n\n`Modeling Graph Structure in Transformer for Better AMR-to-Text Generation\n\u003chttps://arxiv.org/pdf/1909.00136.pdf\u003e`_\n    | :authors:`Jie Zhu, Junhui Li, Muhua Zhu, Longhua Qian, Min Zhang, Guodong Zhou`\n    | :venue:`EMNLP 2019`\n\n`KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning\n\u003chttps://arxiv.org/pdf/1909.02151.pdf\u003e`_\n    | :authors:`Bill Yuchen Lin, Xinyue Chen, Jamin Chen, Xiang Ren`\n    | :venue:`EMNLP 2019`\n\nComputer Vision\n---------------\n\n`3D Graph Neural Networks for RGBD Semantic Segmentation\n\u003chttp://www.cs.toronto.edu/~rjliao/papers/iccv_2017_3DGNN.pdf\u003e`_\n    | :authors:`Xiaojuan Qi, Renjie Liao, Jiaya Jia, Sanja Fidler, Raquel Urtasun`\n    | :venue:`ICCV 2017`\n\n`Situation Recognition With Graph Neural Networks\n\u003chttps://arxiv.org/abs/1708.04320\u003e`_\n    | :authors:`Ruiyu Li, Makarand Tapaswi, Renjie Liao, Jiaya Jia, Raquel Urtasun, Sanja Fidler`\n    | :venue:`ICCV 2017`\n\n`Graph-Based Classification of Omnidirectional Images\n\u003chttps://arxiv.org/abs/1707.08301\u003e`_\n    | :authors:`Renata Khasanova, Pascal Frossard`\n    | :venue:`ICCV 2017`\n\n`Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition\n\u003chttps://arxiv.org/abs/1801.07455\u003e`_\n    | :authors:`Sijie Yan, Yuanjun Xiong, Dahua Lin`\n    | :venue:`AAAI 2018`\n\n`Image Generation from Scene Graphs\n\u003chttps://arxiv.org/abs/1804.01622\u003e`_\n    | :authors:`Justin Johnson, Agrim Gupta, Li Fei-Fei`\n    | :venue:`CVPR 2018`\n\n`FoldingNet: Point Cloud Auto-Encoder via Deep Grid Deformation\n\u003chttps://arxiv.org/abs/1712.07262\u003e`_\n    | :authors:`Yaoqing Yang, Chen Feng, Yiru Shen, Dong Tian`\n    | :venue:`CVPR 2018`\n\n`PPFNet: Global Context Aware Local Features for Robust 3D Point Matching\n\u003chttps://arxiv.org/abs/1802.02669\u003e`_\n    | :authors:`Haowen Deng, Tolga Birdal, Slobodan Ilic`\n    | :venue:`CVPR 2018`\n\n`Iterative Visual Reasoning Beyond Convolutions\n\u003chttps://arxiv.org/abs/1803.11189\u003e`_\n    | :authors:`Xinlei Chen, Li-Jia Li, Li Fei-Fei, Abhinav Gupta`\n    | :venue:`CVPR 2018`\n\n`Surface Networks\n\u003chttps://arxiv.org/abs/1705.10819\u003e`_\n    | :authors:`Ilya Kostrikov, Zhongshi Jiang, Daniele Panozzo, Denis Zorin, Joan Bruna`\n    | :venue:`CVPR 2018`\n\n`FeaStNet: Feature-Steered Graph Convolutions for 3D Shape Analysis\n\u003chttps://arxiv.org/abs/1706.05206\u003e`_\n    | :authors:`Nitika Verma, Edmond Boyer, Jakob Verbeek`\n    | :venue:`CVPR 2018`\n\n`Learning to Act Properly: Predicting and Explaining Affordances From Images\n\u003chttps://arxiv.org/abs/1712.07576\u003e`_\n    | :authors:`Ching-Yao Chuang, Jiaman Li, Antonio Torralba, Sanja Fidler`\n    | :venue:`CVPR 2018`\n\n`Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling\n\u003chttps://arxiv.org/abs/1712.06760\u003e`_\n    | :authors:`Yiru Shen, Chen Feng, Yaoqing Yang, Dong Tian`\n    | :venue:`CVPR 2018`\n\n`Deformable Shape Completion With Graph Convolutional Autoencoders\n\u003chttps://arxiv.org/abs/1712.00268\u003e`_\n    | :authors:`Or Litany, Alex Bronstein, Michael Bronstein, Ameesh Makadia`\n    | :venue:`CVPR 2018`\n\n`Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images\n\u003chttps://arxiv.org/abs/1804.01654\u003e`_\n    | :authors:`Nanyang Wang, Yinda Zhang, Zhuwen Li, Yanwei Fu, Wei Liu, Yu-Gang Jiang`\n    | :venue:`ECCV 2018`\n\n`Learning Human-Object Interactions by Graph Parsing Neural Networks\n\u003chttps://arxiv.org/abs/1808.07962\u003e`_\n    | :authors:`Siyuan Qi, Wenguan Wang, Baoxiong Jia, Jianbing Shen, Song-Chun Zhu`\n    | :venue:`ECCV 2018`\n\n`Generating 3D Faces using Convolutional Mesh Autoencoders\n\u003chttps://arxiv.org/abs/1807.10267\u003e`_\n    | :authors:`Anurag Ranjan, Timo Bolkart, Soubhik Sanyal, Michael J. Black`\n    | :venue:`ECCV 2018`\n\n`Learning SO(3) Equivariant Representations with Spherical CNNs\n\u003chttps://arxiv.org/abs/1711.06721\u003e`_\n    | :authors:`Carlos Esteves, Christine Allen-Blanchette, Ameesh Makadia, Kostas Daniilidis`\n    | :venue:`ECCV 2018`\n\n`Neural Graph Matching Networks for Fewshot 3D Action Recognition\n\u003chttp://openaccess.thecvf.com/content_ECCV_2018/papers/Michelle_Guo_Neural_Graph_Matching_ECCV_2018_paper.pdf\u003e`_\n    | :authors:`Michelle Guo, Edward Chou, De-An Huang, Shuran Song, Serena Yeung, Li Fei-Fei`\n    | :venue:`ECCV 2018`\n\n`Multi-Kernel Diffusion CNNs for Graph-Based Learning on Point Clouds\n\u003chttps://arxiv.org/abs/1809.05370\u003e`_\n    | :authors:`Lasse Hansen, Jasper Diesel, Mattias P. Heinrich`\n    | :venue:`ECCV 2018`\n\n`Hierarchical Video Frame Sequence Representation with Deep Convolutional Graph Network\n\u003chttps://arxiv.org/abs/1906.00377\u003e`_\n    | :authors:`Feng Mao, Xiang Wu, Hui Xue, Rong Zhang`\n    | :venue:`ECCV 2018`\n\n`Graph R-CNN for Scene Graph Generation\n\u003chttps://arxiv.org/abs/1808.00191\u003e`_\n    | :authors:`Jianwei Yang, Jiasen Lu, Stefan Lee, Dhruv Batra, Devi Parikh`\n    | :venue:`ECCV 2018`\n\n`Exploring Visual Relationship for Image Captioning\n\u003chttps://arxiv.org/abs/1809.07041\u003e`_\n    | :authors:`Ting Yao, Yingwei Pan, Yehao Li, Tao Mei`\n    | :venue:`ECCV 2018`\n\n`Beyond Grids: Learning Graph Representations for Visual Recognition\n\u003chttps://papers.nips.cc/paper/8135-beyond-grids-learning-graph-representations-for-visual-recognition\u003e`_\n    | :authors:`Yin Li, Abhinav Gupta`\n    | :venue:`NeurIPS 2018`\n\n`Learning Conditioned Graph Structures for Interpretable Visual Question Answering\n\u003chttps://arxiv.org/abs/1806.07243\u003e`_\n    | :authors:`Will Norcliffe-Brown, Efstathios Vafeias, Sarah Parisot`\n    | :venue:`NeurIPS 2018`\n\n`LinkNet: Relational Embedding for Scene Graph\n\u003chttps://arxiv.org/abs/1811.06410\u003e`_\n    | :authors:`Sanghyun Woo, Dahun Kim, Donghyeon Cho, In So Kweon`\n    | :venue:`NeurIPS 2018`\n\n`Flexible Neural Representation for Physics Prediction\n\u003chttps://arxiv.org/abs/1806.08047\u003e`_\n    | :authors:`Damian Mrowca, Chengxu Zhuang, Elias Wang, Nick Haber, Li Fei-Fei, Joshua B. Tenenbaum, Daniel L. K. Yamins`\n    | :venue:`NeurIPS 2018`\n\n`Learning Localized Generative Models for 3D Point Clouds via Graph Convolution\n\u003chttps://openreview.net/forum?id=SJeXSo09FQ\u003e`_\n    | :authors:`Diego Valsesia, Giulia Fracastoro, Enrico Magli`\n    | :venue:`ICLR 2019`\n\n`Graph-Based Global Reasoning Networks\n\u003chttps://arxiv.org/abs/1811.12814\u003e`_\n    | :authors:`Yunpeng Chen, Marcus Rohrbach, Zhicheng Yan, Shuicheng Yan, Jiashi Feng, Yannis Kalantidis`\n    | :venue:`CVPR 2019`\n\n`Deep Graph Laplacian Regularization for Robust Denoising of Real Images\n\u003chttps://arxiv.org/abs/1807.11637\u003e`_\n    | :authors:`Jin Zeng, Jiahao Pang, Wenxiu Sun, Gene Cheung`\n    | :venue:`CVPR 2019`\n\n`Learning Context Graph for Person Search\n\u003chttps://arxiv.org/abs/1904.01830\u003e`_\n    | :authors:`Yichao Yan, Qiang Zhang, Bingbing Ni, Wendong Zhang, Minghao Xu, Xiaokang Yang`\n    | :venue:`CVPR 2019`\n\n`Graphonomy: Universal Human Parsing via Graph Transfer Learning\n\u003chttps://arxiv.org/abs/1904.04536\u003e`_\n    | :authors:`Ke Gong, Yiming Gao, Xiaodan Liang, Xiaohui Shen, Meng Wang, Liang Lin`\n    | :venue:`CVPR 2019`\n\n`Masked Graph Attention Network for Person Re-Identification\n\u003chttp://openaccess.thecvf.com/content_CVPRW_2019/papers/TRMTMCT/Bao_Masked_Graph_Attention_Network_for_Person_Re-Identification_CVPRW_2019_paper.pdf\u003e`_\nfor_Person_Re-Identification_CVPRW_2019_paper.html\u003e`_\n    | :authors:`Liqiang Bao, Bingpeng Ma, Hong Chang, Xilin Chen`\n    | :venue:`CVPR 2019`\n\n`Learning to Cluster Faces on an Affinity Graph\n\u003chttps://arxiv.org/abs/1904.02749\u003e`_\n    | :authors:`Lei Yang, Xiaohang Zhan, Dapeng Chen, Junjie Yan, Chen Change Loy, Dahua Lin`\n    | :venue:`CVPR 2019`\n\n`Actional-Structural Graph Convolutional Networks for Skeleton-Based Action Recognition\n\u003chttps://arxiv.org/abs/1904.12659\u003e`_\n    | :authors:`Maosen Li, Siheng Chen, Xu Chen, Ya Zhang, Yanfeng Wang, Qi Tian`\n    | :venue:`CVPR 2019`\n\n`Adaptively Connected Neural Networks\n\u003chttps://arxiv.org/abs/1904.03579\u003e`_\n    | :authors:`Guangrun Wang, Keze Wang, Liang Lin`\n    | :venue:`CVPR 2019`\n\n`Reasoning Visual Dialogs with Structural and Partial Observations\n\u003chttps://arxiv.org/abs/1904.03579\u003e`_\n    | :authors:`Zilong Zheng, Wenguan Wang, Siyuan Qi, Song-Chun Zhu`\n    | :venue:`CVPR 2019`\n\n`MeshCNN: A Network with an Edge\n\u003chttps://arxiv.org/pdf/1809.05910.pdf\u003e`_\n    | :authors:`Rana Hanocka, Amir Hertz, Noa Fish, Raja Giryes, Shachar Fleishman, Daniel Cohen-Or`\n    | :venue:`SIGGRAPH 2019`\n    | :keywords:`https://ranahanocka.github.io/MeshCNN/`\n\n`Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learning\n\u003chttps://arxiv.org/pdf/1908.02441.pdf\u003e`_\n    | :authors:`Jiwoong Park, Minsik Lee, Hyung Jin Chang, Kyuewang Lee, Jin Young Choi`\n    | :venue:`ICCV 2019`\n\n`Pixel2Mesh++: Multi-View 3D Mesh Generation via Deformation\n\u003chttps://arxiv.org/pdf/1908.01491.pdf\u003e`_\n    | :authors:`Chao Wen, Yinda Zhang, Zhuwen Li, Yanwei Fu`\n    | :venue:`ICCV 2019`\n\n`Learning Trajectory Dependencies for Human Motion Prediction\n\u003chttps://arxiv.org/pdf/1908.05436.pdf\u003e`_\n    | :authors:`Wei Mao, Miaomiao Liu, Mathieu Salzmann, Hongdong Li`\n    | :venue:`ICCV 2019`\n\n`Graph-Based Object Classification for Neuromorphic Vision Sensing\n\u003chttps://arxiv.org/pdf/1908.06648.pdf\u003e`_\n    | :authors:`Yin Bi, Aaron Chadha, Alhabib Abbas, Eirina Bourtsoulatze, Yiannis Andreopoulos`\n    | :venue:`ICCV 2019`\n\n`Fashion Retrieval via Graph Reasoning Networks on a Similarity Pyramid\n\u003chttps://arxiv.org/pdf/1908.11754.pdf\u003e`_\n    | :authors:`Zhanghui Kuang, Yiming Gao, Guanbin Li, Ping Luo, Yimin Chen, Liang Lin, Wayne Zhang`\n    | :venue:`ICCV 2019`\n\n`Understanding Human Gaze Communication by Spatio-Temporal Graph Reasoning\n\u003chttps://arxiv.org/pdf/1909.02144.pdf\u003e`_\n    | :authors:`Lifeng Fan, Wenguan Wang, Siyuan Huang, Xinyu Tang, Song-Chun Zhu`\n    | :venue:`ICCV 2019`\n\n`Visual Semantic Reasoning for Image-Text Matching\n\u003chttps://arxiv.org/pdf/1909.02701.pdf\u003e`_\n    | :authors:`Kunpeng Li, Yulun Zhang, Kai Li, Yuanyuan Li, Yun Fu`\n    | :venue:`ICCV 2019`\n\n`Graph Convolutional Networks for Temporal Action Localization\n\u003chttps://arxiv.org/pdf/1909.03252.pdf\u003e`_\n    | :authors:`Runhao Zeng, Wenbing Huang, Mingkui Tan, Yu Rong, Peilin Zhao, Junzhou Huang, Chuang Gan`\n    | :venue:`ICCV 2019`\n\n`Semantically-Regularized Logic Graph Embeddings\n\u003chttps://arxiv.org/pdf/1909.01161.pdf\u003e`_\n    | :authors:`Yaqi Xie, Ziwei Xu, Kuldeep Meel, Mohan S Kankanhalli, Harold Soh`\n    | :venue:`NeurIPS 2019`\n\nRecommender Systems\n-------------------\n\n`Graph Convolutional Neural Networks for Web-Scale Recommender Systems\n\u003chttps://arxiv.org/pdf/1806.01973.pdf\u003e`_\n    | :authors:`Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, Jure Leskovec`\n    | :venue:`KDD 2018`\n    | :keywords:`PinSage`\n\n`SocialGCN: An Efficient Graph Convolutional Network based Model for Social Recommendation\n\u003chttps://arxiv.org/pdf/1811.02815.pdf\u003e`_\n    | :authors:`Le Wu, Peijie Sun, Richang Hong, Yanjie Fu, Xiting Wang, Meng Wang`\n    | :venue:`AAAI 2018`\n    | :keywords:`GCN, Social recommendation`\n\n`Session-based Social Recommendation via Dynamic Graph Attention Networks\n\u003chttps://arxiv.org/pdf/1902.09362.pdf\u003e`_\n    | :authors:`Weiping Song, Zhiping Xiao, Yifan Wang, Laurent Charlin, Ming Zhang, Jian Tang`\n    | :venue:`WSDM 2019`\n    | :keywords:`Social recommendation, session-based, GAT`\n\n`Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in\nRecommender Systems\n\u003chttps://arxiv.org/pdf/1903.10433.pdf\u003e`_\n    | :authors:`Qitian Wu, Hengrui Zhang, Xiaofeng Gao, Peng He, Paul Weng, Han Gao, Guihai Chen`\n    | :venue:`WWW 2019`\n    | :keywords:`Social recommendation, GAT`\n\n`Graph Neural Networks for Social Recommendation\n\u003chttps://arxiv.org/pdf/1902.07243.pdf\u003e`_\n    | :authors:`Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, Dawei Yin`\n    | :venue:`WWW 2019`\n    | :keywords:`Social recommendation, GNN`\n\n`Session-based Recommendation with Graph Neural Networks\n\u003chttps://arxiv.org/pdf/1811.00855.pdf\u003e`_\n    | :authors:`Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, Tieniu Tan`\n    | :venue:`AAAI 2019`\n    | :keywords:`Session-based recommendation, GNN`\n\n`A Neural Influence Diffusion Model for Social Recommendation\n\u003chttps://arxiv.org/pdf/1904.10322.pdf\u003e`_\n    | :authors:`Le Wu, Peijie Sun, Yanjie Fu, Richang Hong, Xiting Wang, Meng Wang`\n    | :venue:`SIGIR 2019`\n    | :keywords:`Social Recommendation, diffusion`\n\n`Neural Graph Collaborative Filtering\n\u003chttps://arxiv.org/pdf/1905.08108.pdf\u003e`_\n    | :authors:`Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, Tat-Seng Chua`\n    | :venue:`SIGIR 2019`\n    | :keywords:`Collaborative Filtering, GNN`\n\n`Binarized Collaborative Filtering with Distilling Graph Convolutional Networks\n\u003chttps://arxiv.org/pdf/1906.01829.pdf\u003e`_\n    | :authors:`Haoyu Wang, Defu Lian, Yong Ge`\n    | :venue:`IJCAI 2019`\n\n`IntentGC: A Scalable Graph Convolution Framework Fusing Heterogeneous Information for Recommendation\n\u003chttps://dl.acm.org/citation.cfm?id=3330686\u003e`_\n    | :authors:`Jun Zhao, Zhou Zhou, Ziyu Guan, Wei Zhao, Wei Ning, Guang Qiu, Xiaofei He`\n    | :venue:`KDD 2019`\n\n`An End-to-End Neighborhood-based Interaction Model for Knowledge-enhanced Recommendation\n\u003chttps://arxiv.org/pdf/1908.04032.pdf\u003e`_\n    | :authors:`Yanru Qu, Ting Bai, Weinan Zhang, Jianyun Nie, Jian Tang`\n    | :venue:`KDD 2019 Workshop`\n\nLink Prediction\n---------------\n\n`Link Prediction Based on Graph Neural Networks\n\u003chttps://papers.nips.cc/paper/7763-link-prediction-based-on-graph-neural-networks.pdf\u003e`_\n    | :authors:`Muhan Zhang, Yixin Chen`\n    | :venue:`NeurIPS 2018`\n\n`Link Prediction via Subgraph Embedding-Based Convex Matrix Completion\n\u003chttp://iiis.tsinghua.edu.cn/~weblt/papers/link-prediction-subgraphembeddings.pdf\u003e`_\n    | :authors:`Zhu Cao, Linlin Wang, Gerard de Melo`\n    | :venue:`AAAI 2018`\n\n`Graph Convolutional Matrix Completion\n\u003chttps://www.kdd.org/kdd2018/files/deep-learning-day/DLDay18_paper_32.pdf\u003e`_\n    | :authors:`Rianne van den Berg, Thomas N. Kipf, Max Welling`\n    | :venue:`KDD 2018 Workshop`\n\n`Semi-Implicit Graph Variational Auto-Encoders\n\u003chttps://arxiv.org/pdf/1908.07078.pdf\u003e`_\n    | :authors:`Arman Hasanzadeh, Ehsan Hajiramezanali, Nick Duffield , Krishna Narayanan, Mingyuan Zhou, Xiaoning Qian`\n    | :venue:`NeurIPS 2019`\n\nInfluence Prediction\n--------------------\n\n`DeepInf: Social Influence Prediction with Deep Learning\n\u003chttps://arxiv.org/pdf/1807.05560.pdf\u003e`_\n    | :authors:`Jiezhong Qiu, Jian Tang, Hao Ma, Yuxiao Dong, Kuansan Wang, Jie Tang`\n    | :venue:`KDD 2018`\n\n`Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks\n\u003chttps://arxiv.org/pdf/1905.08865.pdf\u003e`_\n    | :authors:`Namyong Park, Andrey Kan, Xin Luna Dong, Tong Zhao, Christos Faloutsos`\n    | :venue:`KDD 2019`\n\nNeural Architecture Search\n--------------------------\n\n`Graph HyperNetworks for Neural Architecture Search\n\u003chttps://openreview.net/pdf?id=rkgW0oA9FX\u003e`_\n    | :authors:`Chris Zhang, Mengye Ren, Raquel Urtasun`\n    | :venue:`ICLR 2019`\n\n`D-VAE: A Variational Autoencoder for Directed Acyclic Graphs\n\u003chttps://arxiv.org/pdf/1904.11088.pdf\u003e`_\n    | :authors:`Muhan Zhang, Shali Jiang, Zhicheng Cui, Roman Garnett, Yixin Chen`\n    | :venue:`NeurIPS 2019`\n\nReinforcement Learning\n----------------------\n\n`Action Schema Networks: Generalised Policies with Deep Learning\n\u003chttps://arxiv.org/pdf/1709.04271.pdf\u003e`_\n    | :authors:`Sam Toyer, Felipe Trevizan, Sylvie Thiebaux, Lexing Xie`\n    | :venue:`AAAI 2018`\n\n`NerveNet: Learning Structured Policy with Graph Neural Networks\n\u003chttps://openreview.net/pdf?id=S1sqHMZCb\u003e`_\n    | :authors:`Tingwu Wang, Renjie Liao, Jimmy Ba, Sanja Fidler`\n    | :venue:`ICLR 2018`\n\n`Graph Networks as Learnable Physics Engines for Inference and Control\n\u003chttps://arxiv.org/pdf/1806.01242.pdf\u003e`_\n    | :authors:`Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg, Josh Merel, Martin Riedmiller`\n    | :venue:`ICML 2018`\n\n`Learning Policy Representations in Multiagent Systems\n\u003chttps://arxiv.org/pdf/1806.06464.pdf\u003e`_\n    | :authors:`Aditya Grover, Maruan Al-Shedivat, Jayesh K. Gupta, Yura Burda, Harrison Edwards`\n    | :venue:`ICML 2018`\n\n`Relational recurrent neural networks\n\u003chttps://papers.nips.cc/paper/7960-relational-recurrent-neural-networks.pdf\u003e`_\n    | :authors:`Adam Santoro,  Ryan Faulkner, David Raposo, Jack Rae, Mike Chrzanowski,Théophane Weber, Daan Wierstra, Oriol Vinyals, Razvan Pascanu, Timothy Lillicrap`\n    | :venue:`NeurIPS 2018`\n\n`Transfer of Deep Reactive Policies for MDP Planning\n\u003chttp://www.cse.iitd.ac.in/~mausam/papers/nips18.pdf\u003e`_\n    | :authors:`Aniket Bajpai, Sankalp Garg, Mausam`\n    | :venue:`NeurIPS 2018`\n\n`Neural Graph Evolution: Towards Efficient Automatic Robot Design\n\u003chttps://openreview.net/pdf?id=BkgWHnR5tm\u003e`_\n    | :authors:`Tingwu Wang, Yuhao Zhou, Sanja Fidler, Jimmy Ba`\n    | :venue:`ICLR 2019`\n\n`No Press Diplomacy: Modeling Multi-Agent Gameplay\n\u003chttps://arxiv.org/pdf/1909.02128.pdf\u003e`_\n    | :authors:`Philip Paquette, Yuchen Lu, Steven Bocco, Max O. Smith, Satya Ortiz-Gagne, Jonathan K. Kummerfeld, Satinder Singh, Joelle Pineau, Aaron Courville`\n    | :venue:`NeurIPS 2019`\n\nCombinatorial Optimization\n--------------------------\n\n`Learning Combinatorial Optimization Algorithms over Graphs\n\u003chttps://arxiv.org/abs/1704.01665\u003e`_\n    | :authors:`Hanjun Dai, Elias B. Khalil, Yuyu Zhang, Bistra Dilkina, Le Song`\n    | :venue:`NeurIPS 2017`\n\n`Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search\n\u003chttps://arxiv.org/abs/1810.10659\u003e`_\n    | :authors:`Zhuwen Li, Qifeng Chen, Vladlen Koltun`\n    | :venue:`NeurIPS 2018`\n\n`Reinforcement Learning for Solving the Vehicle Routing Problem\n\u003chttps://arxiv.org/abs/1802.04240\u003e`_\n    | :authors:`Mohammadreza Nazari, Afshin Oroojlooy, Lawrence V. Snyder, Martin Takáč`\n    | :venue:`NeurIPS 2018`\n    \n`Attention, Learn to Solve Routing Problems!\n\u003chttps://arxiv.org/abs/1803.08475\u003e`_\n    | :authors:`Wouter Kool, Herke van Hoof, Max Welling`\n    | :venue:`ICLR 2019`\n    \n`Learning a SAT Solver from Single-Bit Supervision\n\u003chttps://arxiv.org/abs/1802.03685\u003e`_\n    | :authors:`Daniel Selsam, Matthew Lamm, Benedikt Bünz, Percy Liang, Leonardo de Moura, David L. Dill`\n    | :venue:`ICLR 2019`\n    \n`An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem\n\u003chttps://arxiv.org/abs/1906.01227\u003e`_\n    | :authors:`Chaitanya K. Joshi, Thomas Laurent, Xavier Bresson`\n    | :venue:`arXiv 2019`\n\n`Approximation Ratios of Graph Neural Networks for Combinatorial Problems\n\u003chttps://arxiv.org/pdf/1905.10261.pdf\u003e`_\n    | :authors:`Ryoma Sato, Makoto Yamada, Hisashi Kashima`\n    | :venue:`NeurIPS 2019`\n\n`Exact Combinatorial Optimization with Graph Convolutional Neural Networks\n\u003chttps://arxiv.org/pdf/1906.01629.pdf\u003e`_\n    | :authors:`Maxime Gasse, Didier Chételat, Nicola Ferroni, Laurent Charlin, Andrea Lodi`\n    | :venue:`NeurIPS 2019`\n    \n`On Learning Paradigms for the Travelling Salesman Problem\n\u003chttps://arxiv.org/pdf/1910.07210.pdf\u003e`_\n    | :authors:`Chaitanya K. Joshi, Thomas Laurent, Xavier Bresson`\n    | :venue:`NeurIPS 2019 Workshop`\n\nAdversarial Attack and Robustness\n------------------\n\n`Adversarial Attack on Graph Structured Data\n\u003chttps://arxiv.org/abs/1806.02371\u003e`_\n    | :authors:`Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, Le Song`\n    | :venue:`ICML 2018`\n\n`Adversarial Attacks on Neural Networks for Graph Data\n\u003chttps://arxiv.org/abs/1805.07984\u003e`_\n    | :authors:`Daniel Zügner, Amir Akbarnejad, Stephan Günnemann`\n    | :venue:`KDD 2018`\n\n`Adversarial Attacks on Graph Neural Networks via Meta Learning\n\u003chttps://arxiv.org/abs/1902.08412\u003e`_\n    | :authors:`Daniel Zügner, Stephan Günnemann`\n    | :venue:`ICLR 2019`\n\n`Robust Graph Convolutional Networks Against Adversarial Attacks\n\u003chttp://pengcui.thumedialab.com/papers/RGCN.pdf\u003e`_\n    | :authors:`Dingyuan Zhu, Ziwei Zhang, Peng Cui, Wenwu Zhu`\n    | :venue:`KDD 2019`\n\n`Certifiable Robustness and Robust Training for Graph Convolutional Networks\n\u003chttps://arxiv.org/pdf/1906.12269.pdf\u003e`_\n    | :authors:`Daniel Zügner, Stephan Günnemann`\n    | :venue:`KDD 2019`\n\nGraph Matching\n-------------\n\n`REGAL: Representation Learning-based Graph Alignment\n\u003chttps://arxiv.org/pdf/1802.06257.pdf\u003e`_\n\t| :authors:`Mark Heimann, Haoming Shen, Tara Safavi, Danai Koutra`\n\t| :venue:`CIKM 2018`\n\n`Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks\n\u003chttps://www.aclweb.org/anthology/D18-1032.pdf\u003e`_\n\t| :authors:`Zhichun Wang, Qingsong Lv, Xiaohan Lan, Yu Zhang`\n\t| :venue:`EMNLP 2018`\n\n`Learning Combinatorial Embedding Networks for Deep Graph Matching\n\u003chttp://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Learning_Combinatorial_Embedding_Networks_for_Deep_Graph_Matching_ICCV_2019_paper.pdf\u003e`_\n\t| :authors:`Runzhong Wang, Junchi Yan, Xiaokang Yang`\n\t| :venue:`ICCV 2019`\n\n`Deep Graph Matching Consensus\n\u003chttps://openreview.net/pdf?id=HyeJf1HKvS\u003e`_\n\t| :authors:`Matthias Fey, Jan E. Lenssen, Christopher Morris, Jonathan Masci, Nils M. Kriege`\n\t| :venue:`ICLR 2020`\n\t\nMeta Learning and Few-shot Learning\n---------------------------------\n\n`Few-Shot Learning with Graph Neural Networks\n\u003chttps://arxiv.org/abs/1711.04043\u003e`_\n    | :authors:`Victor Garcia, Joan Bruna`\n    | :venue:`ICLR 2018`\n\n`Learning Steady-States of Iterative Algorithms over Graphs\n\u003chttp://proceedings.mlr.press/v80/dai18a.html\u003e`_\n    | :authors:`Hanjun Dai, Zornitsa Kozareva, Bo Dai, Alex Smola, Le Song`\n    | :venue:`ICML 2018`\n\n`Learning to Propagate for Graph Meta-Learning\n\u003chttps://arxiv.org/pdf/1909.05024.pdf\u003e`_\n    | :authors:`Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang`\n    | :venue:`NeurIPS 2019`\n\n`Few-Shot Learning on Graphs via Super-Classes based on Graph Spectral Measures\n\u003chttps://openreview.net/forum?id=Bkeeca4Kvr\u003e`_\n\t| :authors:`Jatin Chauhan, Deepak Nathani, Manohar Kaul`\n\t| :venue:`ICLR 2020`\n\n`Automated Relational Meta-learning\n\u003chttps://openreview.net/pdf?id=rklp93EtwH\u003e`_\n\t| :authors:`Huaxiu Yao, Xian Wu, Zhiqiang Tao, Yaliang Li, Bolin Ding, Ruirui Li, Zhenhui Li`\n\t| :venue:`ICLR 2020`\n\nStructure Learning\n------------------\n\n`Neural Relational Inference for Interacting Systems\n\u003chttps://arxiv.org/abs/1802.04687\u003e`_\n    | :authors:`Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, Richard Zemel`\n    | :venue:`ICML 2018`\n\n`Brain Signal Classification via Learning Connectivity Structure\n\u003chttps://arxiv.org/abs/1905.11678\u003e`_\n    | :authors:`Soobeom Jang, Seong-Eun Moon, Jong-Seok Lee`\n    | :venue:`arXiv 2019`\n\n`A Flexible Generative Framework for Graph-based Semi-supervised Learning\n\u003chttps://arxiv.org/abs/1905.10769\u003e`_\n    | :authors:`Jiaqi Ma, Weijing Tang, Ji Zhu, Qiaozhu Mei`\n    | :venue:`NeurIPS 2019`\n\n`Joint embedding of structure and features via graph convolutional networks\n\u003chttps://arxiv.org/abs/1905.08636\u003e`_\n    | :authors:`Sébastien Lerique, Jacob Levy Abitbol, Márton Karsai`\n    | :venue:`arXiv 2019`\n\n`Variational Spectral Graph Convolutional Networks\n\u003chttps://arxiv.org/abs/1906.01852\u003e`_\n    | :authors:`Louis Tiao, Pantelis Elinas, Harrison Nguyen, Edwin V. Bonilla`\n    | :venue:`arXiv 2019`\n\n`Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning\n\u003chttps://arxiv.org/abs/1805.10002\u003e`_\n    | :authors:`Yanbin Liu, Juho Lee, Minseop Park, Saehoon Kim, Eunho Yang, Sung Ju Hwang, Yi Yang`\n    | :venue:`ICLR 2019`\n\n`Graph Learning Network: A Structure Learning Algorithm\n\u003chttps://arxiv.org/abs/1905.12665\u003e`_\n    | :authors:`Darwin Saire Pilco, Adín Ramírez Rivera`\n    | :venue:`ICML 2019 Workshop`\n\n`Learning Discrete Structures for Graph Neural Networks\n\u003chttps://arxiv.org/abs/1903.11960\u003e`_\n    | :authors:`Luca Franceschi, Mathias Niepert, Massimiliano Pontil, Xiao He`\n    | :venue:`ICML 2019`\n\n`Graphite: Iterative Generative Modeling of Graphs\n\u003chttps://arxiv.org/abs/1803.10459\u003e`_\n    | :authors:`Aditya Grover, Aaron Zweig, Stefano Ermon`\n    | :venue:`ICML 2019`\n\nBioinformatics and Chemistry\n--------------\n\n`Protein Interface Prediction using Graph Convolutional Networks\n\u003chttps://papers.nips.cc/paper/7231-protein-interface-prediction-using-graph-convolutional-networks.pdf\u003e`_\n    | :authors:`Alex Fout, Jonathon Byrd, Basir Shariat, Asa Ben-Hur`\n    | :venue:`NeurIPS 2017`\n\n`Modeling Polypharmacy Side Effects with Graph Convolutional Networks\n\u003chttps://arxiv.org/abs/1802.00543\u003e`_\n    | :authors:`Marinka Zitnik, Monica Agrawal, Jure Leskovec`\n    | :venue:`Bioinformatics 2018`\n\n`NeoDTI: Neural Integration of Neighbor Information from a Heterogeneous Network for Discovering New\nDrug–target Interactions\n\u003chttps://academic.oup.com/bioinformatics/article-abstract/35/1/104/5047760?redirectedFrom=fulltext\u003e`_\n    | :authors:`Fangping Wan, Lixiang Hong, An Xiao, Tao Jiang, Jianyang Zeng`\n    | :venue:`Bioinformatics 2018`\n\n`SELFIES: a Robust Representation of Semantically Constrained Graphs with an Example Application in Chemistry\n\u003chttps://arxiv.org/pdf/1905.13741.pdf\u003e`_\n    | :authors:`Mario Krenn, Florian Häse, AkshatKumar Nigam, Pascal Friederich, Alán Aspuru-Guzik`\n    | :venue:`arXiv 2019`\n\n`Drug-Drug Adverse Effect Prediction with Graph Co-Attention\n\u003chttps://arxiv.org/pdf/1905.00534.pdf\u003e`_\n    | :authors:`Andreea Deac, Yu-Hsiang Huang, Petar Veličković, Pietro Liò, Jian Tang`\n    | :venue:`ICML 2019 Workshop`\n\n`GCN-MF: Disease-Gene Association Identification By Graph Convolutional Networks and Matrix Factorization\n\u003chttps://www.kdd.org/kdd2019/accepted-papers/view/gcn-mf-disease-gene-association-identification-by-graph-convolutional-netwo\u003e`_\n    | :authors:`Peng Han, Peng Yang, Peilin Zhao, Shuo Shang, Yong Liu, Jiayu Zhou, Xin Gao, Panos Kalnis`\n    | :venue:`KDD 2019`\n\n`Detecting drug-drug interactions using artificial neural networks and classic graph similarity measures\n\u003chttps://arxiv.org/pdf/1903.04571.pdf\u003e`_\n    | :authors:`Guy Shtar, Lior Rokach, Bracha Shapira`\n    | :venue:`arXiv 2019`\n\n`PGCN: Disease gene prioritization by disease and gene embedding through graph convolutional neural networks\n\u003chttps://www.biorxiv.org/content/biorxiv/early/2019/01/28/532226.full.pdf\u003e`_\n    | :authors:`Yu Li, Hiroyuki Kuwahara, Peng Yang, Le Song, Xin Gao`\n    | :venue:`bioRxiv 2019`\n\n`Identifying Protein-Protein Interaction using Tree LSTM and Structured Attention\n\u003chttps://ieeexplore.ieee.org/abstract/document/8665584\u003e`_\n    | :authors:`Mahtab Ahmed, Jumayel Islam, Muhammad Rifayat Samee, Robert E. Mercer`\n    | :venue:`ICSC 2019`\n\n`GCN-MF: Disease-Gene Association Identification By Graph Convolutional Networks and Matrix Factorization\n\u003chttps://dl.acm.org/citation.cfm?id=3330912\u003e`_\n    | :authors:`Peng Han, Peng Yang, Peilin Zhao, Shuo Shang, Yong Liu, Jiayu Zhou, Xin Gao, Panos Kalnis`\n    | :venue:`KDD 2019`\n\n`Towards perturbation prediction of biological networks using deep learning\n\u003chttps://www.nature.com/articles/s41598-019-48391-y\u003e`_\n    | :authors:`Diya Li, Jianxi Gao`\n    | :venue:`Nature 2019`\n\n`Directional Message Passing for Molecular Graphs\n\u003chttps://openreview.net/pdf?id=B1eWbxStPH\u003e`_\n\t| :authors:`Johannes Klicpera, Janek Groß, Stephan Günnemann`\n\t| :venue:`ICLR 2020`\n\nGraph Algorithms\n---------------\n\n`Neural Execution of Graph Algorithms\n\u003chttps://openreview.net/pdf?id=SkgKO0EtvS\u003e`_\n\t| :authors:`Petar Veličković, Rex Ying, Matilde Padovano, Raia Hadsell, Charles Blundell`\n\t| :venue:`ICLR 2020`\n\nTheorem Proving\n---------------\n\n`Premise Selection for Theorem Proving by Deep Graph Embedding\n\u003chttps://arxiv.org/abs/1709.09994\u003e`_\n    | :authors:`Mingzhe Wang, Yihe Tang, Jian Wang, Jia Deng`\n    | :venue:`NeurIPS 2017`\n\nGraph Generation\n================\n\n`GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models\n\u003chttps://arxiv.org/abs/1802.08773\u003e`_\n    | :authors:`Jiaxuan You, Rex Ying, Xiang Ren, William L. Hamilton, Jure Leskovec`\n    | :venue:`ICML 2018`\n\n`NetGAN: Generating Graphs via Random Walks\n\u003chttps://arxiv.org/abs/1803.00816\u003e`_\n    | :authors:`Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, Stephan Günnemann`\n    | :venue:`ICML 2018`\n\n`Learning Deep Generative Models of Graphs\n\u003chttps://arxiv.org/abs/1803.03324\u003e`_\n    | :authors:`Yujia Li, Oriol Vinyals, Chris Dyer, Razvan Pascanu, Peter Battaglia`\n    | :venue:`ICML 2018`\n\n`Junction Tree Variational Autoencoder for Molecular Graph Generation\n\u003chttps://arxiv.org/abs/1802.04364\u003e`_\n    | :authors:`Wengong Jin, Regina Barzilay, Tommi Jaakkola`\n    | :venue:`ICML 2018`\n\n`MolGAN: An implicit generative model for small molecular graphs\n\u003chttps://arxiv.org/abs/1805.11973\u003e`_\n    | :authors:`Nicola De Cao, Thomas Kipf`\n    | :venue:`arXiv 2018`\n\n`Generative Modeling for Protein Structures\n\u003chttps://papers.nips.cc/paper/7978-generative-modeling-for-protein-structures.pdf\u003e`_\n    | :authors:`Namrata Anand, Po-Ssu Huang`\n    | :venue:`NeurIPS 2018`\n\n`Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders\n\u003chttps://arxiv.org/abs/1809.02630\u003e`_\n    | :authors:`Tengfei Ma, Jie Chen, Cao Xiao`\n    | :venue:`NeurIPS 2018`\n\n`Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation\n\u003chttps://arxiv.org/abs/1806.02473\u003e`_\n    | :authors:`Jiaxuan You, Bowen Liu, Rex Ying, Vijay Pande, Jure Leskovec`\n    | :venue:`NeurIPS 2018`\n\n`Constrained Graph Variational Autoencoders for Molecule Design\n\u003chttps://arxiv.org/abs/1805.09076\u003e`_\n    | :authors:`Qi Liu, Miltiadis Allamanis, Marc Brockschmidt, Alexander L. Gaunt`\n    | :venue:`NeurIPS 2018`\n\n`Learning Multimodal Graph-to-Graph Translation for Molecule Optimization\n\u003chttps://arxiv.org/abs/1812.01070\u003e`_\n    | :authors:`Wengong Jin, Kevin Yang, Regina Barzilay, Tommi Jaakkola`\n    | :venue:`ICLR 2019`\n\n`Generative Code Modeling with Graphs\n\u003chttps://openreview.net/forum?id=Bke4KsA5FX\u003e`_\n    | :authors:`Marc Brockschmidt, Miltiadis Allamanis, Alexander L. Gaunt, Oleksandr Polozov`\n    | :venue:`ICLR 2019`\n\n`DAG-GNN: DAG Structure Learning with Graph Neural Networks\n\u003chttps://arxiv.org/abs/1904.10098\u003e`_\n    | :authors:`Yue Yu, Jie Chen, Tian Gao, Mo Yu`\n    | :venue:`ICML 2019`\n\n`Graph to Graph: a Topology Aware Approach for Graph Structures Learning and Generation\n\u003chttp://proceedings.mlr.press/v89/sun19c.html\u003e`_\n    | :authors:`Mingming Sun, Ping Li`\n    | :venue:`AISTATS 2019`\n\n`Graph Normalizing Flows\n\u003chttps://arxiv.org/abs/1905.13177\u003e`_\n    | :authors:`Jenny Liu, Aviral Kumar, Jimmy Ba, Jamie Kiros, Kevin Swersky`\n    | :venue:`NeurIPS 2019`\n\n`Conditional Structure Generation through Graph Variational Generative Adversarial Nets\n\u003chttp://jiyang3.web.engr.illinois.edu/files/condgen.pdf\u003e`_\n    | :authors:`Carl Yang, Peiye Zhuang, Wenhan Shi, Alan Luu, Pan Li`\n    | :venue:`NeurIPS 2019`\n\n`Efficient Graph Generation with Graph Recurrent Attention Networks\n\u003chttps://arxiv.org/pdf/1910.00760.pdf\u003e`_\n    | :authors:`Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, Charlie Nash, William L. Hamilton, David Duvenaud, Raquel Urtasun, Richard Zemel`\n    | :venue:`NeurIPS 2019`\n\n`GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation\n\u003chttps://openreview.net/pdf?id=S1esMkHYPr\u003e`_\n\t| :authors:`Chence Shi, Minkai Xu, Zhaocheng Zhu, Weinan Zhang, Ming Zhang, Jian Tang`\n\t| :venue:`ICLR 2020`\n\nGraph Layout and High-dimensional Data Visualization\n====================================================\n\n`Visualizing Data using t-SNE\n\u003chttp://www.jmlr.org/papers/volume9/vandermaaten08a/vandermaaten08a.pdf\u003e`_\n    | :authors:`Laurens van der Maaten, Geoffrey Hinton`\n    | :venue:`JMLR 2008`\n\n`Visualizing non-metric similarities in multiple maps\n\u003chttps://link.springer.com/content/pdf/10.1007/s10994-011-5273-4.pdf\u003e`_\n    | :authors:`Laurens van der Maaten, Geoffrey Hinton`\n    | :venue:`ML 2012`\n\n`Visualizing Large-scale and High-dimensional Data\n\u003chttps://arxiv.org/pdf/1602.00370\u003e`_\n    | :authors:`Jian Tang, Jingzhou Liu, Ming Zhang, Qiaozhu Mei`\n    | :venue:`WWW 2016`\n\n`GraphTSNE: A Visualization Technique for Graph-Structured Data\n\u003chttps://arxiv.org/pdf/1904.06915.pdf\u003e`_\n    | :authors:`Yao Yang Leow, Thomas Laurent, Xavier Bresson`\n    | :venue:`ICLR 2019 Workshop`\n\nGraph Representation Learning Systems\n=====================================\n\n`GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding\n\u003chttps://arxiv.org/pdf/1903.00757\u003e`_\n    | :authors:`Zhaocheng Zhu, Shizhen Xu, Meng Qu, Jian Tang`\n    | :venue:`WWW 2019`\n\n`PyTorch-BigGraph: A Large-scale Graph Embedding System\n\u003chttps://arxiv.org/pdf/1903.12287\u003e`_\n    | :authors:`Adam Lerer, Ledell Wu, Jiajun Shen, Timothee Lacroix, Luca Wehrstedt, Abhijit Bose, Alex Peysakhovich`\n    | :venue:`SysML 2019`\n\n`AliGraph: A Comprehensive Graph Neural Network Platform\n\u003chttps://arxiv.org/pdf/1902.08730\u003e`_\n    | :authors:`Rong Zhu, Kun Zhao, Hongxia Yang, Wei Lin, Chang Zhou, Baole Ai, Yong Li, Jingren Zhou`\n    | :venue:`VLDB 2019`\n\n`Deep Graph Library\n\u003chttps://www.dgl.ai\u003e`_\n    | :authors:`DGL Team`\n\n`AmpliGraph\n\u003chttps://github.com/Accenture/AmpliGraph\u003e`_\n    | :authors:`Luca Costabello, Sumit Pai, Chan Le Van, Rory McGrath, Nicholas McCarthy, Pedro Tabacof`\n\n`Euler\n\u003chttps://github.com/alibaba/euler\u003e`_\n    | :authors:`Alimama Engineering Platform Team, Alimama Search Advertising Algorithm Team`\n\nDatasets\n========\n\n`ATOMIC: an atlas of machine commonsense for if-then reasoning\n\u003chttps://wvvw.aaai.org/ojs/index.php/AAAI/article/download/4160/4038\u003e`_\n    | :authors:`Maarten Sap, Ronan Le Bras, Emily Allaway, Chandra Bhagavatula, Nicholas Lourie, Hannah Rashkin, Brendan Roof, Noah A. Smith, Yejin Choi`\n    | :venue:`AAAI 2019`\n\n\n","funding_links":[],"categories":["MACHINE LEARNING","Uncategorized","Others","Table of Contents","Awesome lists"],"sub_categories":["Deep Learning","Uncategorized","Graph theory"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FDeepGraphLearning%2FLiteratureDL4Graph","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FDeepGraphLearning%2FLiteratureDL4Graph","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FDeepGraphLearning%2FLiteratureDL4Graph/lists"}