[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"tool-DeepGraphLearning--LiteratureDL4Graph":3,"similar-DeepGraphLearning--LiteratureDL4Graph":45},{"id":4,"github_repo":5,"name":6,"description_en":7,"description_zh":8,"ai_summary_zh":8,"readme_en":9,"readme_zh":10,"quickstart_zh":11,"use_case_zh":12,"hero_image_url":13,"owner_login":14,"owner_name":15,"owner_avatar_url":16,"owner_bio":17,"owner_company":18,"owner_location":18,"owner_email":19,"owner_twitter":18,"owner_website":20,"owner_url":21,"languages":18,"stars":22,"forks":23,"last_commit_at":24,"license":25,"difficulty_score":26,"env_os":27,"env_gpu":28,"env_ram":28,"env_deps":29,"category_tags":32,"github_topics":34,"view_count":39,"oss_zip_url":18,"oss_zip_packed_at":18,"status":40,"created_at":41,"updated_at":42,"faqs":43,"releases":44},9474,"DeepGraphLearning\u002FLiteratureDL4Graph","LiteratureDL4Graph","A comprehensive collection of recent papers on graph deep learning","LiteratureDL4Graph 是一个专注于图深度学习领域的学术文献汇总库，旨在为研究者和开发者提供一份全面、前沿的论文清单。面对图神经网络领域论文爆发式增长、知识分散难以追踪的痛点，它将大量高质量研究成果进行了系统化梳理与分类。\n\n该资源库涵盖了从经典的无监督节点表示学习（如 DeepWalk、node2vec）到最新的变分图自编码器、对抗生成网络等核心技术方向。其独特亮点在于提供了灵活的检索维度：用户既可按“主题”快速定位特定技术路线（如结构身份识别、高阶邻近性近似），也可按“发表 venue\"追踪顶级会议的最新动态。每篇文献均清晰标注了作者、发表年份及核心关键词，极大地降低了文献调研的时间成本。\n\nLiteratureDL4Graph 特别适合人工智能领域的研究人员、算法工程师以及高校师生使用。无论是希望快速入门图深度学习的新手，还是需要把握行业脉搏、寻找创新灵感的资深专家，都能从中高效获取所需的知识养分，是探索图数据奥秘不可或缺的导航工具。","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>\u003Ca href=\"README.rst\">Sort by topic\u003C\u002Fa>\u003C\u002Fdiv>\n    \u003Cdiv>\u003Ca href=\"BYVENUE.rst\">Sort by venue\u003C\u002Fa>\u003C\u002Fdiv>\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1403.6652>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1503.03578>`_\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:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=2806512>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1607.00653>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1611.07308>`_\n    | :authors:`Thomas N. Kipf, Max Welling`\n    | :venue:`arXiv 2016`\n\n`Scalable Graph Embedding for Asymmetric Proximity\n\u003Chttps:\u002F\u002Faaai.org\u002Focs\u002Findex.php\u002FAAAI\u002FAAAI17\u002Fpaper\u002Fview\u002F14696>`_\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:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2017\u002F544>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1704.03165>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1705.08039>`_\n    | :authors:`Maximilian Nickel, Douwe Kiela`\n    | :venue:`NIPS 2017`\n\n`VERSE: Versatile Graph Embeddings from Similarity Measures\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1803.04742>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1710.02971>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1710.10321>`_\n    | :authors:`Claire Donnat, Marinka Zitnik, David Hallac, Jure Leskovec`\n    | :venue:`KDD 2018`\n\n`Adversarial Network Embedding\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1711.07838>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1711.08267>`_\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:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3291029>`_\n    | :authors:`Xin Liu, Tsuyoshi Murata, Kyoung-Sook Kim, Chatchawan Kotarasu, Chenyi Zhuang`\n    | :venue:`WSDM 2019`\n\n`Deep Graph Infomax\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1809.10341>`_\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:\u002F\u002Fkeg.cs.tsinghua.edu.cn\u002Fjietang\u002Fpublications\u002Fwww19-Qiu-et-al-NetSMF-Large-Scale-Network-Embedding.pdf>`_\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:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3313445>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1906.07159.pdf>`_\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:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3330866>`_\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:\u002F\u002Fopenreview.net\u002Fpdf?id=r1lGO0EKDH>`_\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:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=2556225>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1508.00200>`_\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:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=2783296>`_\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:\u002F\u002Fwww.aaai.org\u002Focs\u002Findex.php\u002FIJCAI\u002FIJCAI15\u002Fpaper\u002Fview\u002F11098>`_\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:\u002F\u002Fwww.ijcai.org\u002FProceedings\u002F16\u002FPapers\u002F547.pdf>`_\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:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3098036>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1610.09769>`_\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:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3132953>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1709.06636>`_\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:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3132909>`_\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:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3132847.3132905>`_\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:\u002F\u002Faclweb.org\u002Fanthology\u002Fpapers\u002FP\u002FP17\u002FP17-1158\u002F>`_\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:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3219986>`_\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:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3209978.3209987>`_\n    | :authors:`Ming Gao, Leihui Chen, Xiangnan He, Aoying Zhou`\n    | :venue:`SIGIR 2018`\n\n`StarSpace: Embed All The Things\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1709.03856>`_\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:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3159655>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1712.00732>`_\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:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3159680>`_\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:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3159711>`_\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:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FGenerative-Adversarial-Network-Based-Heterogeneous-Cai-Han\u002F1596d6487012696ba400fb69904a2c372a08a2be>`_\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:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2018\u002F438>`_\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:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2018\u002F397>`_\n    | :authors:`Wei Wu, Bin Li, Ling Chen, Chengqi Zhang`\n    | :venue:`IJCAI 2018`\n\n`Deep Attributed Network Embedding\n\u003Chttps:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2018\u002F467>`_\n    | :authors:`Hongchang Gao, Heng Huang`\n    | :venue:`IJCAI 2018`\n\n`Co-Regularized Deep Multi-Network Embedding\n\u003Chttps:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3186113>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1807.03490>`_\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:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FMeta-Graph-Based-HIN-Spectral-Embedding%3A-Methods%2C-Yang-Feng\u002F4d5f4d6785d550383e3f3afb04c3015bf0d28405>`_\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:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3186117>`_\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:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3330924>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1705.05742.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1805.11273.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1706.01860.pdf>`_\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:\u002F\u002Fyangy.org\u002Fworks\u002Fdynamictriad\u002Fdynamic_triad.pdf>`_\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:\u002F\u002Fpdfs.semanticscholar.org\u002F9499\u002Fb38866b1eb87ae43fa5be02f9d08cd3c20a8.pdf?_ga=2.6780794.935636364.1561139530-1831876308.1523264869>`_\n    | :authors:`Jianxin Ma, Peng Cui, Wenwu Zhu`\n    | :venue:`AAAI 2018`\n\n`TIMERS: Error-Bounded SVD Restart on Dynamic Networks\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1711.09541.pdf>`_\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:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3219819.3220043>`_\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:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2018\u002F0288.pdf>`_\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:\u002F\u002Fopenreview.net\u002Fpdf?id=HyePrhR5KX>`_\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:\u002F\u002Fcs.stanford.edu\u002F~srijan\u002Fpubs\u002Fjodie-kdd2019.pdf>`_\n    | :authors:`Srijan Kumar, Xikun Zhang, Jure Leskovec`\n    | :venue:`KDD 2019`\n\n`Variational Graph Recurrent Neural Networks\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1908.09710.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1907.03395.pdf>`_\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:\u002F\u002Fwww.icml-2011.org\u002Fpapers\u002F438_icmlpaper.pdf>`_\n    | :authors:`Maximilian Nickel, Volker Tresp, Hans-Peter Kriegel`\n    | :venue:`ICML 2011`\n\n`Translating Embeddings for Modeling Multi-relational Data\n\u003Chttps:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F5071-translating-embeddings-for-modeling-multi-relational-data.pdf>`_\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:\u002F\u002Fwww.aaai.org\u002Focs\u002Findex.php\u002FAAAI\u002FAAAI14\u002Fpaper\u002FviewFile\u002F8531\u002F8546>`_\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:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F5448-reducing-the-rank-in-relational-factorization-models-by-including-observable-patterns.pdf>`_\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:\u002F\u002Fwww.aaai.org\u002Focs\u002Findex.php\u002FAAAI\u002FAAAI15\u002Fpaper\u002FviewFile\u002F9571\u002F9523>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1503.00759.pdf>`_\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:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FP15-1067>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1506.00379>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1412.6575>`_\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:\u002F\u002Fwww.aaai.org\u002Focs\u002Findex.php\u002FAAAI\u002FAAAI16\u002Fpaper\u002FviewPDFInterstitial\u002F12484\u002F11828>`_\n    | :authors:`Maximilian Nickel, Lorenzo Rosasco, Tomaso Poggio`\n    | :venue:`AAAI 2016`\n\n`Complex Embeddings for Simple Link Prediction\n\u003Chttp:\u002F\u002Fwww.jmlr.org\u002Fproceedings\u002Fpapers\u002Fv48\u002Ftrouillon16.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1703.06103>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1710.10881>`_\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:\u002F\u002Fwww.aaai.org\u002Focs\u002Findex.php\u002FAAAI\u002FAAAI18\u002Fpaper\u002Fdownload\u002F17366\u002F15884>`_\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:\u002F\u002Fwww.aaai.org\u002Focs\u002Findex.php\u002FAAAI\u002FAAAI18\u002Fpaper\u002Fdownload\u002F16369\u002F16011>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1711.04071>`_\n    | :authors:`Liwei Cai, William Yang Wang`\n    | :venue:`NAACL 2018`\n\n`Improving Knowledge Graph Embedding Using Simple Constraints\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1805.02408>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1802.04868>`_\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:\u002F\u002Faclweb.org\u002Fanthology\u002Fpapers\u002FN\u002FN18\u002FN18-2053\u002F>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1903.08948>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1902.10197>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1906.01195>`_\n    | :authors:`Deepak Nathani, Jatin Chauhan, Charu Sharma, Manohar Kaul`\n    | :venue:`ACL 2019`\n\n`Probabilistic Logic Neural Networks for Reasoning\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1906.08495.pdf>`_\n    | :authors:`Meng Qu, Jian Tang`\n    | :venue:`NeurIPS 2019`\n\n`Quaternion Knowledge Graph Embeddings\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1904.10281.pdf>`_\n    | :authors:`Shuai Zhang, Yi Tay, Lina Yao, Qi Liu`\n    | :venue:`NeurIPS 2019`\n\n`Quantum Embedding of Knowledge for Reasoning\n\u003Chttps:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F8797-quantum-embedding-of-knowledge-for-reasoning.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.09791.pdf>`_\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:\u002F\u002Fopenreview.net\u002Fforum?id=rkeuAhVKvB>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1603.08861>`_\n    | :authors:`Zhilin Yang, William W. Cohen, Ruslan Salakhutdinov`\n    | :venue:`ICML 2016`\n\n`Semi-Supervised Classification with Graph Convolutional Networks\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1609.02907>`_\n    | :authors:`Thomas N. Kipf, Max Welling`\n    | :venue:`ICLR 2017`\n\n`Neural Message Passing for Quantum Chemistry\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1704.01212>`_\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:\u002F\u002Fgearons.org\u002Fassets\u002Fdocs\u002Fmotif-aware-graph-final.pdf>`_\n    | :authors:`Hoang Nguyen, Tsuyoshi Murata`\n    | :venue:`IJCAI 2017`\n\n`Learning Graph Representations with Embedding Propagation\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1710.03059>`_\n    | :authors:`Alberto Garcia-Duran, Mathias Niepert`\n    | :venue:`NIPS 2017`\n\n`Inductive Representation Learning on Large Graphs\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1706.02216>`_\n    | :authors:`William L. Hamilton, Rex Ying, Jure Leskovec`\n    | :venue:`NIPS 2017`\n\n`Graph Attention Networks\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1710.10903>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1801.10247>`_\n    | :authors:`Jie Chen, Tengfei Ma, Cao Xiao`\n    | :venue:`ICLR 2018`\n\n`Representation Learning on Graphs with Jumping Knowledge Networks\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1806.03536>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1710.10568>`_\n    | :authors:`Jianfei Chen, Jun Zhu, Le Song`\n    | :venue:`ICML 2018`\n\n`Large-Scale Learnable Graph Convolutional Networks\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1808.03965>`_\n    | :authors:`Hongyang Gao, Zhengyang Wang, Shuiwang Ji`\n    | :venue:`KDD 2018`\n\n`Adaptive Sampling Towards Fast Graph Representation Learning\n\u003Chttps:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F7707-adaptive-sampling-towards-fast-graph-representation-learning.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1806.08804>`_\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:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F7440-bayesian-semi-supervised-learning-with-graph-gaussian-processes.pdf>`_\n    | :authors:`Yin Cheng Ng, Nicolò Colombo, Ricardo Silva`\n    | :venue:`NeurIPS 2018`\n\n`Pitfalls of Graph Neural Network Evaluation\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1811.05868>`_\n    | :authors:`Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, Stephan Günnemann`\n    | :venue:`arXiv 2018`\n\n`Heterogeneous Graph Attention Network\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1903.07293>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1811.11103.pdf>`_\n    | :authors:`Yingxue Zhang, Soumyasundar Pal, Mark Coates, Deniz Üstebay`\n    | :venue:`AAAI 2019`\n\n`How Powerful are Graph Neural Networks?\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1810.00826>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1901.01484>`_\n    | :authors:`Renjie Liao, Zhizhen Zhao, Raquel Urtasun, Richard S. Zemel`\n    | :venue:`ICLR 2019`\n\n`Graph Wavelet Neural Network\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1904.07785>`_\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:\u002F\u002Fopenreview.net\u002Fpdf?id=H1g0Z3A9Fm>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1810.05997>`_\n    | :authors:`Johannes Klicpera, Aleksandar Bojchevski, Stephan Günnemann`\n    | :venue:`ICLR 2019`\n\n`Invariant and Equivariant Graph Networks\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1812.09902>`_\n    | :authors:`Haggai Maron, Heli Ben-Hamu, Nadav Shamir, Yaron Lipman`\n    | :venue:`ICLR 2019`\n\n`Capsule Graph Neural Network\n\u003Chttps:\u002F\u002Fopenreview.net\u002Fpdf?id=Byl8BnRcYm>`_\n    | :authors:`Zhang Xinyi, Lihui Chen`\n    | :venue:`ICLR 2019`\n\n`MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.00067>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.05178>`_\n    | :authors:`Hongyang Gao, Shuiwang Ji`\n    | :venue:`ICML 2019`\n\n`Disentangled Graph Convolutional Networks\n\u003Chttp:\u002F\u002Fproceedings.mlr.press\u002Fv97\u002Fma19a\u002Fma19a.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.06214>`_\n    | :authors:`Meng Qu, Yoshua Bengio, Jian Tang`\n    | :venue:`ICML 2019`\n\n`Simplifying Graph Convolutional Networks\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1902.07153>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1906.04817>`_\n    | :authors:`Jiaxuan You, Rex Ying, Jure Leskovec`\n    | :venue:`ICML 2019`\n\n`Self-Attention Graph Pooling\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1904.08082>`_\n    | :authors:`Junhyun Lee, Inyeop Lee, Jaewoo Kang`\n    | :venue:`ICML 2019`\n\n`Relational Pooling for Graph Representations\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1903.02541>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1907.04652.pdf>`_\n    | :authors:`Hongyang Gao, Shuiwang Ji`\n    | :venue:`KDD 2019`\n\n`Conditional Random Field Enhanced Graph Convolutional Neural Networks\n\u003Chttps:\u002F\u002Fwww.kdd.org\u002Fkdd2019\u002Faccepted-papers\u002Fview\u002Fconditional-random-field-enhanced-graph-convolutional-neural-networks>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1905.07953>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1906.02319>`_\n    | :authors:`Jun Wu, Jingrui He, Jiejun Xu`\n    | :venue:`KDD 2019`\n\n`HetGNN: Heterogeneous Graph Neural Network\n\u003Chttps:\u002F\u002Fwww.kdd.org\u002Fkdd2019\u002Faccepted-papers\u002Fview\u002Fhetgnn-heterogeneous-graph-neural-network>`_\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:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3292500.3330941>`_\n    | :authors:`Xiao Huang, Qingquan Song, Yuening Li, Xia Hu`\n    | :venue:`KDD 2019`\n\n`Graph Convolutional Networks with EigenPooling\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1904.13107>`_\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:\u002F\u002Fusers.cecs.anu.edu.au\u002F~u5170295\u002Fpapers\u002Fnips-wijesinghe-2019.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1907.05008.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.10769.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1910.02548.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1906.02174.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1806.09206.pdf>`_\n    | :authors:`Shengchao Liu, Thevaa Chandereng, Yingyu Liang`\n    | :venue:`NeurIPS 2019`\n\n`DeepGCNs: Can GCNs Go as Deep as CNNs?\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1904.03751.pdf>`_\n    | :authors:`Guohao Li, Matthias Muller, Ali Thabet, Bernard Ghanem`\n    | :venue:`ICCV 2019`\n\n`Continuous Graph Neural Networks\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1912.00967.pdf>`_\n    | :authors:`Louis-Pascal A. C. Xhonneux, Meng Qu, Jian Tang`\n    | :venue:`arXiv 2019`\n\n`Curvature Graph Network\n\u003Chttps:\u002F\u002Fopenreview.net\u002Fpdf?id=BylEqnVFDB>`_\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:\u002F\u002Fopenreview.net\u002Fpdf?id=r1laNeBYPB>`_\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:\u002F\u002Fopenreview.net\u002Fpdf?id=HJlWWJSFDH>`_\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:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FD17-1159>`_\n    | :authors:`Diego Marcheggiani, Ivan Titov`\n    | :venue:`EMNLP 2017`\n\n`Graph Convolutional Encoders for Syntax-aware Neural Machine Translation\n\u003Chttps:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FD17-1209>`_\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:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FK17-1045>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1804.09541.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1703.03130.pdf>`_\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:\u002F\u002Faclweb.org\u002Fanthology\u002FC18-1280>`_\n    | :authors:`Daniil Sorokin, Iryna Gurevych`\n    | :venue:`COLING 2018`\n\n`Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks\n\u003Chttps:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FN18-2078>`_\n    | :authors:`Diego Marcheggiani, Joost Bastings, Ivan Titov`\n    | :venue:`NAACL 2018`\n\n`Linguistically-Informed Self-Attention for Semantic Role Labeling\n\u003Chttps:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FD18-1548>`_\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:\u002F\u002Faclweb.org\u002Fanthology\u002FD18-1244>`_\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:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FP18-1150>`_\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:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FP18-1026>`_\n    | :authors:`Daniel Beck, Gholamreza Haffari, Trevor Cohn`\n    | :venue:`ACL 2018`\n\n`Graph Convolutional Networks for Text Classification\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1809.05679.pdf>`_\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:\u002F\u002Fopenreview.net\u002Fpdf?id=BJlgNh0qKQ>`_\n    | :authors:`Caio Corro, Ivan Titov`\n    | :venue:`ICLR 2019`\n\n`Structured Neural Summarization\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1811.01824.pdf>`_\n    | :authors:`Patrick Fernandes, Miltiadis Allamanis, Marc Brockschmid`\n    | :venue:`ICLR 2019`\n\n`Multi-task Learning over Graph Structures\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1811.10211.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1903.02591.pdf>`_\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:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FN19-1173>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1903.01306.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1902.00756.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.06933.pdf>`_\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:\u002F\u002Fwww.cs.purdue.edu\u002Fhomes\u002Fdgoldwas\u002F\u002Fdownloads\u002Fpapers\u002FLiG_acl_2019.pdf>`_\n    | :authors:`Chang Li, Dan Goldwasser`\n    | :venue:`ACL 2019`\n\n`Attention Guided Graph Convolutional Networks for Relation Extraction\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1906.07510.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1809.04283.pdf>`_\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:\u002F\u002Ftsujuifu.github.io\u002Fpubs\u002Facl19_graph-rel.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.07374.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.05460.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1906.01231.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1802.07459.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1906.03753.pdf>`_\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:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FP19-1247.pdf>`_\n    | :authors:`Chang Li, Dan Goldwasser`\n    | :venue:`ACL 2019`\n\n`A Neural Multi-digraph Model for Chinese NER with Gazetteers\n\u003Chttps:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FP19-1141.pdf>`_\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:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FP19-1342.pdf>`_\n    | :authors:`Yuan Zhang, Yue Zhang`\n    | :venue:`ACL 2019`\n\n`A2N: Attending to Neighbors for Knowledge Graph Inference\n\u003Chttps:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FP19-1431.pdf>`_\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:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FP19-1347.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.08868.pdf>`_\n    | :authors:`Yinchuan Xu, Junlin Yang`\n    | :venue:`ACL 2019 Workshop`\n    | :keywords:`https:\u002F\u002Fgithub.com\u002Fianycxu\u002FRGCN-with-BERT`\n\n`Learning Graph Pooling and Hybrid Convolutional Operations for Text Representations\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1901.06965.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1909.12231.pdf>`_\n    | :authors:`Diego Antognini, Boi Faltings`\n    | :venue:`EMNLP 2019`\n\n`Dependency-Guided LSTM-CRF for Named Entity Recognition\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1909.10148.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1909.08211.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1908.11540.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1909.00136.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1909.02151.pdf>`_\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:\u002F\u002Fwww.cs.toronto.edu\u002F~rjliao\u002Fpapers\u002Ficcv_2017_3DGNN.pdf>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1708.04320>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1707.08301>`_\n    | :authors:`Renata Khasanova, Pascal Frossard`\n    | :venue:`ICCV 2017`\n\n`Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1801.07455>`_\n    | :authors:`Sijie Yan, Yuanjun Xiong, Dahua Lin`\n    | :venue:`AAAI 2018`\n\n`Image Generation from Scene Graphs\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1804.01622>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1712.07262>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1802.02669>`_\n    | :authors:`Haowen Deng, Tolga Birdal, Slobodan Ilic`\n    | :venue:`CVPR 2018`\n\n`Iterative Visual Reasoning Beyond Convolutions\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1803.11189>`_\n    | :authors:`Xinlei Chen, Li-Jia Li, Li Fei-Fei, Abhinav Gupta`\n    | :venue:`CVPR 2018`\n\n`Surface Networks\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1705.10819>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1706.05206>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1712.07576>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1712.06760>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1712.00268>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1804.01654>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1808.07962>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1807.10267>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1711.06721>`_\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:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_ECCV_2018\u002Fpapers\u002FMichelle_Guo_Neural_Graph_Matching_ECCV_2018_paper.pdf>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1809.05370>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1906.00377>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1808.00191>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1809.07041>`_\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:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F8135-beyond-grids-learning-graph-representations-for-visual-recognition>`_\n    | :authors:`Yin Li, Abhinav Gupta`\n    | :venue:`NeurIPS 2018`\n\n`Learning Conditioned Graph Structures for Interpretable Visual Question Answering\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1806.07243>`_\n    | :authors:`Will Norcliffe-Brown, Efstathios Vafeias, Sarah Parisot`\n    | :venue:`NeurIPS 2018`\n\n`LinkNet: Relational Embedding for Scene Graph\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1811.06410>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1806.08047>`_\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:\u002F\u002Fopenreview.net\u002Fforum?id=SJeXSo09FQ>`_\n    | :authors:`Diego Valsesia, Giulia Fracastoro, Enrico Magli`\n    | :venue:`ICLR 2019`\n\n`Graph-Based Global Reasoning Networks\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1811.12814>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1807.11637>`_\n    | :authors:`Jin Zeng, Jiahao Pang, Wenxiu Sun, Gene Cheung`\n    | :venue:`CVPR 2019`\n\n`Learning Context Graph for Person Search\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1904.01830>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1904.04536>`_\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:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPRW_2019\u002Fpapers\u002FTRMTMCT\u002FBao_Masked_Graph_Attention_Network_for_Person_Re-Identification_CVPRW_2019_paper.pdf>`_\nfor_Person_Re-Identification_CVPRW_2019_paper.html>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1904.02749>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1904.12659>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1904.03579>`_\n    | :authors:`Guangrun Wang, Keze Wang, Liang Lin`\n    | :venue:`CVPR 2019`\n\n`Reasoning Visual Dialogs with Structural and Partial Observations\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1904.03579>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1809.05910.pdf>`_\n    | :authors:`Rana Hanocka, Amir Hertz, Noa Fish, Raja Giryes, Shachar Fleishman, Daniel Cohen-Or`\n    | :venue:`SIGGRAPH 2019`\n    | :keywords:`https:\u002F\u002Franahanocka.github.io\u002FMeshCNN\u002F`\n\n`Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learning\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1908.02441.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1908.01491.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1908.05436.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1908.06648.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1908.11754.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1909.02144.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1909.02701.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1909.03252.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1909.01161.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1806.01973.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1811.02815.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1902.09362.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1903.10433.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1902.07243.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1811.00855.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1904.10322.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.08108.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1906.01829.pdf>`_\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:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3330686>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1908.04032.pdf>`_\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:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F7763-link-prediction-based-on-graph-neural-networks.pdf>`_\n    | :authors:`Muhan Zhang, Yixin Chen`\n    | :venue:`NeurIPS 2018`\n\n`Link Prediction via Subgraph Embedding-Based Convex Matrix Completion\n\u003Chttp:\u002F\u002Fiiis.tsinghua.edu.cn\u002F~weblt\u002Fpapers\u002Flink-prediction-subgraphembeddings.pdf>`_\n    | :authors:`Zhu Cao, Linlin Wang, Gerard de Melo`\n    | :venue:`AAAI 2018`\n\n`Graph Convolutional Matrix Completion\n\u003Chttps:\u002F\u002Fwww.kdd.org\u002Fkdd2018\u002Ffiles\u002Fdeep-learning-day\u002FDLDay18_paper_32.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1908.07078.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1807.05560.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.08865.pdf>`_\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:\u002F\u002Fopenreview.net\u002Fpdf?id=rkgW0oA9FX>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1904.11088.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1709.04271.pdf>`_\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:\u002F\u002Fopenreview.net\u002Fpdf?id=S1sqHMZCb>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1806.01242.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1806.06464.pdf>`_\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:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F7960-relational-recurrent-neural-networks.pdf>`_\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:\u002F\u002Fwww.cse.iitd.ac.in\u002F~mausam\u002Fpapers\u002Fnips18.pdf>`_\n    | :authors:`Aniket Bajpai, Sankalp Garg, Mausam`\n    | :venue:`NeurIPS 2018`\n\n`Neural Graph Evolution: Towards Efficient Automatic Robot Design\n\u003Chttps:\u002F\u002Fopenreview.net\u002Fpdf?id=BkgWHnR5tm>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1909.02128.pdf>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1704.01665>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1810.10659>`_\n    | :authors:`Zhuwen Li, Qifeng Chen, Vladlen Koltun`\n    | :venue:`NeurIPS 2018`\n\n`Reinforcement Learning for Solving the Vehicle Routing Problem\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1802.04240>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1803.08475>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1802.03685>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1906.01227>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.10261.pdf>`_\n    | :authors:`Ryoma Sato, Makoto Yamada, Hisashi Kashima`\n    | :venue:`NeurIPS 2019`\n\n`Exact Combinatorial Optimization with Graph Convolutional Neural Networks\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1906.01629.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1910.07210.pdf>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1806.02371>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1805.07984>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1902.08412>`_\n    | :authors:`Daniel Zügner, Stephan Günnemann`\n    | :venue:`ICLR 2019`\n\n`Robust Graph Convolutional Networks Against Adversarial Attacks\n\u003Chttp:\u002F\u002Fpengcui.thumedialab.com\u002Fpapers\u002FRGCN.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1906.12269.pdf>`_\n    | :authors:`Daniel Zügner, Stephan Günnemann`\n    | :venue:`KDD 2019`\n\nGraph Matching\n-------------\n\n`REGAL: Representation Learning-based Graph Alignment\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1802.06257.pdf>`_\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:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FD18-1032.pdf>`_\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:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_ICCV_2019\u002Fpapers\u002FWang_Learning_Combinatorial_Embedding_Networks_for_Deep_Graph_Matching_ICCV_2019_paper.pdf>`_\n\t| :authors:`Runzhong Wang, Junchi Yan, Xiaokang Yang`\n\t| :venue:`ICCV 2019`\n\n`Deep Graph Matching Consensus\n\u003Chttps:\u002F\u002Fopenreview.net\u002Fpdf?id=HyeJf1HKvS>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1711.04043>`_\n    | :authors:`Victor Garcia, Joan Bruna`\n    | :venue:`ICLR 2018`\n\n`Learning Steady-States of Iterative Algorithms over Graphs\n\u003Chttp:\u002F\u002Fproceedings.mlr.press\u002Fv80\u002Fdai18a.html>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1909.05024.pdf>`_\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:\u002F\u002Fopenreview.net\u002Fforum?id=Bkeeca4Kvr>`_\n\t| :authors:`Jatin Chauhan, Deepak Nathani, Manohar Kaul`\n\t| :venue:`ICLR 2020`\n\n`Automated Relational Meta-learning\n\u003Chttps:\u002F\u002Fopenreview.net\u002Fpdf?id=rklp93EtwH>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1802.04687>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1905.11678>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1905.10769>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1905.08636>`_\n    | :authors:`Sébastien Lerique, Jacob Levy Abitbol, Márton Karsai`\n    | :venue:`arXiv 2019`\n\n`Variational Spectral Graph Convolutional Networks\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1906.01852>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1805.10002>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1905.12665>`_\n    | :authors:`Darwin Saire Pilco, Adín Ramírez Rivera`\n    | :venue:`ICML 2019 Workshop`\n\n`Learning Discrete Structures for Graph Neural Networks\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1903.11960>`_\n    | :authors:`Luca Franceschi, Mathias Niepert, Massimiliano Pontil, Xiao He`\n    | :venue:`ICML 2019`\n\n`Graphite: Iterative Generative Modeling of Graphs\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1803.10459>`_\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:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F7231-protein-interface-prediction-using-graph-convolutional-networks.pdf>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1802.00543>`_\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:\u002F\u002Facademic.oup.com\u002Fbioinformatics\u002Farticle-abstract\u002F35\u002F1\u002F104\u002F5047760?redirectedFrom=fulltext>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.13741.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.00534.pdf>`_\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:\u002F\u002Fwww.kdd.org\u002Fkdd2019\u002Faccepted-papers\u002Fview\u002Fgcn-mf-disease-gene-association-identification-by-graph-convolutional-netwo>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1903.04571.pdf>`_\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:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002Fbiorxiv\u002Fearly\u002F2019\u002F01\u002F28\u002F532226.full.pdf>`_\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:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8665584>`_\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:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3330912>`_\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:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-019-48391-y>`_\n    | :authors:`Diya Li, Jianxi Gao`\n    | :venue:`Nature 2019`\n\n`Directional Message Passing for Molecular Graphs\n\u003Chttps:\u002F\u002Fopenreview.net\u002Fpdf?id=B1eWbxStPH>`_\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:\u002F\u002Fopenreview.net\u002Fpdf?id=SkgKO0EtvS>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1709.09994>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1802.08773>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1803.00816>`_\n    | :authors:`Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, Stephan Günnemann`\n    | :venue:`ICML 2018`\n\n`Learning Deep Generative Models of Graphs\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1803.03324>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1802.04364>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1805.11973>`_\n    | :authors:`Nicola De Cao, Thomas Kipf`\n    | :venue:`arXiv 2018`\n\n`Generative Modeling for Protein Structures\n\u003Chttps:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F7978-generative-modeling-for-protein-structures.pdf>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1809.02630>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1806.02473>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1805.09076>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1812.01070>`_\n    | :authors:`Wengong Jin, Kevin Yang, Regina Barzilay, Tommi Jaakkola`\n    | :venue:`ICLR 2019`\n\n`Generative Code Modeling with Graphs\n\u003Chttps:\u002F\u002Fopenreview.net\u002Fforum?id=Bke4KsA5FX>`_\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:\u002F\u002Farxiv.org\u002Fabs\u002F1904.10098>`_\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:\u002F\u002Fproceedings.mlr.press\u002Fv89\u002Fsun19c.html>`_\n    | :authors:`Mingming Sun, Ping Li`\n    | :venue:`AISTATS 2019`\n\n`Graph Normalizing Flows\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1905.13177>`_\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:\u002F\u002Fjiyang3.web.engr.illinois.edu\u002Ffiles\u002Fcondgen.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1910.00760.pdf>`_\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:\u002F\u002Fopenreview.net\u002Fpdf?id=S1esMkHYPr>`_\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:\u002F\u002Fwww.jmlr.org\u002Fpapers\u002Fvolume9\u002Fvandermaaten08a\u002Fvandermaaten08a.pdf>`_\n    | :authors:`Laurens van der Maaten, Geoffrey Hinton`\n    | :venue:`JMLR 2008`\n\n`Visualizing non-metric similarities in multiple maps\n\u003Chttps:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs10994-011-5273-4.pdf>`_\n    | :authors:`Laurens van der Maaten, Geoffrey Hinton`\n    | :venue:`ML 2012`\n\n`Visualizing Large-scale and High-dimensional Data\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1602.00370>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1904.06915.pdf>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1903.00757>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1903.12287>`_\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:\u002F\u002Farxiv.org\u002Fpdf\u002F1902.08730>`_\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:\u002F\u002Fwww.dgl.ai>`_\n    | :authors:`DGL Team`\n\n`AmpliGraph\n\u003Chttps:\u002F\u002Fgithub.com\u002FAccenture\u002FAmpliGraph>`_\n    | :authors:`Luca Costabello, Sumit Pai, Chan Le Van, Rory McGrath, Nicholas McCarthy, Pedro Tabacof`\n\n`Euler\n\u003Chttps:\u002F\u002Fgithub.com\u002Falibaba\u002Feuler>`_\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:\u002F\u002Fwvvw.aaai.org\u002Fojs\u002Findex.php\u002FAAAI\u002Farticle\u002Fdownload\u002F4160\u002F4038>`_\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","图深度学习文献\n**********************\n\n这是一个关于图深度学习的论文列表。\n\n.. raw:: html\n\n    \u003Cdiv>\u003Ca href=\"README.rst\">按主题排序\u003C\u002Fa>\u003C\u002Fdiv>\n    \u003Cdiv>\u003Ca href=\"BYVENUE.rst\">按会议排序\u003C\u002Fa>\u003C\u002Fdiv>\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\n节点表示学习\n============\n\n无监督节点表示学习\n-------------------------\n\n`DeepWalk: 社交网络表示的在线学习\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1403.6652>`_\n    | :authors:`Bryan Perozzi, Rami Al-Rfou, Steven Skiena`\n    | :venue:`KDD 2014`\n    | :keywords:`节点分类, 随机游走, Skip-gram`\n\n`LINE: 大规模信息网络嵌入\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1503.03578>`_\n    | :authors:`Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, Qiaozhu Mei`\n    | :venue:`WWW 2015`\n    | :keywords:`一阶, 二阶, 节点分类`\n\n`GraRep: 利用全局结构信息学习图表示\n\u003Chttps:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=2806512>`_\n    | :authors:`Shaosheng Cao, Wei Lu, Qiongkai Xu`\n    | :venue:`CIKM 2015`\n    | :keywords:`高阶, SVD`\n\n`node2vec: 可扩展的网络特征学习\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1607.00653>`_\n    | :authors:`Aditya Grover, Jure Leskovec`\n    | :venue:`KDD 2016`\n    | :keywords:`广度优先搜索, 深度优先搜索, 节点分类, 链接预测`\n\n`变分图自编码器\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1611.07308>`_\n    | :authors:`Thomas N. Kipf, Max Welling`\n    | :venue:`arXiv 2016`\n\n`非对称邻近性的可扩展图嵌入\n\u003Chttps:\u002F\u002Faaai.org\u002Focs\u002Findex.php\u002FAAAI\u002FAAAI17\u002Fpaper\u002Fview\u002F14696>`_\n    | :authors:`Chang Zhou, Yuqiong Liu, Xiaofei Liu, Zhongyi Liu, Jun Gao`\n    | :venue:`AAAI 2017`\n\n`通过高阶邻近性近似快速增强网络嵌入\n\u003Chttps:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2017\u002F544>`_\n    | :authors:`Cheng Yang, Maosong Sun, Zhiyuan Liu, Cunchao Tu`\n    | :venue:`IJCAI 2017`\n\n`struc2vec: 从结构身份学习节点表示\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1704.03165>`_\n    | :authors:`Leonardo F. R. Ribeiro, Pedro H. P. Savarese, Daniel R. Figueiredo`\n    | :venue:`KDD 2017`\n    | :keywords:`结构身份`\n\n`用于学习层次化表示的庞加莱嵌入\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1705.08039>`_\n    | :authors:`Maximilian Nickel, Douwe Kiela`\n    | :venue:`NIPS 2017`\n\n`VERSE: 基于相似性度量的多功能图嵌入\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1803.04742>`_\n    | :authors:`Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Emmanuel Müller`\n    | :venue:`WWW 2018`\n\n`网络嵌入作为矩阵分解：统一DeepWalk、LINE、PTE和node2vec\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1710.02971>`_\n    | :authors:`Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, Jie Tang`\n    | :venue:`WSDM 2018`\n\n`通过扩散小波学习结构化节点嵌入\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1710.10321>`_\n    | :authors:`Claire Donnat, Marinka Zitnik, David Hallac, Jure Leskovec`\n    | :venue:`KDD 2018`\n\n`对抗性网络嵌入\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1711.07838>`_\n    | :authors:`Quanyu Dai, Qiang Li, Jian Tang, Dan Wang`\n    | :venue:`AAAI 2018`\n\n`GraphGAN: 基于生成对抗网络的图表示学习\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1711.08267>`_\n    | :authors:`Hongwei Wang, Jia Wang, Jialin Wang, Miao Zhao, Weinan Zhang, Fuzheng Zhang, Xing Xie, Minyi Guo`\n    | :venue:`AAAI 2018`\n\n`网络嵌入作为矩阵分解的一般视角\n\u003Chttps:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3291029>`_\n    | :authors:`Xin Liu, Tsuyoshi Murata, Kyoung-Sook Kim, Chatchawan Kotarasu, Chenyi Zhuang`\n    | :venue:`WSDM 2019`\n\n`深度图信息最大化\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1809.10341>`_\n    | :authors:`Petar Veličković, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, R Devon Hjelm`\n    | :venue:`ICLR 2019`\n\n`NetSMF: 大规模网络嵌入作为稀疏矩阵分解\n\u003Chttp:\u002F\u002Fkeg.cs.tsinghua.edu.cn\u002Fjietang\u002Fpublications\u002Fwww19-Qiu-et-al-NetSMF-Large-Scale-Network-Embedding.pdf>`_\n    | :authors:`Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Chi Wang, Kuansan Wang, Jie Tang`\n    | :venue:`WWW 2019`\n\n`用于网络嵌入的对抗训练方法\n\u003Chttps:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3313445>`_\n    | :authors:`Quanyu Dai, Xiao Shen, Liang Zhang, Qiang Li, Dan Wang`\n    | :venue:`WWW 2019`\n\n`vGraph: 一种用于联合社区发现和节点表示学习的生成模型\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1906.07159.pdf>`_\n    | :authors:`Fan-Yun Sun, Meng Qu, Jordan Hoffmann, Chin-Wei Huang, Jian Tang`\n    | :venue:`NeurIPS 2019`\n\n`ProGAN: 基于邻近性生成对抗网络的网络嵌入\n\u003Chttps:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3330866>`_\n    | :authors:`Hongchang Gao, Jian Pei, Heng Huang`\n    | :venue:`KDD 2019`\n\n`GraphZoom: 一种多级谱方法，用于实现准确且可扩展的图嵌入\n\u003Chttps:\u002F\u002Fopenreview.net\u002Fpdf?id=r1lGO0EKDH>`_\n    | :authors:`Chenhui Deng, Zhiqiang Zhao, Yongyu Wang, Zhiru Zhang, Zhuo Feng`\n    | :venue:`ICLR 2020`\n\n异构图中的节点表示学习\n------------------------\n\n`在异构社交网络中进行分类的节点潜在表示学习\n\u003Chttps:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=2556225>`_\n    | :authors:`Yann Jacob, Ludovic Denoyer, Patrick Gallinari`\n    | :venue:`WSDM 2014`\n\n`PTE: 通过大规模异构文本网络进行预测性文本嵌入\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1508.00200>`_\n    | :authors:`Jian Tang, Meng Qu, Qiaozhu Mei`\n    | :venue:`KDD 2015`\n    | :keywords:`文本嵌入, 异构文本图`\n\n`基于深度架构的异构网络嵌入\n\u003Chttps:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=2783296>`_\n    | :authors:`Shiyu Chang, Wei Han, Jiliang Tang, Guo-Jun Qi, Charu C. Aggarwal, Thomas S. Huang`\n    | :venue:`KDD 2015`\n\n`具有丰富文本信息的网络表示学习\n\u003Chttps:\u002F\u002Fwww.aaai.org\u002Focs\u002Findex.php\u002FIJCAI\u002FIJCAI15\u002Fpaper\u002Fview\u002F11098>`_\n    | :authors:`Cheng Yang, Zhiyuan Liu, Deli Zhao, Maosong Sun, Edward Chang`\n    | :venue:`AAAI 2015`\n\n`最大间隔DeepWalk: 网络表示的判别式学习\n\u003Chttps:\u002F\u002Fwww.ijcai.org\u002FProceedings\u002F16\u002FPapers\u002F547.pdf>`_\n    | :authors:`Cunchao Tu, Weicheng Zhang, Zhiyuan Liu, Maosong Sun`\n    | :venue:`IJCAI 2016`\n\n`metapath2vec: 异构网络的可扩展表示学习\n\u003Chttps:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3098036>`_\n    | :authors:`Yuxiao Dong, Nitesh V. Chawla, Ananthram Swami`\n    | :venue:`KDD 2017`\n\n`元路径引导的嵌入，用于大规模异构信息网络中的相似性搜索\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1610.09769>`_\n    | :authors:`Jingbo Shang, Meng Qu, Jialu Liu, Lance M. Kaplan, Jiawei Han, Jian Peng`\n    | :venue:`arXiv 2016`\n\n`HIN2Vec：在异构信息网络中探索元路径以进行表示学习\n\u003Chttps:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3132953>`_\n    | :authors:`傅涛阳、李王健、雷震`\n    | :venue:`CIKM 2017`\n\n`基于注意力的多视图网络表示学习协作框架\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1709.06636>`_\n    | :authors:`Qu Meng、Tang Jian、Shang Jingbo、Ren Xiang、Zhang Ming、Han Jiawei`\n    | :venue:`CIKM 2017`\n\n`用于连接组分析的图嵌入多视图聚类\n\u003Chttps:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3132909>`_\n    | :authors:`Ma Guixiang、He Lifang、Lu Chun-Ta、Shao Weixiang、Yu Philip S.、Leow Alex D.、Ragin Ann B.`\n    | :venue:`CIKM 2017`\n\n`属性有向网络嵌入\n\u003Chttps:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3132847.3132905>`_\n    | :authors:`Wang Suhang、Aggarwal Charu、Tang Jiliang、Liu Huan`\n    | :venue:`CIKM 2017`\n\n`CANE：用于关系建模的上下文感知网络嵌入\n\u003Chttps:\u002F\u002Faclweb.org\u002Fanthology\u002Fpapers\u002FP\u002FP17\u002FP17-1158\u002F>`_\n    | :authors:`Tu Cunchao、Liu Han、Liu Zhiyuan、Sun Maosong`\n    | :venue:`ACL 2017`\n\n`PME：用于链接预测的异构网络上的投影度量嵌入\n\u003Chttps:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3219986>`_\n    | :authors:`Chen Hongxu、Yin Hongzhi、Wang Weiqing、Wang Hao、Nguyen Quoc Viet Hung、Li Xue`\n    | :venue:`KDD 2018`\n\n`BiNE：二分网络嵌入\n\u003Chttps:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3209978.3209987>`_\n    | :authors:`Gao Ming、Chen Leihui、He Xiangnan、Zhou Aoying`\n    | :venue:`SIGIR 2018`\n\n`StarSpace：嵌入所有事物\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1709.03856>`_\n    | :authors:`Wu Ledell、Fisch Adam、Chopra Sumit、Adams Keith、Bordes Antoine、Weston Jason`\n    | :venue:`AAAI 2018`\n\n`探索专家认知以进行属性网络嵌入\n\u003Chttps:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3159655>`_\n    | :authors:`Huang Xiao、Song Qingquan、Li Jundong、Hu Xia`\n    | :venue:`WSDM 2018`\n\n`SHINE：用于情感链接预测的有向异构信息网络嵌入\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1712.00732>`_\n    | :authors:`Wang Hongwei、Zhang Fuzheng、Hou Min、Xie Xing、Guo Minyi、Liu Qi`\n    | :venue:`WSDM 2018`\n\n`具有层次结构的多维网络嵌入\n\u003Chttps:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3159680>`_\n    | :authors:`Ma Yao、Ren Zhaochun、Jiang Ziheng、Tang Jiliang、Yin Dawei`\n    | :venue:`WSDM 2018`\n\n`通过深度强化学习进行异构星型网络嵌入的课程学习\n\u003Chttps:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3159711>`_\n    | :authors:`Qu Meng、Tang Jian、Han Jiawei`\n    | :venue:`WSDM 2018`\n\n`基于生成对抗网络的异构书目网络表示，用于个性化引用推荐\n\u003Chttps:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FGenerative-Adversarial-Network-Based-Heterogeneous-Cai-Han\u002F1596d6487012696ba400fb69904a2c372a08a2be>`_\n    | :authors:`Cai Xiaoyan、Han Junwei、Yang Libin`\n    | :venue:`AAAI 2018`\n\n`ANRL：通过深度神经网络进行属性网络表示学习\n\u003Chttps:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2018\u002F438>`_\n    | :authors:`Zhang Zhen、Yang Hongxia、Bu Jiajun、Zhou Sheng、Yu Pinggang、Zhang Jianwei、Ester Martin、Wang Can`\n    | :venue:`IJCAI 2018`\n\n`通过递归随机哈希实现高效的属性网络嵌入\n\u003Chttps:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2018\u002F397>`_\n    | :authors:`Wu Wei、Li Bin、Chen Ling、Zhang Chengqi`\n    | :venue:`IJCAI 2018`\n\n`深度属性网络嵌入\n\u003Chttps:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2018\u002F467>`_\n    | :authors:`Gao Hongchang、Huang Heng`\n    | :venue:`IJCAI 2018`\n\n`协同正则化的深度多网络嵌入\n\u003Chttps:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3186113>`_\n    | :authors:`Ni Jingchao、Chang Shiyu、Liu Xiao、Cheng Wei、Chen Haifeng、Xu Dongkuan、Zhang Xiang`\n    | :venue:`WWW 2018`\n\n`通过全面转录异构信息网络来简化嵌入学习\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1807.03490>`_\n    | :authors:`Shi Yu、Zhu Qi、Guo Fang、Zhang Chao、Han Jiawei`\n    | :venue:`KDD 2018`\n\n`基于元图的HIN谱嵌入：方法、分析与见解\n\u003Chttps:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FMeta-Graph-Based-HIN-Spectral-Embedding%3A-Methods%2C-Yang-Feng\u002F4d5f4d6785d550383e3f3afb04c3015bf0d28405>`_\n    | :authors:`Yang Carl、Feng Yichen、Li Pan、Shi Yu、Han Jiawei`\n    | :venue:`ICDM 2018`\n\n`SIDE：有向有符号网络中的表示学习\n\u003Chttps:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3186117>`_\n    | :authors:`Kim Junghwan、Park Haekyu、Lee Ji-Eun、U Kang`\n    | :venue:`WWW 2018`\n\n`用于内容丰富网络嵌入的网络到网络模型学习\n\u003Chttps:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3330924>`_\n    | :authors:`He Zhicheng、Liu Jie、Li Na、Huang Yalou`\n    | :venue:`KDD 2019`\n\n动态图中的节点表示学习\n----------------------------------------------\n\n`Know-evolve：用于动态知识图谱的深度时间推理\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1705.05742.pdf>`_\n    | :authors:`Trivedi Rakshit、Dai Hanjun、Wang Yichen、Song Le`\n    | :venue:`ICML 2017`\n\n`Dyngem：用于动态图的深度嵌入方法\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1805.11273.pdf>`_\n    | :authors:`Goyal Palash、Kamra Nitin、He Xinran、Liu Yan`\n    | :venue:`ICLR 2017研讨会`\n\n`用于动态环境中学习的属性网络嵌入\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1706.01860.pdf>`_\n    | :authors:`Li Jundong、Dani Harsh、Hu Xia、Tang Jiliang、Chang Yi、Liu Huan`\n    | :venue:`CIKM 2017`\n\n`通过建模三元闭包过程进行动态网络嵌入\n\u003Chttp:\u002F\u002Fyangy.org\u002Fworks\u002Fdynamictriad\u002Fdynamic_triad.pdf>`_\n    | :authors:`Zhou Lekui、Yang Yang、Ren Xiang、Wu Fei、Zhuang Yueting`\n    | :venue:`AAAI 2018`\n\n`DepthLGP：在动态网络中学习样本外节点的嵌入\n\u003Chttps:\u002F\u002Fpdfs.semanticscholar.org\u002F9499\u002Fb38866b1eb87ae43fa5be02f9d08cd3c20a8.pdf?_ga=2.6780794.935636364.1561139530-1831876308.1523264869>`_\n    | :authors:`Ma Jianxin、Cui Peng、Zhu Wenwu`\n    | :venue:`AAAI 2018`\n\n`TIMERS：在动态网络上进行误差受限的SVD重启\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1711.09541.pdf>`_\n    | :authors:`Zhang Ziwei、Cui Peng、Pei Jian、Wang Xiao、Zhu Wenwu`\n    | :venue:`AAAI 2018`\n\n`Twitter中用于用户画像的动态嵌入\n\u003Chttps:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3219819.3220043>`_\n    | :authors:`Liang Shangsong、Zhang Xiangliang、Ren Zhaochun、Kanoulas Evangelos`\n    | :venue:`KDD 2018`\n\n`动态网络嵌入：一种基于Skip-gram的网络嵌入扩展方法\n\u003Chttps:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2018\u002F0288.pdf>`_\n    | :authors:`Du Lun、Wang Yun、Song Guojie、Lu Zhicong、Wang Junshan`\n    | :venue:`IJCAI 2018`\n\n`DyRep：在动态图上学习表示\n\u003Chttps:\u002F\u002Fopenreview.net\u002Fpdf?id=HyePrhR5KX>`_\n    | :authors:`Trivedi Rakshit、Farajtabar Mehrdad、Biswal Prasenjeet、Zha Hongyuan`\n    | :venue:`ICLR 2019`\n\n`预测时序交互网络中的动态嵌入轨迹\n\u003Chttps:\u002F\u002Fcs.stanford.edu\u002F~srijan\u002Fpubs\u002Fjodie-kdd2019.pdf>`_\n    | :authors:`Srijan Kumar, Xikun Zhang, Jure Leskovec`\n    | :venue:`KDD 2019`\n\n`变分图递归神经网络\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1908.09710.pdf>`_\n    | :authors:`Ehsan Hajiramezanali, Arman Hasanzadeh, Nick Duffield, Krishna R Narayanan, Mingyuan Zhou, Xiaoning Qian`\n    | :venue:`NeurIPS 2019`\n\n`Social-BiGAT：基于Bicycle-GAN和图注意力网络的多模态轨迹预测\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1907.03395.pdf>`_\n    | :authors:`Vineet Kosaraju, Amir Sadeghian, Roberto Martín-Martín, Ian Reid, S. Hamid Rezatofighi, Silvio Savarese`\n    | :venue:`NeurIPS 2019`\n\n知识图谱嵌入\n=========================\n\n`一种用于多关系数据集体学习的三路模型。\n\u003Chttp:\u002F\u002Fwww.icml-2011.org\u002Fpapers\u002F438_icmlpaper.pdf>`_\n    | :authors:`Maximilian Nickel, Volker Tresp, Hans-Peter Kriegel`\n    | :venue:`ICML 2011`\n\n`通过平移嵌入建模多关系数据\n\u003Chttps:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F5071-translating-embeddings-for-modeling-multi-relational-data.pdf>`_\n    | :authors:`Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, Oksana Yakhnenko`\n    | :venue:`NIPS 2013`\n\n`通过超平面平移进行知识图谱嵌入\n\u003Chttps:\u002F\u002Fwww.aaai.org\u002Focs\u002Findex.php\u002FAAAI\u002FAAAI14\u002Fpaper\u002FviewFile\u002F8531\u002F8546>`_\n    | :authors:`Zhen Wang, Jianwen Zhang, Jianlin Feng, Zheng Chen`\n    | :venue:`AAAI 2014`\n\n`通过引入可观测模式降低关系分解模型的秩\n\u003Chttp:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F5448-reducing-the-rank-in-relational-factorization-models-by-including-observable-patterns.pdf>`_\n    | :authors:`Maximilian Nickel, Xueyan Jiang, Volker Tresp`\n    | :venue:`NIPS 2014`\n\n`学习实体和关系嵌入以完成知识图谱\n\u003Chttps:\u002F\u002Fwww.aaai.org\u002Focs\u002Findex.php\u002FAAAI\u002FAAAI15\u002Fpaper\u002FviewFile\u002F9571\u002F9523>`_\n    | :authors:`Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, Xuan Zhu`\n    | :venue:`AAAI 2015`\n\n`知识图谱关系机器学习综述\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1503.00759.pdf>`_\n    | :authors:`Maximilian Nickel, Kevin Murphy, Volker Tresp, Evgeniy Gabrilovich`\n    | :venue:`IEEE 2015`\n\n`通过动态映射矩阵进行知识图谱嵌入\n\u003Chttps:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FP15-1067>`_\n    | :authors:`Guoliang Ji, Shizhu He, Liheng Xu, Kang Liu, Jun Zha`\n    | :venue:`ACL 2015`\n\n`为知识库表示学习建模关系路径\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1506.00379>`_\n    | :authors:`Yankai Lin, Zhiyuan Liu, Huanbo Luan, Maosong Sun, Siwei Rao, Song Liu`\n    | :venue:`EMNLP 2015`\n\n`为知识库的学习与推理嵌入实体和关系\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1412.6575>`_\n    | :authors:`Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, Li Deng`\n    | :venue:`ICLR 2015`\n\n`知识图谱的全息嵌入\n\u003Chttps:\u002F\u002Fwww.aaai.org\u002Focs\u002Findex.php\u002FAAAI\u002FAAAI16\u002Fpaper\u002FviewPDFInterstitial\u002F12484\u002F11828>`_\n    | :authors:`Maximilian Nickel, Lorenzo Rosasco, Tomaso Poggio`\n    | :venue:`AAAI 2016`\n\n`复杂嵌入实现简单链接预测\n\u003Chttp:\u002F\u002Fwww.jmlr.org\u002Fproceedings\u002Fpapers\u002Fv48\u002Ftrouillon16.pdf>`_\n    | :authors:`Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, Guillaume Bouchard`\n    | :venue:`ICML 2016`\n\n`用图卷积网络建模关系数据\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1703.06103>`_\n    | :authors:`Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, Max Welling`\n    | :venue:`arXiv 2017`\n\n`知识图谱嵌入的快速线性模型\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1710.10881>`_\n    | :authors:`Armand Joulin, Edouard Grave, Piotr Bojanowski, Maximilian Nickel, Tomas Mikolov`\n    | :venue:`arXiv 2017`\n\n`卷积2D知识图谱嵌入\n\u003Chttps:\u002F\u002Fwww.aaai.org\u002Focs\u002Findex.php\u002FAAAI\u002FAAAI18\u002Fpaper\u002Fdownload\u002F17366\u002F15884>`_\n    | :authors:`Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, Sebastian Riedel`\n    | :venue:`AAAI 2018`\n\n`基于软规则迭代指导的知识图谱嵌入\n\u003Chttps:\u002F\u002Fwww.aaai.org\u002Focs\u002Findex.php\u002FAAAI\u002FAAAI18\u002Fpaper\u002Fdownload\u002F16369\u002F16011>`_\n    | :authors:`Shu Guo, Quan Wang, Lihong Wang, Bin Wang, Li Guo`\n    | :venue:`AAAI 2018`\n\n`KBGAN：知识图谱嵌入的对抗学习\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1711.04071>`_\n    | :authors:`Liwei Cai, William Yang Wang`\n    | :venue:`NAACL 2018`\n\n`利用简单约束改进知识图谱嵌入\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1805.02408>`_\n    | :authors:`Boyang Ding, Quan Wang, Bin Wang, Li Guo`\n    | :venue:`ACL 2018`\n\n`SimplE嵌入用于知识图谱中的链接预测\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1802.04868>`_\n    | :authors:`Seyed Mehran Kazemi, David Poole`\n    | :venue:`NeurIPS 2018`\n\n`基于卷积神经网络的知识库补全新型嵌入模型\n\u003Chttps:\u002F\u002Faclweb.org\u002Fanthology\u002Fpapers\u002FN\u002FN18\u002FN18-2053\u002F>`_\n    | :authors:`Dai Quoc Nguyen, Tu Dinh Nguyen, Dat Quoc Nguyen, Dinh Phung`\n    | :venue:`NAACL 2018`\n\n`迭代学习知识图谱推理的嵌入与规则\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1903.08948>`_\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：在复数空间中通过关系旋转进行知识图谱嵌入\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1902.10197>`_\n    | :authors:`Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, Jian Tang`\n    | :venue:`ICLR 2019`\n\n`学习基于注意力的关系预测嵌入\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1906.01195>`_\n    | :authors:`Deepak Nathani, Jatin Chauhan, Charu Sharma, Manohar Kaul`\n    | :venue:`ACL 2019`\n\n`用于推理的概率逻辑神经网络\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1906.08495.pdf>`_\n    | :authors:`Meng Qu, Jian Tang`\n    | :venue:`NeurIPS 2019`\n\n`四元数知识图谱嵌入\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1904.10281.pdf>`_\n    | :authors:`Shuai Zhang, Yi Tay, Lina Yao, Qi Liu`\n    | :venue:`NeurIPS 2019`\n\n`用于推理的知识量子嵌入\n\u003Chttps:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F8797-quantum-embedding-of-knowledge-for-reasoning.pdf>`_\n    | :authors:`Dinesh Garg, Santosh K. Srivastava, Hima Karanam`\n    | :venue:`NeurIPS 2019`\n\n`多关系庞加莱图嵌入\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.09791.pdf>`_\n    | :authors:`Ivana Balaževic, Carl Allen, Timothy Hospedales`\n    | :venue:`NeurIPS 2019`\n\n`用于大规模知识图谱推理的动态剪枝消息传递网络\n\u003Chttps:\u002F\u002Fopenreview.net\u002Fforum?id=rkeuAhVKvB>`_\n    | :authors:`Xiaoran Xu, Wei Feng, Yunsheng Jiang, Xiaohui Xie, Zhiqing Sun, Zhi-Hong Deng`\n    | :venue:`ICLR 2020`\n\n图神经网络\n=====================\n\n`重新审视基于图嵌入的半监督学习\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1603.08861>`_\n    | :authors:`Zhilin Yang, William W. Cohen, Ruslan Salakhutdinov`\n    | :venue:`ICML 2016`\n\n`图卷积网络的半监督分类\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1609.02907>`_\n    | :authors:`托马斯·N·基普夫，马克·韦林`\n    | :venue:`ICLR 2017`\n\n`用于量子化学的神经消息传递\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1704.01212>`_\n    | :authors:`贾斯汀·吉尔默，塞缪尔·S·舍恩霍尔茨，帕特里克·F·莱利，奥里奥尔·维尼亚尔斯，乔治·E·达尔`\n    | :venue:`ICML 2017`\n\n`基于模体感知的图嵌入\n\u003Chttp:\u002F\u002Fgearons.org\u002Fassets\u002Fdocs\u002Fmotif-aware-graph-final.pdf>`_\n    | :authors:`黄阮，村田刚`\n    | :venue:`IJCAI 2017`\n\n`基于嵌入传播的图表示学习\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1710.03059>`_\n    | :authors:`阿尔贝托·加西亚-杜兰，马蒂亚斯·尼珀特`\n    | :venue:`NIPS 2017`\n\n`大规模图上的归纳式表示学习\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1706.02216>`_\n    | :authors:`威廉·L·汉密尔顿，雷克斯·英，朱雷·莱斯科韦克`\n    | :venue:`NIPS 2017`\n\n`图注意力网络\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1710.10903>`_\n    | :authors:`彼得·韦利奇科维奇，吉列姆·库库鲁尔，阿兰查·卡萨诺瓦，阿德里亚娜·罗梅罗，皮耶特罗·利奥，约书亚·本吉奥`\n    | :venue:`ICLR 2018`\n\n`FastGCN：通过重要性采样实现的图卷积网络快速学习\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1801.10247>`_\n    | :authors:`陈杰，马腾飞，肖操`\n    | :venue:`ICLR 2018`\n\n`基于跳跃知识网络的图表示学习\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1806.03536>`_\n    | :authors:`许凯儒，李成涛，田永龙，园部智弘，川原端一，施特法妮·耶格尔卡`\n    | :venue:`ICML 2018`\n\n`具有方差缩减的图卷积网络随机训练\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1710.10568>`_\n    | :authors:`陈建飞，朱俊，宋乐`\n    | :venue:`ICML 2018`\n\n`大规模可学习的图卷积网络\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1808.03965>`_\n    | :authors:`高洪洋，王正阳，季水旺`\n    | :venue:`KDD 2018`\n\n`面向快速图表示学习的自适应采样\n\u003Chttps:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F7707-adaptive-sampling-towards-fast-graph-representation-learning.pdf>`_\n    | :authors:`黄文兵，张彤，荣宇，黄俊洲`\n    | :venue:`NeurIPS 2018`\n\n`基于可微池化的层次化图表示学习\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1806.08804>`_\n    | :authors:`雷克斯·英，尤佳轩，克里斯托弗·莫里斯，任翔，威廉·L·汉密尔顿，朱雷·莱斯科韦克`\n    | :venue:`NeurIPS 2018`\n\n`基于图高斯过程的贝叶斯半监督学习\n\u003Chttps:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F7440-bayesian-semi-supervised-learning-with-graph-gaussian-processes.pdf>`_\n    | :authors:`吴银雪，尼古洛·科隆博，里卡多·席尔瓦`\n    | :venue:`NeurIPS 2018`\n\n`图神经网络评估中的陷阱\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1811.05868>`_\n    | :authors:`奥列克桑德尔·什丘尔，马克西米利安·穆梅，亚历山大·博伊切夫斯基，施特凡·居内曼`\n    | :venue:`arXiv 2018`\n\n`异质图注意力网络\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1903.07293>`_\n    | :authors:`王晓，姬厚业，石川，王白，崔鹏，P·于，叶燕芳`\n    | :venue:`WWW 2019`\n\n`用于半监督分类的贝叶斯图卷积神经网络\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1811.11103.pdf>`_\n    | :authors:`张颖雪，索米亚桑达尔·帕尔，马克·科茨，丹尼斯·于斯特拜`\n    | :venue:`AAAI 2019`\n\n`图神经网络有多强大？\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1810.00826>`_\n    | :authors:`许凯儒，胡伟华，朱雷·莱斯科韦克，施特法妮·耶格尔卡`\n    | :venue:`ICLR 2019`\n\n`LanczosNet：多尺度深度图卷积网络\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1901.01484>`_\n    | :authors:`廖仁杰，赵志珍，拉奎尔·乌尔塔孙，理查德·S·泽梅尔`\n    | :venue:`ICLR 2019`\n\n`图小波神经网络\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1904.07785>`_\n    | :authors:`徐冰冰，沈华为，曹琪，邱云启，程雪齐`\n    | :venue:`ICLR 2019`\n\n`利用线图神经网络进行有监督社区检测\n\u003Chttps:\u002F\u002Fopenreview.net\u002Fpdf?id=H1g0Z3A9Fm>`_\n    | :authors:`陈正道，李翔，琼·布鲁纳`\n    | :venue:`ICLR 2019`\n\n`先预测再传播：图神经网络与个性化PageRank的结合\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1810.05997>`_\n    | :authors:`约翰内斯·克利佩拉，亚历山大·博伊切夫斯基，施特凡·居内曼`\n    | :venue:`ICLR 2019`\n\n`不变性和等变图网络\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1812.09902>`_\n    | :authors:`哈盖·马龙，赫利·本-哈穆，纳达夫·沙米尔，亚伦·利普曼`\n    | :venue:`ICLR 2019`\n\n`胶囊图神经网络\n\u003Chttps:\u002F\u002Fopenreview.net\u002Fpdf?id=Byl8BnRcYm>`_\n    | :authors:`张欣怡，陈丽慧`\n    | :venue:`ICLR 2019`\n\n`MixHop：通过稀疏化邻域混合实现的高阶图卷积架构\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.00067>`_\n    | :authors:`萨米·阿布-埃尔-海贾，布莱恩·佩罗齐，阿莫尔·卡普尔，纳扎宁·阿里普尔法德，克里斯蒂娜·勒曼，赫赖尔·哈鲁秋扬，格雷格·韦尔·斯蒂格，阿拉姆·加尔斯蒂安`\n    | :venue:`ICML 2019`\n\n`图U型网络\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.05178>`_\n    | :authors:`高洪洋，季水旺`\n    | :venue:`ICML 2019`\n\n`解耦图卷积网络\n\u003Chttp:\u002F\u002Fproceedings.mlr.press\u002Fv97\u002Fma19a\u002Fma19a.pdf>`_\n    | :authors:`马建新，崔鹏，匡坤，王鑫，朱文武`\n    | :venue:`ICML 2019`\n\n`GMNN：图马尔可夫神经网络\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.06214>`_\n    | :authors:`孟曲，约书亚·本吉奥，唐健`\n    | :venue:`ICML 2019`\n\n`简化图卷积网络\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1902.07153>`_\n    | :authors:`菲利克斯·吴，张天义，阿毛里·霍兰达·德·索萨二世，克里斯托弗·五十，陶宇，基利安·Q·温伯格`\n    | :venue:`ICML 2019`\n\n`位置感知图神经网络\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1906.04817>`_\n    | :authors:`尤佳轩，雷克斯·英，朱雷·莱斯科韦克`\n    | :venue:`ICML 2019`\n\n`自注意力图池化\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1904.08082>`_\n    | :authors:`李俊贤，李仁烨，姜在宇`\n    | :venue:`ICML 2019`\n\n`用于图表示的关联池化\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1903.02541>`_\n    | :authors:`瑞安·L·墨菲，巴拉苏布拉马尼安·斯里尼瓦桑，维纳亚克·拉奥，布鲁诺·里贝罗`\n    | :venue:`ICML 2019`\n\n`通过硬注意力和通道注意力网络进行图表示学习\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1907.04652.pdf>`_\n    | :authors:`高洪洋，季水旺`\n    | :venue:`KDD 2019`\n\n`条件随机场增强的图卷积神经网络\n\u003Chttps:\u002F\u002Fwww.kdd.org\u002Fkdd2019\u002Faccepted-papers\u002Fview\u002Fconditional-random-field-enhanced-graph-convolutional-neural-networks>`_\n    | :authors:`高洪昌，裴坚，黄恒`\n    | :venue:`KDD 2019`\n\n`Cluster-GCN：一种高效训练深度且大型图卷积网络的算法\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1905.07953>`_\n    | :authors:`蒋伟林，刘宣庆，司司，李阳，萨米·本吉奥，谢长睿`\n    | :venue:`KDD 2019`\n\n`DEMO-Net：用于节点和图分类的度特定图神经网络\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1906.02319>`_\n    | :authors:`吴军，何静睿，徐继军`\n    | :venue:`KDD 2019`\n\n`HetGNN：异构图神经网络\n\u003Chttps:\u002F\u002Fwww.kdd.org\u002Fkdd2019\u002Faccepted-papers\u002Fview\u002Fhetgnn-heterogeneous-graph-neural-network>`_\n    | :authors:`张楚旭、宋东进、黄超、阿南特拉姆·斯瓦米、尼特什·V·乔拉`\n    | :venue:`KDD 2019`\n\n`基于属性随机游走的图递归网络\n\u003Chttps:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3292500.3330941>`_\n    | :authors:`黄晓、宋庆泉、李宇宁、胡霞`\n    | :venue:`KDD 2019`\n\n`带有特征池化的图卷积网络\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1904.13107>`_\n    | :authors:`马瑶、王苏航、查鲁·阿加瓦尔、唐继亮`\n    | :venue:`KDD 2019`\n\n`DFNets：用于具有反馈环滤波器的图的谱卷积神经网络\n\u003Chttp:\u002F\u002Fusers.cecs.anu.edu.au\u002F~u5170295\u002Fpapers\u002Fnips-wijesinghe-2019.pdf>`_\n    | :authors:`阿西里·维杰辛格、王青`\n    | :venue:`NeurIPS 2019`\n\n`理解图神经网络在学习图拓扑结构方面的表示能力\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1907.05008.pdf>`_\n    | :authors:`尼玛·德赫马米、阿尔伯特-拉斯洛·巴拉巴希、罗斯·余`\n    | :venue:`NeurIPS 2019`\n\n`面向基于图的半监督学习的灵活生成式框架\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.10769.pdf>`_\n    | :authors:`马佳琪、汤伟晶、朱基、梅巧珍`\n    | :venue:`NeurIPS 2019`\n\n`重新思考用于图上节点表示学习的核方法\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1910.02548.pdf>`_\n    | :authors:`田宇、赵龙、彭曦、迪米特里斯·N·梅塔克萨斯`\n    | :venue:`NeurIPS 2019`\n\n`突破天花板：更强大的多尺度深度图卷积网络\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1906.02174.pdf>`_\n    | :authors:`栾思涛、赵明德、常晓文、多伊娜·普雷库普`\n    | :venue:`NeurIPS 2019`\n\n`N元组图：一种简单的无监督分子表示方法\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1806.09206.pdf>`_\n    | :authors:`刘盛超、切瓦·钱德伦、梁英宇`\n    | :venue:`NeurIPS 2019`\n\n`DeepGCNs：GCN能否像CNN一样深？\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1904.03751.pdf>`_\n    | :authors:`李国豪、马蒂亚斯·穆勒、阿里·塔贝特、伯纳德·加内姆`\n    | :venue:`ICCV 2019`\n\n`连续图神经网络\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1912.00967.pdf>`_\n    | :authors:`路易斯-帕斯卡尔·A·C·克索纽、孟曲、唐健`\n    | :venue:`arXiv 2019`\n\n`曲率图网络\n\u003Chttps:\u002F\u002Fopenreview.net\u002Fpdf?id=BylEqnVFDB>`_\n\t| :authors:`叶泽、刘锦森、马腾飞、高杰、陈超`\n\t| :venue:`ICLR 2020`\n\n`基于记忆的图网络\n\u003Chttps:\u002F\u002Fopenreview.net\u002Fpdf?id=r1laNeBYPB>`_\n\t| :authors:`阿米尔·侯赛因·卡萨哈马迪、卡韦·哈萨尼、帕尔萨·莫拉迪、李Leo、莫里斯·奎德`\n\t| :venue:`ICLR 2020`\n\t\n`图神经网络预训练策略\n\u003Chttps:\u002F\u002Fopenreview.net\u002Fpdf?id=HJlWWJSFDH>`_\n\t| :authors:`胡伟华、刘博文、约瑟夫·戈梅斯、马林卡·齐特尼克、珀西·梁、维杰·潘德、朱雷·莱斯科韦茨`\n\t| :venue:`ICLR 2020`\n\n图深度学习的应用\n=================================\n\n自然语言处理\n---------------------------\n\n`利用图卷积网络对句子进行编码以进行语义角色标注\n\u003Chttps:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FD17-1159>`_\n    | :authors:`迭戈·马尔切贾尼、伊万·季托夫`\n    | :venue:`EMNLP 2017`\n\n`面向句法感知的神经机器翻译的图卷积编码器\n\u003Chttps:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FD17-1209>`_\n    | :authors:`约斯特·巴斯廷斯、伊万·季托夫、维尔克·阿齐兹、迭戈·马尔切贾尼、哈利勒·西马安`\n    | :venue:`EMNLP 2017`\n\n`基于图的神经多文档摘要\n\u003Chttps:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FK17-1045>`_\n    | :authors:`米奇弘·安永、张睿、克希提吉·米卢、阿尤什·帕里克、克里希南·斯里尼瓦桑、德拉戈米尔·拉德夫`\n    | :venue:`CoNLL 2017`\n\n`QANet：将局部卷积与全局自注意力相结合用于阅读理解\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1804.09541.pdf>`_\n    | :authors:`亚当斯·魏宇、大卫·多汉、明泰·隆、赵睿、陈凯、穆罕默德·诺鲁齐、郭文·乐`\n    | :venue:`ICLR 2018`\n\n`一种结构化的自注意力句子嵌入\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1703.03130.pdf>`_\n    | :authors:`林周涵、冯敏伟、西塞罗·诺盖拉·多斯·桑托斯、于墨、向兵、周博文、约书亚·本吉奥`\n    | :venue:`ICLR 2018`\n\n`利用门控图神经网络建模语义以进行知识库问答\n\u003Chttps:\u002F\u002Faclweb.org\u002Fanthology\u002FC18-1280>`_\n    | :authors:`丹尼尔·索罗金、伊琳娜·古列维奇`\n    | :venue:`COLING 2018`\n\n`利用图卷积网络在神经机器翻译中挖掘语义\n\u003Chttps:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FN18-2078>`_\n    | :authors:`迭戈·马尔切贾尼、约斯特·巴斯廷斯、伊万·季托夫`\n    | :venue:`NAACL 2018`\n\n`面向语义角色标注的语言学启发式自注意力\n\u003Chttps:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FD18-1548>`_\n    | :authors:`艾玛·斯特鲁贝尔、帕特里克·维尔加、丹尼尔·安多尔、大卫·魏斯、安德鲁·麦考勒姆`\n    | :venue:`EMNLP 2018`\n\n`在修剪后的依存树上进行图卷积可提升关系抽取效果\n\u003Chttps:\u002F\u002Faclweb.org\u002Fanthology\u002FD18-1244>`_\n    | :authors:`张宇浩、齐鹏、克里斯托弗·D·曼宁`\n    | :venue:`EMNLP 2018`\n\n`用于AMR到文本生成的图到序列模型\n\u003Chttps:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FP18-1150>`_\n    | :authors:`宋林峰、张悦、王志国、丹尼尔·吉尔迪亚`\n    | :venue:`ACL 2018`\n\n`利用门控图神经网络进行图到序列学习\n\u003Chttps:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FP18-1026>`_\n    | :authors:`丹尼尔·贝克、戈拉姆雷扎·哈法里、特雷弗·科恩`\n    | :venue:`ACL 2018`\n\n`用于文本分类的图卷积网络\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1809.05679.pdf>`_\n    | :authors:`姚亮、毛成生、罗源`\n    | :venue:`AAAI 2019`\n\n`可微扰并解析：基于结构化变分自编码器的半监督句法分析\n\u003Chttps:\u002F\u002Fopenreview.net\u002Fpdf?id=BJlgNh0qKQ>`_\n    | :authors:`凯奥·科罗、伊万·季托夫`\n    | :venue:`ICLR 2019`\n\n`结构化神经摘要\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1811.01824.pdf>`_\n    | :authors:`帕特里克·费尔南德斯、米尔蒂阿迪斯·阿拉马尼斯、马克·布罗克斯米德`\n    | :venue:`ICLR 2019`\n\n`基于图结构的多任务学习\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1811.10211.pdf>`_\n    | :authors:`刘鹏飞、傅杰、董悦、邱锡鹏、张嘉琪`\n    | :venue:`AAAI 2019`\n\n`施加标签关系归纳偏置以实现极细粒度实体类型标注\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1903.02591.pdf>`_\n    | :authors:`熊文翰、吴嘉伟、雷德仁、于墨、常世宇、郭晓晓、威廉·杨·王`\n    | :venue:`NAACL 2019`\n\n`单文档摘要作为树形结构推导\n\u003Chttps:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FN19-1173>`_\n    | :authors:`刘洋、伊万·季托夫、米雷拉·拉帕塔`\n    | :venue:`NAACL 2019`\n\n`通过知识图嵌入和图卷积网络进行长尾关系抽取\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1903.01306.pdf>`_\n    | :authors:`张宁宇、邓淑敏、孙占林、王冠英、陈曦、张伟、陈华军`\n    | :venue:`NAACL 2019`\n\n`基于生成参数的图神经网络用于关系抽取\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1902.00756.pdf>`_\n    | :authors:`Hao Zhu, Yankai Lin, Zhiyuan Liu, Jie Fu, Tat-seng Chua, Maosong Sun`\n    | :venue:`ACL 2019`\n\n`用于多跳推理的动态融合图网络\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.06933.pdf>`_\n    | :authors:`Yunxuan Xiao, Yanru Qu, Lin Qiu, Hao Zhou, Lei Li, Weinan Zhang, Yong Yu`\n    | :venue:`ACL 2019`\n\n`利用图卷积网络编码社交信息以检测新闻媒体中的政治立场\n\u003Chttps:\u002F\u002Fwww.cs.purdue.edu\u002Fhomes\u002Fdgoldwas\u002F\u002Fdownloads\u002Fpapers\u002FLiG_acl_2019.pdf>`_\n    | :authors:`Chang Li, Dan Goldwasser`\n    | :venue:`ACL 2019`\n\n`用于关系抽取的注意力引导图卷积网络\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1906.07510.pdf>`_\n    | :authors:`Zhijiang Guo, Yan Zhang, Wei Lu`\n    | :venue:`ACL 2019`\n\n`利用图卷积网络在词嵌入中融入句法和语义信息\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1809.04283.pdf>`_\n    | :authors:`Shikhar Vashishth, Manik Bhandari, Prateek Yadav, Piyush Rai, Chiranjib Bhattacharyya, Partha Talukdar`\n    | :venue:`ACL 2019`\n\n`GraphRel：将文本建模为关系图以进行实体与关系联合抽取\n\u003Chttps:\u002F\u002Ftsujuifu.github.io\u002Fpubs\u002Facl19_graph-rel.pdf>`_\n    | :authors:`Tsu-Jui Fu, Peng-Hsuan Li, Wei-Yun Ma`\n    | :venue:`ACL 2019`\n\n`通过异构图推理实现跨多文档的多跳阅读理解\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.07374.pdf>`_\n    | :authors:`Ming Tu, Guangtao Wang, Jing Huang, Yun Tang, Xiaodong He, Bowen Zhou`\n    | :venue:`ACL 2019`\n\n`面向大规模多跳阅读理解的认知图\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.05460.pdf>`_\n    | :authors:`Ming Ding, Chang Zhou, Qibin Chen, Hongxia Yang, Jie Tang`\n    | :venue:`ACL 2019`\n\n`基于图到序列模型的中文文章连贯评论生成\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1906.01231.pdf>`_\n    | :authors:`Wei Li, Jingjing Xu, Yancheng He, Shengli Yan, Yunfang Wu, Xu Sun`\n    | :venue:`ACL 2019`\n\n`利用图分解与卷积匹配文章对\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1802.07459.pdf>`_\n    | :authors:`Bang Liu, Di Niu, Haojie Wei, Jinghong Lin, Yancheng He, Kunfeng Lai, Yu Xu`\n    | :venue:`ACL 2019`\n\n`基于 grounded 语言信息的嵌入补全\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1906.03753.pdf>`_\n    | :authors:`Ziyi Yang, Chenguang Zhu, Vin Sachidananda, Eric Darve`\n    | :venue:`ACL 2019`\n\n`利用图卷积网络编码社交信息以检测新闻媒体中的政治立场\n\u003Chttps:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FP19-1247.pdf>`_\n    | :authors:`Chang Li, Dan Goldwasser`\n    | :venue:`ACL 2019`\n\n`一种结合地名辞典的中文命名实体识别神经多有向图模型\n\u003Chttps:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FP19-1141.pdf>`_\n    | :authors:`Ruixue Ding, Pengjun Xie, Xiaoyan Zhang, Wei Lu, Linlin Li, Luo Si`\n    | :venue:`ACL 2019`\n\n`用于情感分析的树状通信模型\n\u003Chttps:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FP19-1342.pdf>`_\n    | :authors:`Yuan Zhang, Yue Zhang`\n    | :venue:`ACL 2019`\n\n`A2N：关注邻居以进行知识图谱推理\n\u003Chttps:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FP19-1431.pdf>`_\n    | :authors:`Trapit Bansal, Da-Cheng Juan, Sujith Ravi, Andrew McCallum`\n    | :venue:`ACL 2019`\n\n`基于多模态上下文图理解和自监督开放集阅读理解的教科书问答\n\u003Chttps:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FP19-1347.pdf>`_\n    | :authors:`Daesik Kim, Seonhoon Kim, Nojun Kwak`\n    | :venue:`ACL 2019`\n\n`再看句法：用于性别歧义代词消解的关系图卷积网络\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.08868.pdf>`_\n    | :authors:`Yinchuan Xu, Junlin Yang`\n    | :venue:`ACL 2019 研讨会`\n    | :keywords:`https:\u002F\u002Fgithub.com\u002Fianycxu\u002FRGCN-with-BERT`\n\n`学习用于文本表示的图池化和混合卷积操作\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1901.06965.pdf>`_\n    | :authors:`Hongyang Gao, Yongjun Chen, Shuiwang Ji`\n    | :venue:`WWW 2019`\n\n`学习创建句子语义关系图以进行多文档摘要\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1909.12231.pdf>`_\n    | :authors:`Diego Antognini, Boi Faltings`\n    | :venue:`EMNLP 2019`\n\n`依赖关系指导的 LSTM-CRF 用于命名实体识别\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1909.10148.pdf>`_\n    | :authors:`Zhanming Jie, Wei Lu`\n    | :venue:`EMNLP 2019`\n\n`建模对话结构和时间动态以联合预测谣言立场和真实性\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1909.08211.pdf>`_\n    | :authors:`Penghui Wei, Nan Xu, Wenji Mao`\n    | :venue:`EMNLP 2019`\n\n`DialogueGCN：用于对话中情绪识别的图卷积神经网络\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1908.11540.pdf>`_\n    | :authors:`Deepanway Ghosal, Navonil Majumder, Soujanya Poria, Niyati Chhaya, Alexander Gelbukh`\n    | :venue:`EMNLP 2019`\n\n`在 Transformer 中建模图结构以更好地进行 AMR 到文本生成\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1909.00136.pdf>`_\n    | :authors:`Jie Zhu, Junhui Li, Muhua Zhu, Longhua Qian, Min Zhang, Guodong Zhou`\n    | :venue:`EMNLP 2019`\n\n`KagNet：用于常识推理的知识感知图网络\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1909.02151.pdf>`_\n    | :authors:`Bill Yuchen Lin, Xinyue Chen, Jamin Chen, Xiang Ren`\n    | :venue:`EMNLP 2019`\n\n计算机视觉\n---------------\n\n`用于 RGBD 语义分割的 3D 图神经网络\n\u003Chttp:\u002F\u002Fwww.cs.toronto.edu\u002F~rjliao\u002Fpapers\u002Ficcv_2017_3DGNN.pdf>`_\n    | :authors:`Xiaojuan Qi, Renjie Liao, Jiaya Jia, Sanja Fidler, Raquel Urtasun`\n    | :venue:`ICCV 2017`\n\n`利用图神经网络进行场景识别\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1708.04320>`_\n    | :authors:`Ruiyu Li, Makarand Tapaswi, Renjie Liao, Jiaya Jia, Raquel Urtasun, Sanja Fidler`\n    | :venue:`ICCV 2017`\n\n`基于图的全方位图像分类\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1707.08301>`_\n    | :authors:`Renata Khasanova, Pascal Frossard`\n    | :venue:`ICCV 2017`\n\n`基于骨骼的动作识别时空图卷积网络\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1801.07455>`_\n    | :authors:`Sijie Yan, Yuanjun Xiong, Dahua Lin`\n    | :venue:`AAAI 2018`\n\n`从场景图生成图像\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1804.01622>`_\n    | :authors:`Justin Johnson, Agrim Gupta, Li Fei-Fei`\n    | :venue:`CVPR 2018`\n\n`FoldingNet：通过深度网格变形的点云自编码器\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1712.07262>`_\n    | :authors:`Yaoqing Yang, Chen Feng, Yiru Shen, Dong Tian`\n    | :venue:`CVPR 2018`\n\n`PPFNet：具有全局上下文感知的局部特征，用于鲁棒的 3D 点匹配\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1802.02669>`_\n    | :authors:`Haowen Deng, Tolga Birdal, Slobodan Ilic`\n    | :venue:`CVPR 2018`\n\n`超越卷积的迭代视觉推理\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1803.11189>`_\n    | :authors:`Xinlei Chen, Li-Jia Li, Li Fei-Fei, Abhinav Gupta`\n    | :venue:`CVPR 2018`\n\n`曲面网络\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1705.10819>`_\n    | :authors:`伊利亚·科斯特里科夫、蒋中石、达尼埃莱·帕诺佐、丹尼斯·佐林、琼·布鲁纳`\n    | :venue:`CVPR 2018`\n\n`FeaStNet：用于3D形状分析的特征引导图卷积\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1706.05206>`_\n    | :authors:`尼蒂卡·维尔马、埃德蒙·博耶、雅各布·韦尔贝克`\n    | :venue:`CVPR 2018`\n\n`学习恰当的行为：从图像中预测和解释可供性\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1712.07576>`_\n    | :authors:`程耀庄、李嘉曼、安东尼奥·托拉尔巴、桑贾·菲德勒`\n    | :venue:`CVPR 2018`\n\n`通过核相关性和图池化挖掘点云局部结构\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1712.06760>`_\n    | :authors:`沈一儒、冯晨、杨耀庆、田东`\n    | :venue:`CVPR 2018`\n\n`基于图卷积自编码器的可变形形状补全\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1712.00268>`_\n    | :authors:`奥尔·利塔尼、亚历克斯·布朗斯坦、迈克尔·布朗斯坦、阿米什·马卡迪亚`\n    | :venue:`CVPR 2018`\n\n`Pixel2Mesh：从单张RGB图像生成3D网格模型\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1804.01654>`_\n    | :authors:`王南洋、张银达、李竹文、傅延伟、刘伟、蒋宇刚`\n    | :venue:`ECCV 2018`\n\n`利用图解析神经网络学习人-物体交互\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1808.07962>`_\n    | :authors:`齐思远、王文冠、贾宝雄、沈建兵、朱松纯`\n    | :venue:`ECCV 2018`\n\n`使用卷积网格自编码器生成3D人脸\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1807.10267>`_\n    | :authors:`阿努拉格·兰詹、蒂莫·博尔卡特、索比克·桑亚尔、迈克尔·J·布莱克`\n    | :venue:`ECCV 2018`\n\n`利用球面CNN学习SO(3)等变表示\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1711.06721>`_\n    | :authors:`卡洛斯·埃斯特维斯、克里斯汀·艾伦-布兰切特、阿米什·马卡迪亚、科斯塔斯·达尼利迪斯`\n    | :venue:`ECCV 2018`\n\n`用于少样本3D动作识别的神经图匹配网络\n\u003Chttp:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_ECCV_2018\u002Fpapers\u002FMichelle_Guo_Neural_Graph_Matching_ECCV_2018_paper.pdf>`_\n    | :authors:`米歇尔·郭、爱德华·周、黄德安、宋书然、塞蕾娜·杨、李飞飞`\n    | :venue:`ECCV 2018`\n\n`用于点云上基于图的学习的多核扩散CNN\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1809.05370>`_\n    | :authors:`拉斯·汉森、贾斯珀·迪塞尔、马蒂亚斯·P·海因里希`\n    | :venue:`ECCV 2018`\n\n`基于深度卷积图网络的视频帧序列层次化表示\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1906.00377>`_\n    | :authors:`毛峰、吴翔、薛辉、张荣`\n    | :venue:`ECCV 2018`\n\n`用于场景图生成的Graph R-CNN\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1808.00191>`_\n    | :authors:`杨建伟、陆家森、史蒂芬·李、德鲁夫·巴特拉、黛薇·帕里克`\n    | :venue:`ECCV 2018`\n\n`探索视觉关系用于图像字幕生成\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1809.07041>`_\n    | :authors:`姚婷、潘英伟、李叶浩、梅涛`\n    | :venue:`ECCV 2018`\n\n`超越网格：为视觉识别学习图表示\n\u003Chttps:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F8135-beyond-grids-learning-graph-representations-for-visual-recognition>`_\n    | :authors:`李寅、阿比纳夫·古普塔`\n    | :venue:`NeurIPS 2018`\n\n`为可解释的视觉问答学习条件化的图结构\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1806.07243>`_\n    | :authors:`威尔·诺克利夫-布朗、埃夫斯塔西奥斯·瓦菲亚斯、萨拉·帕里索`\n    | :venue:`NeurIPS 2018`\n\n`LinkNet：用于场景图的关系嵌入\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1811.06410>`_\n    | :authors:`吴相贤、金大勋、曹东贤、权仁洙`\n    | :venue:`NeurIPS 2018`\n\n`用于物理预测的灵活神经表示\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1806.08047>`_\n    | :authors:`达米安·姆罗夫卡、庄成旭、伊莱亚斯·王、尼克·哈伯、李飞飞、乔舒亚·B·特南鲍姆、丹尼尔·L·K·亚明斯`\n    | :venue:`NeurIPS 2018`\n\n`通过图卷积学习3D点云的局部生成模型\n\u003Chttps:\u002F\u002Fopenreview.net\u002Fforum?id=SJeXSo09FQ>`_\n    | :authors:`迭戈·瓦尔塞西亚、朱莉娅·弗拉卡斯托罗、恩里科·马利`\n    | :venue:`ICLR 2019`\n\n`基于图的全局推理网络\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1811.12814>`_\n    | :authors:`陈云鹏、马库斯·罗尔巴赫、严志诚、颜水成、冯家诗、扬尼斯·卡兰蒂迪斯`\n    | :venue:`CVPR 2019`\n\n`用于真实图像鲁棒去噪的深度图拉普拉斯正则化\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1807.11637>`_\n    | :authors:`曾进、庞佳豪、孙文秀、吉恩·张`\n    | :venue:`CVPR 2019`\n\n`用于行人搜索的学习上下文图\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1904.01830>`_\n    | :authors:`燕义超、张强、倪冰冰、张文东、徐明浩、杨晓康`\n    | :venue:`CVPR 2019`\n\n`Graphonomy：通过图迁移学习实现通用人体解析\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1904.04536>`_\n    | :authors:`龚科、高一鸣、梁晓丹、申晓辉、王萌、林亮`\n    | :venue:`CVPR 2019`\n\n`用于行人重识别的掩码图注意力网络\n\u003Chttp:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPRW_2019\u002Fpapers\u002FTRMTMCT\u002FBao_Masked_Graph_Attention_Network_for_Person_Re-Identification_CVPRW_2019_paper.pdf>`_\n    | :authors:`鲍立强、马炳鹏、常洪、陈锡林`\n    | :venue:`CVPR 2019`\n\n`在亲和力图上学习对人脸进行聚类\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1904.02749>`_\n    | :authors:`杨雷、詹晓航、陈大鹏、闫俊杰、陈昌礼、林大华`\n    | :venue:`CVPR 2019`\n\n`用于基于骨骼的动作识别的行动-结构图卷积网络\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1904.12659>`_\n    | :authors:`李茂森、陈思恒、陈旭、张雅、王彦峰、田琪`\n    | :venue:`CVPR 2019`\n\n`自适应连接的神经网络\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1904.03579>`_\n    | :authors:`王广润、王克泽、林亮`\n    | :venue:`CVPR 2019`\n\n`结合结构与部分观测进行视觉对话推理\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1904.03579>`_\n    | :authors:`郑子龙、王文冠、齐思远、朱松纯`\n    | :venue:`CVPR 2019`\n\n`MeshCNN：一种带有边界的网络\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1809.05910.pdf>`_\n    | :authors:`拉娜·哈诺卡、阿米尔·赫兹、诺亚·费什、拉贾·吉里耶斯、沙查尔·弗莱施曼、丹尼尔·科恩-奥尔`\n    | :venue:`SIGGRAPH 2019`\n    | :keywords:`https:\u002F\u002Franahanocka.github.io\u002FMeshCNN\u002F`\n\n`用于无监督图表示学习的对称图卷积自编码器\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1908.02441.pdf>`_\n    | :authors:`朴智雄、李敏植、张炯珍、李奎旺、崔镇英`\n    | :venue:`ICCV 2019`\n\n`Pixel2Mesh++：通过形变实现多视角3D网格生成\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1908.01491.pdf>`_\n    | :authors:`温超、张银达、李竹文、傅延伟`\n    | :venue:`ICCV 2019`\n\n`学习轨迹依赖性以预测人类运动\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1908.05436.pdf>`_\n    | :authors:`毛伟、刘妙妙、马修·萨尔茨曼、李洪东`\n    | :venue:`ICCV 2019`\n\n`基于图的对象分类用于神经形态视觉感知\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1908.06648.pdf>`_\n    | :authors:`Yin Bi, Aaron Chadha, Alhabib Abbas, Eirina Bourtsoulatze, Yiannis Andreopoulos`\n    | :venue:`ICCV 2019`\n\n`基于相似性金字塔的图推理网络进行时尚检索\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1908.11754.pdf>`_\n    | :authors:`Zhanghui Kuang, Yiming Gao, Guanbin Li, Ping Luo, Yimin Chen, Liang Lin, Wayne Zhang`\n    | :venue:`ICCV 2019`\n\n`通过时空图推理理解人类注视交流\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1909.02144.pdf>`_\n    | :authors:`Lifeng Fan, Wenguan Wang, Siyuan Huang, Xinyu Tang, Song-Chun Zhu`\n    | :venue:`ICCV 2019`\n\n`用于图像-文本匹配的视觉语义推理\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1909.02701.pdf>`_\n    | :authors:`Kunpeng Li, Yulun Zhang, Kai Li, Yuanyuan Li, Yun Fu`\n    | :venue:`ICCV 2019`\n\n`用于时序动作定位的图卷积网络\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1909.03252.pdf>`_\n    | :authors:`Runhao Zeng, Wenbing Huang, Mingkui Tan, Yu Rong, Peilin Zhao, Junzhou Huang, Chuang Gan`\n    | :venue:`ICCV 2019`\n\n`语义正则化的逻辑图嵌入\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1909.01161.pdf>`_\n    | :authors:`Yaqi Xie, Ziwei Xu, Kuldeep Meel, Mohan S Kankanhalli, Harold Soh`\n    | :venue:`NeurIPS 2019`\n\n推荐系统\n-------------------\n\n`用于大规模Web推荐系统的图卷积神经网络\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1806.01973.pdf>`_\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：一种基于图卷积网络的高效社交推荐模型\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1811.02815.pdf>`_\n    | :authors:`Le Wu, Peijie Sun, Richang Hong, Yanjie Fu, Xiting Wang, Meng Wang`\n    | :venue:`AAAI 2018`\n    | :keywords:`GCN，社交推荐`\n\n`基于会话的社交推荐：利用动态图注意力网络\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1902.09362.pdf>`_\n    | :authors:`Weiping Song, Zhiping Xiao, Yifan Wang, Laurent Charlin, Ming Zhang, Jian Tang`\n    | :venue:`WSDM 2019`\n    | :keywords:`社交推荐，基于会话，GAT`\n\n`双图注意力网络：用于推荐系统中多方面社交效应的深度潜在表示\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1903.10433.pdf>`_\n    | :authors:`Qitian Wu, Hengrui Zhang, Xiaofeng Gao, Peng He, Paul Weng, Han Gao, Guihai Chen`\n    | :venue:`WWW 2019`\n    | :keywords:`社交推荐，GAT`\n\n`用于社交推荐的图神经网络\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1902.07243.pdf>`_\n    | :authors:`Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, Dawei Yin`\n    | :venue:`WWW 2019`\n    | :keywords:`社交推荐，GNN`\n\n`基于图神经网络的会话型推荐\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1811.00855.pdf>`_\n    | :authors:`Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, Tieniu Tan`\n    | :venue:`AAAI 2019`\n    | :keywords:`会话型推荐，GNN`\n\n`用于社交推荐的神经影响力扩散模型\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1904.10322.pdf>`_\n    | :authors:`Le Wu, Peijie Sun, Yanjie Fu, Richang Hong, Xiting Wang, Meng Wang`\n    | :venue:`SIGIR 2019`\n    | :keywords:`社交推荐，扩散`\n\n`神经图协同过滤\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.08108.pdf>`_\n    | :authors:`Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, Tat-Seng Chua`\n    | :venue:`SIGIR 2019`\n    | :keywords:`协同过滤，GNN`\n\n`基于蒸馏图卷积网络的二值化协同过滤\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1906.01829.pdf>`_\n    | :authors:`Haoyu Wang, Defu Lian, Yong Ge`\n    | :venue:`IJCAI 2019`\n\n`IntentGC：一个融合异构信息的可扩展图卷积框架，用于推荐\n\u003Chttps:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3330686>`_\n    | :authors:`Jun Zhao, Zhou Zhou, Ziyu Guan, Wei Zhao, Wei Ning, Guang Qiu, Xiaofei He`\n    | :venue:`KDD 2019`\n\n`面向知识增强推荐的端到端邻域交互模型\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1908.04032.pdf>`_\n    | :authors:`Yanru Qu, Ting Bai, Weinan Zhang, Jianyun Nie, Jian Tang`\n    | :venue:`KDD 2019研讨会`\n\n链接预测\n---------------\n\n`基于图神经网络的链接预测\n\u003Chttps:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F7763-link-prediction-based-on-graph-neural-networks.pdf>`_\n    | :authors:`Muhan Zhang, Yixin Chen`\n    | :venue:`NeurIPS 2018`\n\n`基于子图嵌入的凸矩阵补全进行链接预测\n\u003Chttp:\u002F\u002Fiiis.tsinghua.edu.cn\u002F~weblt\u002Fpapers\u002Flink-prediction-subgraphembeddings.pdf>`_\n    | :authors:`Zhu Cao, Linlin Wang, Gerard de Melo`\n    | :venue:`AAAI 2018`\n\n`图卷积矩阵补全\n\u003Chttps:\u002F\u002Fwww.kdd.org\u002Fkdd2018\u002Ffiles\u002Fdeep-learning-day\u002FDLDay18_paper_32.pdf>`_\n    | :authors:`Rianne van den Berg, Thomas N. Kipf, Max Welling`\n    | :venue:`KDD 2018研讨会`\n\n`半隐式图变分自编码器\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1908.07078.pdf>`_\n    | :authors:`Arman Hasanzadeh, Ehsan Hajiramezanali, Nick Duffield , Krishna Narayanan, Mingyuan Zhou, Xiaoning Qian`\n    | :venue:`NeurIPS 2019`\n\n影响力预测\n--------------------\n\n`DeepInf：基于深度学习的社交影响力预测\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1807.05560.pdf>`_\n    | :authors:`Jiezhong Qiu, Jian Tang, Hao Ma, Yuxiao Dong, Kuansan Wang, Jie Tang`\n    | :venue:`KDD 2018`\n\n`利用图神经网络估计知识图中的节点重要性\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.08865.pdf>`_\n    | :authors:`Namyong Park, Andrey Kan, Xin Luna Dong, Tong Zhao, Christos Faloutsos`\n    | :venue:`KDD 2019`\n\n神经架构搜索\n--------------------------\n\n`用于神经架构搜索的图超网络\n\u003Chttps:\u002F\u002Fopenreview.net\u002Fpdf?id=rkgW0oA9FX>`_\n    | :authors:`Chris Zhang, Mengye Ren, Raquel Urtasun`\n    | :venue:`ICLR 2019`\n\n`D-VAE：一种用于有向无环图的变分自编码器\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1904.11088.pdf>`_\n    | :authors:`Muhan Zhang, Shali Jiang, Zhicheng Cui, Roman Garnett, Yixin Chen`\n    | :venue:`NeurIPS 2019`\n\n强化学习\n----------------------\n\n`动作模式网络：基于深度学习的泛化策略\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1709.04271.pdf>`_\n    | :authors:`Sam Toyer, Felipe Trevizan, Sylvie Thiebaux, Lexing Xie`\n    | :venue:`AAAI 2018`\n\n`NerveNet：利用图神经网络学习结构化策略\n\u003Chttps:\u002F\u002Fopenreview.net\u002Fpdf?id=S1sqHMZCb>`_\n    | :authors:`Tingwu Wang, Renjie Liao, Jimmy Ba, Sanja Fidler`\n    | :venue:`ICLR 2018`\n\n`图网络作为可学习的物理引擎，用于推理和控制\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1806.01242.pdf>`_\n    | :authors:`Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg, Josh Merel, Martin Riedmiller`\n    | :venue:`ICML 2018`\n\n`多智能体系统中的策略表示学习\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1806.06464.pdf>`_\n    | :authors:`Aditya Grover, Maruan Al-Shedivat, Jayesh K. Gupta, Yura Burda, Harrison Edwards`\n    | :venue:`ICML 2018`\n\n`关系递归神经网络\n\u003Chttps:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F7960-relational-recurrent-neural-networks.pdf>`_\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`用于MDP规划的深度反应式策略迁移\n\u003Chttp:\u002F\u002Fwww.cse.iitd.ac.in\u002F~mausam\u002Fpapers\u002Fnips18.pdf>`_\n    | :authors:`Aniket Bajpai, Sankalp Garg, Mausam`\n    | :venue:`NeurIPS 2018`\n\n`神经图进化：迈向高效的自动机器人设计\n\u003Chttps:\u002F\u002Fopenreview.net\u002Fpdf?id=BkgWHnR5tm>`_\n    | :authors:`Tingwu Wang, Yuhao Zhou, Sanja Fidler, Jimmy Ba`\n    | :venue:`ICLR 2019`\n\n`无压外交：多智能体游戏建模\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1909.02128.pdf>`_\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\n组合优化\n----------\n\n`基于图的组合优化算法学习\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1704.01665>`_\n    | :authors:`Hanjun Dai, Elias B. Khalil, Yuyu Zhang, Bistra Dilkina, Le Song`\n    | :venue:`NeurIPS 2017`\n\n`基于图卷积网络与引导树搜索的组合优化\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1810.10659>`_\n    | :authors:`Zhuwen Li, Qifeng Chen, Vladlen Koltun`\n    | :venue:`NeurIPS 2018`\n\n`强化学习求解车辆路径问题\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1802.04240>`_\n    | :authors:`Mohammadreza Nazari, Afshin Oroojlooy, Lawrence V. Snyder, Martin Takáč`\n    | :venue:`NeurIPS 2018`\n\n`注意力机制，学会解决路由问题！\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1803.08475>`_\n    | :authors:`Wouter Kool, Herke van Hoof, Max Welling`\n    | :venue:`ICLR 2019`\n\n`从单比特监督中学习SAT求解器\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1802.03685>`_\n    | :authors:`Daniel Selsam, Matthew Lamm, Benedikt Bünz, Percy Liang, Leonardo de Moura, David L. Dill`\n    | :venue:`ICLR 2019`\n\n`一种针对旅行商问题的高效图卷积网络技术\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1906.01227>`_\n    | :authors:`Chaitanya K. Joshi, Thomas Laurent, Xavier Bresson`\n    | :venue:`arXiv 2019`\n\n`图神经网络在组合问题上的近似比\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.10261.pdf>`_\n    | :authors:`Ryoma Sato, Makoto Yamada, Hisashi Kashima`\n    | :venue:`NeurIPS 2019`\n\n`利用图卷积神经网络进行精确的组合优化\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1906.01629.pdf>`_\n    | :authors:`Maxime Gasse, Didier Chételat, Nicola Ferroni, Laurent Charlin, Andrea Lodi`\n    | :venue:`NeurIPS 2019`\n\n`关于旅行商问题的学习范式\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1910.07210.pdf>`_\n    | :authors:`Chaitanya K. Joshi, Thomas Laurent, Xavier Bresson`\n    | :venue:`NeurIPS 2019研讨会`\n\n对抗攻击与鲁棒性\n------------------\n\n`针对图结构数据的对抗攻击\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1806.02371>`_\n    | :authors:`Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, Le Song`\n    | :venue:`ICML 2018`\n\n`针对图数据神经网络的对抗攻击\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1805.07984>`_\n    | :authors:`Daniel Zügner, Amir Akbarnejad, Stephan Günnemann`\n    | :venue:`KDD 2018`\n\n`通过元学习对图神经网络进行对抗攻击\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1902.08412>`_\n    | :authors:`Daniel Zügner, Stephan Günnemann`\n    | :venue:`ICLR 2019`\n\n`对抗攻击下的鲁棒图卷积网络\n\u003Chttp:\u002F\u002Fpengcui.thumedialab.com\u002Fpapers\u002FRGCN.pdf>`_\n    | :authors:`Dingyuan Zhu, Ziwei Zhang, Peng Cui, Wenwu Zhu`\n    | :venue:`KDD 2019`\n\n`图卷积网络的可认证鲁棒性与鲁棒训练\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1906.12269.pdf>`_\n    | :authors:`Daniel Zügner, Stephan Günnemann`\n    | :venue:`KDD 2019`\n\n图匹配\n-------------\n\n`REGAL：基于表示学习的图对齐\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1802.06257.pdf>`_\n\t| :authors:`Mark Heimann, Haoming Shen, Tara Safavi, Danai Koutra`\n\t| :venue:`CIKM 2018`\n\n`跨语言知识图谱对齐——基于图卷积网络\n\u003Chttps:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FD18-1032.pdf>`_\n\t| :authors:`Zhichun Wang, Qingsong Lv, Xiaohan Lan, Yu Zhang`\n\t| :venue:`EMNLP 2018`\n\n`用于深度图匹配的组合嵌入网络学习\n\u003Chttp:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_ICCV_2019\u002Fpapers\u002FWang_Learning_Combinatorial_Embedding_Networks_for_Deep_Graph_Matching_ICCV_2019_paper.pdf>`_\n\t| :authors:`Runzhong Wang, Junchi Yan, Xiaokang Yang`\n\t| :venue:`ICCV 2019`\n\n`深度图匹配共识\n\u003Chttps:\u002F\u002Fopenreview.net\u002Fpdf?id=HyeJf1HKvS>`_\n\t| :authors:`Matthias Fey, Jan E. Lenssen, Christopher Morris, Jonathan Masci, Nils M. Kriege`\n\t| :venue:`ICLR 2020`\n\n元学习与少样本学习\n---------------------------------\n\n`基于图神经网络的少样本学习\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1711.04043>`_\n    | :authors:`Victor Garcia, Joan Bruna`\n    | :venue:`ICLR 2018`\n\n`学习图上迭代算法的稳态\n\u003Chttp:\u002F\u002Fproceedings.mlr.press\u002Fv80\u002Fdai18a.html>`_\n    | :authors:`Hanjun Dai, Zornitsa Kozareva, Bo Dai, Alex Smola, Le Song`\n    | :venue:`ICML 2018`\n\n`为图元学习而学习传播\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1909.05024.pdf>`_\n    | :authors:`Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang`\n    | :venue:`NeurIPS 2019`\n\n`基于图谱特征的超类进行图上的少样本学习\n\u003Chttps:\u002F\u002Fopenreview.net\u002Fforum?id=Bkeeca4Kvr>`_\n\t| :authors:`Jatin Chauhan, Deepak Nathani, Manohar Kaul`\n\t| :venue:`ICLR 2020`\n\n`自动化的关系型元学习\n\u003Chttps:\u002F\u002Fopenreview.net\u002Fpdf?id=rklp93EtwH>`_\n\t| :authors:`Huaxiu Yao, Xian Wu, Zhiqiang Tao, Yaliang Li, Bolin Ding, Ruirui Li, Zhenhui Li`\n\t| :venue:`ICLR 2020`\n\n结构学习\n------------------\n\n`用于交互系统的神经关系推理\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1802.04687>`_\n    | :authors:`Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, Richard Zemel`\n    | :venue:`ICML 2018`\n\n`通过学习连接结构进行脑信号分类\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1905.11678>`_\n    | :authors:`Soobeom Jang, Seong-Eun Moon, Jong-Seok Lee`\n    | :venue:`arXiv 2019`\n\n`一种灵活的基于图的半监督学习生成框架\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1905.10769>`_\n    | :authors:`Jiaqi Ma, Weijing Tang, Ji Zhu, Qiaozhu Mei`\n    | :venue:`NeurIPS 2019`\n\n`通过图卷积网络实现结构与特征的联合嵌入\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1905.08636>`_\n    | :authors:`Sébastien Lerique, Jacob Levy Abitbol, Márton Karsai`\n    | :venue:`arXiv 2019`\n\n`变分谱图卷积网络\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1906.01852>`_\n    | :authors:`Louis Tiao, Pantelis Elinas, Harrison Nguyen, Edwin V. Bonilla`\n    | :venue:`arXiv 2019`\n\n`学习标签传播：用于少样本学习的直推式传播网络\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1805.10002>`_\n    | :authors:`Yanbin Liu, Juho Lee, Minseop Park, Saehoon Kim, Eunho Yang, Sung Ju Hwang, Yi Yang`\n    | :venue:`ICLR 2019`\n\n`图学习网络：一种结构学习算法\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1905.12665>`_\n    | :authors:`Darwin Saire Pilco, Adín Ramírez Rivera`\n    | :venue:`ICML 2019 研讨会`\n\n`为图神经网络学习离散结构\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1903.11960>`_\n    | :authors:`Luca Franceschi, Mathias Niepert, Massimiliano Pontil, Xiao He`\n    | :venue:`ICML 2019`\n\n`Graphite：图的迭代生成模型\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1803.10459>`_\n    | :authors:`Aditya Grover, Aaron Zweig, Stefano Ermon`\n    | :venue:`ICML 2019`\n\n生物信息学与化学\n--------------\n\n`利用图卷积网络进行蛋白质界面预测\n\u003Chttps:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F7231-protein-interface-prediction-using-graph-convolutional-networks.pdf>`_\n    | :authors:`Alex Fout, Jonathon Byrd, Basir Shariat, Asa Ben-Hur`\n    | :venue:`NeurIPS 2017`\n\n`用图卷积网络建模多药联用的副作用\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1802.00543>`_\n    | :authors:`Marinka Zitnik, Monica Agrawal, Jure Leskovec`\n    | :venue:`Bioinformatics 2018`\n\n`NeoDTI：基于异质网络的邻居信息神经融合，用于发现新的药物—靶点相互作用\n\u003Chttps:\u002F\u002Facademic.oup.com\u002Fbioinformatics\u002Farticle-abstract\u002F35\u002F1\u002F104\u002F5047760?redirectedFrom=fulltext>`_\n    | :authors:`Fangping Wan, Lixiang Hong, An Xiao, Tao Jiang, Jianyang Zeng`\n    | :venue:`Bioinformatics 2018`\n\n`SELFIES：一种鲁棒的语义约束图表示法，并以化学领域为例\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.13741.pdf>`_\n    | :authors:`Mario Krenn, Florian Häse, AkshatKumar Nigam, Pascal Friederich, Alán Aspuru-Guzik`\n    | :venue:`arXiv 2019`\n\n`基于图协同注意力的药物—药物不良反应预测\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.00534.pdf>`_\n    | :authors:`Andreea Deac, Yu-Hsiang Huang, Petar Veličković, Pietro Liò, Jian Tang`\n    | :venue:`ICML 2019 研讨会`\n\n`GCN-MF：通过图卷积网络和矩阵分解识别疾病—基因关联\n\u003Chttps:\u002F\u002Fwww.kdd.org\u002Fkdd2019\u002Faccepted-papers\u002Fview\u002Fgcn-mf-disease-gene-association-identification-by-graph-convolutional-netwo>`_\n    | :authors:`Peng Han, Peng Yang, Peilin Zhao, Shuo Shang, Yong Liu, Jiayu Zhou, Xin Gao, Panos Kalnis`\n    | :venue:`KDD 2019`\n\n`利用人工神经网络和经典图相似度指标检测药物—药物相互作用\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1903.04571.pdf>`_\n    | :authors:`Guy Shtar, Lior Rokach, Bracha Shapira`\n    | :venue:`arXiv 2019`\n\n`PGCN：通过图卷积神经网络对疾病和基因进行嵌入，实现疾病基因优先级排序\n\u003Chttps:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002Fbiorxiv\u002Fearly\u002F2019\u002F01\u002F28\u002F532226.full.pdf>`_\n    | :authors:`Yu Li, Hiroyuki Kuwahara, Peng Yang, Le Song, Xin Gao`\n    | :venue:`bioRxiv 2019`\n\n`利用树LSTM和结构化注意力识别蛋白质—蛋白质相互作用\n\u003Chttps:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8665584>`_\n    | :authors:`Mahtab Ahmed, Jumayel Islam, Muhammad Rifayat Samee, Robert E. Mercer`\n    | :venue:`ICSC 2019`\n\n`GCN-MF：通过图卷积网络和矩阵分解识别疾病—基因关联\n\u003Chttps:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3330912>`_\n    | :authors:`Peng Han, Peng Yang, Peilin Zhao, Shuo Shang, Yong Liu, Jiayu Zhou, Xin Gao, Panos Kalnis`\n    | :venue:`KDD 2019`\n\n`利用深度学习预测生物网络扰动\n\u003Chttps:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-019-48391-y>`_\n    | :authors:`Diya Li, Jianxi Gao`\n    | :venue:`Nature 2019`\n\n`面向分子图的定向消息传递\n\u003Chttps:\u002F\u002Fopenreview.net\u002Fpdf?id=B1eWbxStPH>`_\n\t| :authors:`Johannes Klicpera, Janek Groß, Stephan Günnemann`\n\t| :venue:`ICLR 2020`\n\n图算法\n---------------\n\n`图算法的神经执行\n\u003Chttps:\u002F\u002Fopenreview.net\u002Fpdf?id=SkgKO0EtvS>`_\n\t| :authors:`Petar Veličković, Rex Ying, Matilde Padovano, Raia Hadsell, Charles Blundell`\n\t| :venue:`ICLR 2020`\n\n定理证明\n---------------\n\n`基于深度图嵌入的定理证明前提选择\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1709.09994>`_\n    | :authors:`Mingzhe Wang, Yihe Tang, Jian Wang, Jia Deng`\n    | :venue:`NeurIPS 2017`\n\n图生成\n================\n\n`GraphRNN：利用深度自回归模型生成逼真的图\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1802.08773>`_\n    | :authors:`Jiaxuan You, Rex Ying, Xiang Ren, William L. Hamilton, Jure Leskovec`\n    | :venue:`ICML 2018`\n\n`NetGAN：通过随机游走生成图\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1803.00816>`_\n    | :authors:`Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, Stephan Günnemann`\n    | :venue:`ICML 2018`\n\n`学习图的深度生成模型\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1803.03324>`_\n    | :authors:`Yujia Li, Oriol Vinyals, Chris Dyer, Razvan Pascanu, Peter Battaglia`\n    | :venue:`ICML 2018`\n\n`用于分子图生成的交界树变分自编码器\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1802.04364>`_\n    | :authors:`Wengong Jin, Regina Barzilay, Tommi Jaakkola`\n    | :venue:`ICML 2018`\n\n`MolGAN：一种用于小型分子图的隐式生成模型\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1805.11973>`_\n    | :authors:`Nicola De Cao, Thomas Kipf`\n    | :venue:`arXiv 2018`\n\n`蛋白质结构的生成式建模\n\u003Chttps:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F7978-generative-modeling-for-protein-structures.pdf>`_\n    | :authors:`Namrata Anand, Po-Ssu Huang`\n    | :venue:`NeurIPS 2018`\n\n`通过正则化变分自编码器生成语义有效的约束图\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1809.02630>`_\n    | :authors:`Tengfei Ma, Jie Chen, Cao Xiao`\n    | :venue:`NeurIPS 2018`\n\n`面向目标的分子图生成的图卷积策略网络\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1806.02473>`_\n    | :authors:`Jiaxuan You, Bowen Liu, Rex Ying, Vijay Pande, Jure Leskovec`\n    | :venue:`NeurIPS 2018`\n\n`用于分子设计的约束图变分自编码器\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1805.09076>`_\n    | :authors:`Qi Liu, Miltiadis Allamanis, Marc Brockschmidt, Alexander L. Gaunt`\n    | :venue:`NeurIPS 2018`\n\n`用于分子优化的多模态图—图翻译学习\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1812.01070>`_\n    | :authors:`Wengong Jin, Kevin Yang, Regina Barzilay, Tommi Jaakkola`\n    | :venue:`ICLR 2019`\n\n`基于图的生成式代码建模\n\u003Chttps:\u002F\u002Fopenreview.net\u002Fforum?id=Bke4KsA5FX>`_\n    | :authors:`Marc Brockschmidt, Miltiadis Allamanis, Alexander L. Gaunt, Oleksandr Polozov`\n    | :venue:`ICLR 2019`\n\n`DAG-GNN：基于图神经网络的有向无环图结构学习\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1904.10098>`_\n    | :authors:`Yue Yu, Jie Chen, Tian Gao, Mo Yu`\n    | :venue:`ICML 2019`\n\n`图到图：一种拓扑感知的图结构学习与生成方法\n\u003Chttp:\u002F\u002Fproceedings.mlr.press\u002Fv89\u002Fsun19c.html>`_\n    | :authors:`Mingming Sun, Ping Li`\n    | :venue:`AISTATS 2019`\n\n`图归一化流\n\u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F1905.13177>`_\n    | :authors:`Jenny Liu, Aviral Kumar, Jimmy Ba, Jamie Kiros, Kevin Swersky`\n    | :venue:`NeurIPS 2019`\n\n`通过图变分生成对抗网络进行条件结构生成\n\u003Chttp:\u002F\u002Fjiyang3.web.engr.illinois.edu\u002Ffiles\u002Fcondgen.pdf>`_\n    | :authors:`Carl Yang, Peiye Zhuang, Wenhan Shi, Alan Luu, Pan Li`\n    | :venue:`NeurIPS 2019`\n\n`利用图递归注意力网络高效生成图\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1910.00760.pdf>`_\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：一种基于流的自回归模型用于分子图生成\n\u003Chttps:\u002F\u002Fopenreview.net\u002Fpdf?id=S1esMkHYPr>`_\n\t| :authors:`Chence Shi, Minkai Xu, Zhaocheng Zhu, Weinan Zhang, Ming Zhang, Jian Tang`\n\t| :venue:`ICLR 2020`\n\n图布局与高维数据可视化\n====================================================\n\n`使用t-SNE可视化数据\n\u003Chttp:\u002F\u002Fwww.jmlr.org\u002Fpapers\u002Fvolume9\u002Fvandermaaten08a\u002Fvandermaaten08a.pdf>`_\n    | :authors:`Laurens van der Maaten, Geoffrey Hinton`\n    | :venue:`JMLR 2008`\n\n`在多张地图中可视化非度量相似性\n\u003Chttps:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs10994-011-5273-4.pdf>`_\n    | :authors:`Laurens van der Maaten, Geoffrey Hinton`\n    | :venue:`ML 2012`\n\n`大规模和高维数据的可视化\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1602.00370>`_\n    | :authors:`Jian Tang, Jingzhou Liu, Ming Zhang, Qiaozhu Mei`\n    | :venue:`WWW 2016`\n\n`GraphTSNE：一种用于图结构数据的可视化技术\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1904.06915.pdf>`_\n    | :authors:`Yao Yang Leow, Thomas Laurent, Xavier Bresson`\n    | :venue:`ICLR 2019 Workshop`\n\n图表示学习系统\n=====================================\n\n`GraphVite：一个高性能的CPU-GPU混合节点嵌入系统\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1903.00757>`_\n    | :authors:`Zhaocheng Zhu, Shizhen Xu, Meng Qu, Jian Tang`\n    | :venue:`WWW 2019`\n\n`PyTorch-BigGraph：一个大规模图嵌入系统\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1903.12287>`_\n    | :authors:`Adam Lerer, Ledell Wu, Jiajun Shen, Timothee Lacroix, Luca Wehrstedt, Abhijit Bose, Alex Peysakhovich`\n    | :venue:`SysML 2019`\n\n`AliGraph：一个全面的图神经网络平台\n\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F1902.08730>`_\n    | :authors:`Rong Zhu, Kun Zhao, Hongxia Yang, Wei Lin, Chang Zhou, Baole Ai, Yong Li, Jingren Zhou`\n    | :venue:`VLDB 2019`\n\n`深度图库\n\u003Chttps:\u002F\u002Fwww.dgl.ai>`_\n    | :authors:`DGL团队`\n\n`AmpliGraph\n\u003Chttps:\u002F\u002Fgithub.com\u002FAccenture\u002FAmpliGraph>`_\n    | :authors:`Luca Costabello, Sumit Pai, Chan Le Van, Rory McGrath, Nicholas McCarthy, Pedro Tabacof`\n\n`Euler\n\u003Chttps:\u002F\u002Fgithub.com\u002Falibaba\u002Feuler>`_\n    | :authors:`阿里妈妈工程平台团队，阿里妈妈搜索广告算法团队`\n\n数据集\n========\n\n`ATOMIC：用于if-then推理的机器常识图谱\n\u003Chttps:\u002F\u002Fwvvw.aaai.org\u002Fojs\u002Findex.php\u002FAAAI\u002Farticle\u002Fdownload\u002F4160\u002F4038>`_\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`","# LiteratureDL4Graph 快速上手指南\n\n**LiteratureDL4Graph** 并非一个可安装的软件库或代码框架，而是一个**图深度学习（Deep Learning for Graphs）领域的学术论文清单**。它汇集了该领域的重要研究成果，按主题（如节点表示学习、异构图、动态图等）和会议进行分类整理。\n\n因此，本指南将指导您如何访问、浏览和利用这份资源，而非执行传统的软件安装步骤。\n\n## 1. 环境准备\n\n由于该项目本质上是文档列表，您无需配置复杂的开发环境（如 Python 版本、GPU 驱动等）。仅需满足以下条件即可：\n\n*   **操作系统**：任意支持现代浏览器的系统（Windows, macOS, Linux）。\n*   **前置依赖**：\n    *   现代网页浏览器（推荐 Chrome, Firefox, Edge）。\n    *   （可选）Git：如果您希望克隆仓库到本地进行离线阅读或贡献。\n*   **网络要求**：\n    *   项目托管于 GitHub，国内访问可能不稳定。\n    *   **推荐方案**：建议使用 **GitHub 加速镜像** 或配置代理访问。\n    *   论文链接多指向 arXiv、ACM DL 或 IEEE Xplore，部分可能需要机构权限或使用 arXiv 镜像（如 `arxiv.org.cn`）下载 PDF。\n\n## 2. 获取与安装步骤\n\n您有两种方式查看该论文清单：\n\n### 方式一：在线浏览（推荐）\n直接访问项目的 GitHub 页面，这是最便捷的方式，内容会实时更新。\n\n1.  打开浏览器。\n2.  访问项目主页：\n    ```text\n    https:\u002F\u002Fgithub.com\u002Flemon-lab\u002FLiteratureDL4Graph\n    ```\n    *(注：如果直连速度慢，可在 URL 前加上镜像前缀，例如使用 `https:\u002F\u002Fghproxy.com\u002Fhttps:\u002F\u002Fraw.githubusercontent.com\u002F...` 查看原始文件，或直接搜索项目名称寻找国内镜像站)*\n\n### 方式二：本地克隆（适合离线阅读或贡献）\n如果您需要本地保存或修改 `.rst` 源文件：\n\n1.  打开终端（Terminal 或 CMD）。\n2.  执行克隆命令（建议使用加速地址）：\n    ```bash\n    git clone https:\u002F\u002Fghproxy.com\u002Fhttps:\u002F\u002Fgithub.com\u002Flemon-lab\u002FLiteratureDL4Graph.git\n    ```\n    *若无加速需求，可使用原生命令：*\n    ```bash\n    git clone https:\u002F\u002Fgithub.com\u002Flemon-lab\u002FLiteratureDL4Graph.git\n    ```\n3.  进入目录：\n    ```bash\n    cd LiteratureDL4Graph\n    ```\n4.  （可选）在本地构建 HTML 文档（需安装 Sphinx）：\n    ```bash\n    pip install sphinx\n    make html\n    # 然后在浏览器中打开 build\u002Fhtml\u002Findex.html\n    ```\n\n## 3. 基本使用\n\n本项目的使用核心在于**检索**和**追踪**前沿论文。以下是使用示例：\n\n### 场景 A：查找特定主题的论文\n假设您想研究“无监督节点表示学习”（Unsupervised Node Representation Learning）：\n\n1.  在网页版或本地 `README.rst` 文件中，定位到 **Node Representation Learning** 章节。\n2.  找到 **Unsupervised Node Representation Learning** 子标题。\n3.  浏览列表，例如您看到了 **node2vec**：\n    *   **标题**: `node2vec: Scalable Feature Learning for Networks`\n    *   **作者**: Aditya Grover, Jure Leskovec\n    *   **会议**: KDD 2016\n    *   **关键词**: Breadth-first Search, Depth-first Search, Node Classification\n    *   **操作**: 点击标题链接直接跳转至 arXiv PDF 或论文主页进行阅读。\n\n### 场景 B：按会议\u002F期刊筛选\n假设您只想看 **WWW** 会议的相关论文：\n\n1.  在项目首页顶部，点击 **\"Sort by venue\"** 链接（对应文件 `BYVENUE.rst`）。\n2.  在生成的列表中查找 **WWW** 标签。\n3.  您将看到按年份排序的 WWW 收录论文，如 `LINE (WWW 2015)`, `VERSE (WWW 2018)` 等。\n\n### 场景 C：追踪最新进展\n由于这是一个开源列表，社区会持续更新：\n\n1.  定期访问 GitHub 仓库的 **Commits** 页面。\n2.  查看最近的提交记录，确认是否有新论文被添加（通常标题会包含 \"Add paper: [Paper Name]\"）。\n3.  如果是本地克隆版本，运行以下命令同步最新列表：\n    ```bash\n    git pull origin main\n    ```\n\n通过上述步骤，您可以高效地利用 LiteratureDL4Graph 作为图深度学习领域的导航图，快速定位所需的核心文献。","某金融科技公司的算法团队正致力于构建一个基于图神经网络的欺诈检测系统，急需调研最新的节点表示学习技术以优化模型效果。\n\n### 没有 LiteratureDL4Graph 时\n- **检索效率低下**：研究人员需在 Google Scholar、arXiv 和各大会议官网间反复切换，手动筛选“图深度学习”相关论文，耗时数天仍难保全。\n- **关键信息遗漏**：由于缺乏系统性整理，极易错过如 `struc2vec`（结构身份识别）或 `Poincaré Embeddings`（层次化表示）等对特定欺诈模式至关重要的经典工作。\n- **技术选型盲目**：面对海量文献，难以快速对比 `DeepWalk`、`node2vec` 与 `GraphGAN` 等算法的核心关键词（如随机游走 vs 对抗生成），导致基线模型选择缺乏依据。\n- **脉络梳理困难**：无法直观看清从 2014 年 `DeepWalk` 到 2019 年 `Deep Graph Infomax` 的技术演进路线，浪费大量时间在阅读低相关性摘要上。\n\n### 使用 LiteratureDL4Graph 后\n- **一站式获取资源**：团队直接访问该清单，按“主题”或“会议 venue\"瞬间锁定近五年所有图深度学习顶会论文，将调研周期从数天压缩至几小时。\n- **精准匹配需求**：通过清单中的关键词索引（如\"Structural Identity\"、\"Adversarial\"），迅速定位到能解决团伙欺诈隐蔽性问题的 `struc2vec` 和 `Adversarial Network Embedding`。\n- **科学决策基线**：利用清单提供的作者、年份及核心方法摘要，快速评估并确定了结合 `node2vec` 的广度优先搜索与 `GraphGAN` 生成能力的混合架构作为初始方案。\n- **清晰把握演进**：借助按时间排序的列表，团队清晰掌握了从无监督随机游走到变分自编码器再到信息最大化策略的技术迭代逻辑，避免了重复造轮子。\n\nLiteratureDL4Graph 将碎片化的学术情报转化为结构化的知识地图，让算法团队能将宝贵精力从“找论文”彻底转向“用论文”。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FDeepGraphLearning_LiteratureDL4Graph_132e47a9.png","DeepGraphLearning","MilaGraph","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002FDeepGraphLearning_0319a057.png","Research group led by Prof. Jian Tang at Mila-Quebec AI Institute (https:\u002F\u002Fmila.quebec\u002F) focusing on graph representation learning and graph neural networks.",null,"tangjianpku@gmail.com","https:\u002F\u002Fjian-tang.com\u002Fstudents\u002F","https:\u002F\u002Fgithub.com\u002FDeepGraphLearning",3080,559,"2026-04-11T11:10:32","MIT",1,"","未说明",{"notes":30,"python":28,"dependencies":31},"该工具并非一个可执行的软件库或框架，而是一个关于“图深度学习”的学术论文列表（Paper List）。README 内容仅列出了相关论文的标题、作者、发表 venue 和关键词链接，不包含任何代码安装指南、运行环境需求或依赖库信息。",[],[33],"开发框架",[35,36,37,38],"machine-learning","deep-learning","papers","arxiv",2,"ready","2026-03-27T02:49:30.150509","2026-04-19T15:26:04.451151",[],[],[46,58,66,75,83,92],{"id":47,"name":48,"github_repo":49,"description_zh":50,"stars":51,"difficulty_score":52,"last_commit_at":53,"category_tags":54,"status":40},4358,"openclaw","openclaw\u002Fopenclaw","OpenClaw 是一款专为个人打造的本地化 AI 助手，旨在让你在自己的设备上拥有完全可控的智能伙伴。它打破了传统 AI 助手局限于特定网页或应用的束缚，能够直接接入你日常使用的各类通讯渠道，包括微信、WhatsApp、Telegram、Discord、iMessage 等数十种平台。无论你在哪个聊天软件中发送消息，OpenClaw 都能即时响应，甚至支持在 macOS、iOS 和 Android 设备上进行语音交互，并提供实时的画布渲染功能供你操控。\n\n这款工具主要解决了用户对数据隐私、响应速度以及“始终在线”体验的需求。通过将 AI 部署在本地，用户无需依赖云端服务即可享受快速、私密的智能辅助，真正实现了“你的数据，你做主”。其独特的技术亮点在于强大的网关架构，将控制平面与核心助手分离，确保跨平台通信的流畅性与扩展性。\n\nOpenClaw 非常适合希望构建个性化工作流的技术爱好者、开发者，以及注重隐私保护且不愿被单一生态绑定的普通用户。只要具备基础的终端操作能力（支持 macOS、Linux 及 Windows WSL2），即可通过简单的命令行引导完成部署。如果你渴望拥有一个懂你",349277,3,"2026-04-06T06:32:30",[55,33,56,57],"Agent","图像","数据工具",{"id":59,"name":60,"github_repo":61,"description_zh":62,"stars":63,"difficulty_score":52,"last_commit_at":64,"category_tags":65,"status":40},3808,"stable-diffusion-webui","AUTOMATIC1111\u002Fstable-diffusion-webui","stable-diffusion-webui 是一个基于 Gradio 构建的网页版操作界面，旨在让用户能够轻松地在本地运行和使用强大的 Stable Diffusion 图像生成模型。它解决了原始模型依赖命令行、操作门槛高且功能分散的痛点，将复杂的 AI 绘图流程整合进一个直观易用的图形化平台。\n\n无论是希望快速上手的普通创作者、需要精细控制画面细节的设计师，还是想要深入探索模型潜力的开发者与研究人员，都能从中获益。其核心亮点在于极高的功能丰富度：不仅支持文生图、图生图、局部重绘（Inpainting）和外绘（Outpainting）等基础模式，还独创了注意力机制调整、提示词矩阵、负向提示词以及“高清修复”等高级功能。此外，它内置了 GFPGAN 和 CodeFormer 等人脸修复工具，支持多种神经网络放大算法，并允许用户通过插件系统无限扩展能力。即使是显存有限的设备，stable-diffusion-webui 也提供了相应的优化选项，让高质量的 AI 艺术创作变得触手可及。",162132,"2026-04-05T11:01:52",[33,56,55],{"id":67,"name":68,"github_repo":69,"description_zh":70,"stars":71,"difficulty_score":39,"last_commit_at":72,"category_tags":73,"status":40},1381,"everything-claude-code","affaan-m\u002Feverything-claude-code","everything-claude-code 是一套专为 AI 编程助手（如 Claude Code、Codex、Cursor 等）打造的高性能优化系统。它不仅仅是一组配置文件，而是一个经过长期实战打磨的完整框架，旨在解决 AI 代理在实际开发中面临的效率低下、记忆丢失、安全隐患及缺乏持续学习能力等核心痛点。\n\n通过引入技能模块化、直觉增强、记忆持久化机制以及内置的安全扫描功能，everything-claude-code 能显著提升 AI 在复杂任务中的表现，帮助开发者构建更稳定、更智能的生产级 AI 代理。其独特的“研究优先”开发理念和针对 Token 消耗的优化策略，使得模型响应更快、成本更低，同时有效防御潜在的攻击向量。\n\n这套工具特别适合软件开发者、AI 研究人员以及希望深度定制 AI 工作流的技术团队使用。无论您是在构建大型代码库，还是需要 AI 协助进行安全审计与自动化测试，everything-claude-code 都能提供强大的底层支持。作为一个曾荣获 Anthropic 黑客大奖的开源项目，它融合了多语言支持与丰富的实战钩子（hooks），让 AI 真正成长为懂上",160411,"2026-04-18T23:33:24",[33,55,74],"语言模型",{"id":76,"name":77,"github_repo":78,"description_zh":79,"stars":80,"difficulty_score":39,"last_commit_at":81,"category_tags":82,"status":40},2271,"ComfyUI","Comfy-Org\u002FComfyUI","ComfyUI 是一款功能强大且高度模块化的视觉 AI 引擎，专为设计和执行复杂的 Stable Diffusion 图像生成流程而打造。它摒弃了传统的代码编写模式，采用直观的节点式流程图界面，让用户通过连接不同的功能模块即可构建个性化的生成管线。\n\n这一设计巧妙解决了高级 AI 绘图工作流配置复杂、灵活性不足的痛点。用户无需具备编程背景，也能自由组合模型、调整参数并实时预览效果，轻松实现从基础文生图到多步骤高清修复等各类复杂任务。ComfyUI 拥有极佳的兼容性，不仅支持 Windows、macOS 和 Linux 全平台，还广泛适配 NVIDIA、AMD、Intel 及苹果 Silicon 等多种硬件架构，并率先支持 SDXL、Flux、SD3 等前沿模型。\n\n无论是希望深入探索算法潜力的研究人员和开发者，还是追求极致创作自由度的设计师与资深 AI 绘画爱好者，ComfyUI 都能提供强大的支持。其独特的模块化架构允许社区不断扩展新功能，使其成为当前最灵活、生态最丰富的开源扩散模型工具之一，帮助用户将创意高效转化为现实。",109154,"2026-04-18T11:18:24",[33,56,55],{"id":84,"name":85,"github_repo":86,"description_zh":87,"stars":88,"difficulty_score":39,"last_commit_at":89,"category_tags":90,"status":40},6121,"gemini-cli","google-gemini\u002Fgemini-cli","gemini-cli 是一款由谷歌推出的开源 AI 命令行工具，它将强大的 Gemini 大模型能力直接集成到用户的终端环境中。对于习惯在命令行工作的开发者而言，它提供了一条从输入提示词到获取模型响应的最短路径，无需切换窗口即可享受智能辅助。\n\n这款工具主要解决了开发过程中频繁上下文切换的痛点，让用户能在熟悉的终端界面内直接完成代码理解、生成、调试以及自动化运维任务。无论是查询大型代码库、根据草图生成应用，还是执行复杂的 Git 操作，gemini-cli 都能通过自然语言指令高效处理。\n\n它特别适合广大软件工程师、DevOps 人员及技术研究人员使用。其核心亮点包括支持高达 100 万 token 的超长上下文窗口，具备出色的逻辑推理能力；内置 Google 搜索、文件操作及 Shell 命令执行等实用工具；更独特的是，它支持 MCP（模型上下文协议），允许用户灵活扩展自定义集成，连接如图像生成等外部能力。此外，个人谷歌账号即可享受免费的额度支持，且项目基于 Apache 2.0 协议完全开源，是提升终端工作效率的理想助手。",100752,"2026-04-10T01:20:03",[91,55,56,33],"插件",{"id":93,"name":94,"github_repo":95,"description_zh":96,"stars":97,"difficulty_score":39,"last_commit_at":98,"category_tags":99,"status":40},4721,"markitdown","microsoft\u002Fmarkitdown","MarkItDown 是一款由微软 AutoGen 团队打造的轻量级 Python 工具，专为将各类文件高效转换为 Markdown 格式而设计。它支持 PDF、Word、Excel、PPT、图片（含 OCR）、音频（含语音转录）、HTML 乃至 YouTube 链接等多种格式的解析，能够精准提取文档中的标题、列表、表格和链接等关键结构信息。\n\n在人工智能应用日益普及的今天，大语言模型（LLM）虽擅长处理文本，却难以直接读取复杂的二进制办公文档。MarkItDown 恰好解决了这一痛点，它将非结构化或半结构化的文件转化为模型“原生理解”且 Token 效率极高的 Markdown 格式，成为连接本地文件与 AI 分析 pipeline 的理想桥梁。此外，它还提供了 MCP（模型上下文协议）服务器，可无缝集成到 Claude Desktop 等 LLM 应用中。\n\n这款工具特别适合开发者、数据科学家及 AI 研究人员使用，尤其是那些需要构建文档检索增强生成（RAG）系统、进行批量文本分析或希望让 AI 助手直接“阅读”本地文件的用户。虽然生成的内容也具备一定可读性，但其核心优势在于为机器",93400,"2026-04-06T19:52:38",[91,33]]