Literature of Deep Learning for Graphs
This is a paper list about deep learning for graphs.
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DeepWalk: Online Learning of Social Representations <https://arxiv.org/pdf/1403.6652>
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| :authors:Bryan Perozzi, Rami Al-Rfou, Steven Skiena
| :venue:KDD 2014
| :keywords:Node classification, Random walk, Skip-gram
LINE: Large-scale Information Network Embedding <https://arxiv.org/pdf/1503.03578>
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| :authors:Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, Qiaozhu Mei
| :venue:WWW 2015
| :keywords:First-order, Second-order, Node classification
GraRep: Learning Graph Representations with Global Structural Information <https://dl.acm.org/citation.cfm?id=2806512>
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| :authors:Shaosheng Cao, Wei Lu, Qiongkai Xu
| :venue:CIKM 2015
| :keywords:High-order, SVD
node2vec: Scalable Feature Learning for Networks <https://arxiv.org/pdf/1607.00653>
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| :authors:Aditya Grover, Jure Leskovec
| :venue:KDD 2016
| :keywords:Breadth-first Search, Depth-first Search, Node Classification, Link Prediction
Variational Graph Auto-Encoders <https://arxiv.org/abs/1611.07308>
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| :authors:Thomas N. Kipf, Max Welling
| :venue:arXiv 2016
Scalable Graph Embedding for Asymmetric Proximity <https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14696>
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| :authors:Chang Zhou, Yuqiong Liu, Xiaofei Liu, Zhongyi Liu, Jun Gao
| :venue:AAAI 2017
Fast Network Embedding Enhancement via High Order Proximity Approximation <https://www.ijcai.org/proceedings/2017/544>
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| :authors:Cheng Yang, Maosong Sun, Zhiyuan Liu, Cunchao Tu
| :venue:IJCAI 2017
struc2vec: Learning Node Representations from Structural Identity <https://arxiv.org/pdf/1704.03165>
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| :authors:Leonardo F. R. Ribeiro, Pedro H. P. Savarese, Daniel R. Figueiredo
| :venue:KDD 2017
| :keywords:Structural Identity
Poincaré Embeddings for Learning Hierarchical Representations <https://arxiv.org/pdf/1705.08039>
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| :authors:Maximilian Nickel, Douwe Kiela
| :venue:NIPS 2017
VERSE: Versatile Graph Embeddings from Similarity Measures <https://arxiv.org/pdf/1803.04742>
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| :authors:Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Emmanuel Müller
| :venue:WWW 2018
Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec <https://arxiv.org/pdf/1710.02971>
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| :authors:Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, Jie Tang
| :venue:WSDM 2018
Learning Structural Node Embeddings via Diffusion Wavelets <https://arxiv.org/pdf/1710.10321>
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| :authors:Claire Donnat, Marinka Zitnik, David Hallac, Jure Leskovec
| :venue:KDD 2018
Adversarial Network Embedding <https://arxiv.org/pdf/1711.07838>
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| :authors:Quanyu Dai, Qiang Li, Jian Tang, Dan Wang
| :venue:AAAI 2018
GraphGAN: Graph Representation Learning with Generative Adversarial Nets <https://arxiv.org/pdf/1711.08267>
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| :authors:Hongwei Wang, Jia Wang, Jialin Wang, Miao Zhao, Weinan Zhang, Fuzheng Zhang, Xing Xie, Minyi Guo
| :venue:AAAI 2018
A General View for Network Embedding as Matrix Factorization <https://dl.acm.org/citation.cfm?id=3291029>
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| :authors:Xin Liu, Tsuyoshi Murata, Kyoung-Sook Kim, Chatchawan Kotarasu, Chenyi Zhuang
| :venue:WSDM 2019
Deep Graph Infomax <https://arxiv.org/pdf/1809.10341>
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| :authors:Petar Veličković, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, R Devon Hjelm
| :venue:ICLR 2019
NetSMF: Large-Scale Network Embedding as Sparse Matrix Factorization <http://keg.cs.tsinghua.edu.cn/jietang/publications/www19-Qiu-et-al-NetSMF-Large-Scale-Network-Embedding.pdf>
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| :authors:Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Chi Wang, Kuansan Wang, Jie Tang
| :venue:WWW 2019
Adversarial Training Methods for Network Embedding <https://dl.acm.org/citation.cfm?id=3313445>
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| :authors:Quanyu Dai, Xiao Shen, Liang Zhang, Qiang Li, Dan Wang
| :venue:WWW 2019
vGraph: A Generative Model for Joint Community Detection and Node Representation Learning <https://arxiv.org/pdf/1906.07159.pdf>
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| :authors:Fan-Yun Sun, Meng Qu, Jordan Hoffmann, Chin-Wei Huang, Jian Tang
| :venue:NeurIPS 2019
ProGAN: Network Embedding via Proximity Generative Adversarial Network <https://dl.acm.org/citation.cfm?id=3330866>
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| :authors:Hongchang Gao, Jian Pei, Heng Huang
| :venue:KDD 2019
GraphZoom: A Multi-level Spectral Approach for Accurate and Scalable Graph Embedding <https://openreview.net/pdf?id=r1lGO0EKDH>
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| :authors:Chenhui Deng, Zhiqiang Zhao, Yongyu Wang, Zhiru Zhang, Zhuo Feng
| :venue:ICLR 2020
Learning Latent Representations of Nodes for Classifying in Heterogeneous Social Networks <https://dl.acm.org/citation.cfm?id=2556225>
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| :authors:Yann Jacob, Ludovic Denoyer, Patrick Gallinari
| :venue:WSDM 2014
PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks <https://arxiv.org/pdf/1508.00200>
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| :authors:Jian Tang, Meng Qu, Qiaozhu Mei
| :venue:KDD 2015
| :keywords:Text Embedding, Heterogeneous Text Graphs
Heterogeneous Network Embedding via Deep Architectures <https://dl.acm.org/citation.cfm?id=2783296>
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| :authors:Shiyu Chang, Wei Han, Jiliang Tang, Guo-Jun Qi, Charu C. Aggarwal, Thomas S. Huang
| :venue:KDD 2015
Network Representation Learning with Rich Text Information <https://www.aaai.org/ocs/index.php/IJCAI/IJCAI15/paper/view/11098>
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| :authors:Cheng Yang, Zhiyuan Liu, Deli Zhao, Maosong Sun, Edward Chang
| :venue:AAAI 2015
Max-Margin DeepWalk: Discriminative Learning of Network Representation <https://www.ijcai.org/Proceedings/16/Papers/547.pdf>
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| :authors:Cunchao Tu, Weicheng Zhang, Zhiyuan Liu, Maosong Sun
| :venue:IJCAI 2016
metapath2vec: Scalable Representation Learning for Heterogeneous Networks <https://dl.acm.org/citation.cfm?id=3098036>
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| :authors:Yuxiao Dong, Nitesh V. Chawla, Ananthram Swami
| :venue:KDD 2017
Meta-Path Guided Embedding for Similarity Search in Large-Scale Heterogeneous Information Networks <https://arxiv.org/pdf/1610.09769>
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| :authors:Jingbo Shang, Meng Qu, Jialu Liu, Lance M. Kaplan, Jiawei Han, Jian Peng
| :venue:arXiv 2016
HIN2Vec: Explore Meta-paths in Heterogeneous Information Networks for Representation Learning <https://dl.acm.org/citation.cfm?id=3132953>
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| :authors:Tao-yang Fu, Wang-Chien Lee, Zhen Lei
| :venue:CIKM 2017
An Attention-based Collaboration Framework for Multi-View Network Representation Learning <https://arxiv.org/pdf/1709.06636>
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| :authors:Meng Qu, Jian Tang, Jingbo Shang, Xiang Ren, Ming Zhang, Jiawei Han
| :venue:CIKM 2017
Multi-view Clustering with Graph Embedding for Connectome Analysis <https://dl.acm.org/citation.cfm?id=3132909>
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| :authors:Guixiang Ma, Lifang He, Chun-Ta Lu, Weixiang Shao, Philip S. Yu, Alex D. Leow, Ann B. Ragin
| :venue:CIKM 2017
Attributed Signed Network Embedding <https://dl.acm.org/citation.cfm?id=3132847.3132905>
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| :authors:Suhang Wang, Charu Aggarwal, Jiliang Tang, Huan Liu
| :venue:CIKM 2017
CANE: Context-Aware Network Embedding for Relation Modeling <https://aclweb.org/anthology/papers/P/P17/P17-1158/>
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| :authors:Cunchao Tu, Han Liu, Zhiyuan Liu, Maosong Sun
| :venue:ACL 2017
PME: Projected Metric Embedding on Heterogeneous Networks for Link Prediction <https://dl.acm.org/citation.cfm?id=3219986>
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| :authors:Hongxu Chen, Hongzhi Yin, Weiqing Wang, Hao Wang, Quoc Viet Hung Nguyen, Xue Li
| :venue:KDD 2018
BiNE: Bipartite Network Embedding <https://dl.acm.org/citation.cfm?id=3209978.3209987>
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| :authors:Ming Gao, Leihui Chen, Xiangnan He, Aoying Zhou
| :venue:SIGIR 2018
StarSpace: Embed All The Things <https://arxiv.org/pdf/1709.03856>
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| :authors:Ledell Wu, Adam Fisch, Sumit Chopra, Keith Adams, Antoine Bordes, Jason Weston
| :venue:AAAI 2018
Exploring Expert Cognition for Attributed Network Embedding <https://dl.acm.org/citation.cfm?id=3159655>
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| :authors:Xiao Huang, Qingquan Song, Jundong Li, Xia Hu
| :venue:WSDM 2018
SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction <https://arxiv.org/pdf/1712.00732>
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| :authors:Hongwei Wang, Fuzheng Zhang, Min Hou, Xing Xie, Minyi Guo, Qi Liu
| :venue:WSDM 2018
Multidimensional Network Embedding with Hierarchical Structures <https://dl.acm.org/citation.cfm?id=3159680>
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| :authors:Yao Ma, Zhaochun Ren, Ziheng Jiang, Jiliang Tang, Dawei Yin
| :venue:WSDM 2018
Curriculum Learning for Heterogeneous Star Network Embedding via Deep Reinforcement Learning <https://dl.acm.org/citation.cfm?id=3159711>
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| :authors:Meng Qu, Jian Tang, Jiawei Han
| :venue:WSDM 2018
Generative Adversarial Network based Heterogeneous Bibliographic Network Representation for Personalized Citation Recommendation <https://www.semanticscholar.org/paper/Generative-Adversarial-Network-Based-Heterogeneous-Cai-Han/1596d6487012696ba400fb69904a2c372a08a2be>
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| :authors:Xiaoyan Cai, Junwei Han, Libin Yang
| :venue:AAAI 2018
ANRL: Attributed Network Representation Learning via Deep Neural Networks <https://www.ijcai.org/proceedings/2018/438>
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| :authors:Zhen Zhang, Hongxia Yang, Jiajun Bu, Sheng Zhou, Pinggang Yu, Jianwei Zhang, Martin Ester, Can Wang
| :venue:IJCAI 2018
Efficient Attributed Network Embedding via Recursive Randomized Hashing <https://www.ijcai.org/proceedings/2018/397>
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| :authors:Wei Wu, Bin Li, Ling Chen, Chengqi Zhang
| :venue:IJCAI 2018
Deep Attributed Network Embedding <https://www.ijcai.org/proceedings/2018/467>
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| :authors:Hongchang Gao, Heng Huang
| :venue:IJCAI 2018
Co-Regularized Deep Multi-Network Embedding <https://dl.acm.org/citation.cfm?id=3186113>
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| :authors:Jingchao Ni, Shiyu Chang, Xiao Liu, Wei Cheng, Haifeng Chen, Dongkuan Xu, Xiang Zhang
| :venue:WWW 2018
Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks <https://arxiv.org/pdf/1807.03490>
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| :authors:Yu Shi, Qi Zhu, Fang Guo, Chao Zhang, Jiawei Han
| :venue:KDD 2018
Meta-Graph Based HIN Spectral Embedding: Methods, Analyses, and Insights <https://www.semanticscholar.org/paper/Meta-Graph-Based-HIN-Spectral-Embedding%3A-Methods%2C-Yang-Feng/4d5f4d6785d550383e3f3afb04c3015bf0d28405>
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| :authors:Carl Yang, Yichen Feng, Pan Li, Yu Shi, Jiawei Han
| :venue:ICDM 2018
SIDE: Representation Learning in Signed Directed Networks <https://dl.acm.org/citation.cfm?id=3186117>
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| :authors:Junghwan Kim, Haekyu Park, Ji-Eun Lee, U Kang
| :venue:WWW 2018
Learning Network-to-Network Model for Content-rich Network Embedding <https://dl.acm.org/citation.cfm?id=3330924>
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| :authors:Zhicheng He, Jie Liu, Na Li, Yalou Huang
| :venue:KDD 2019
Know-evolve: Deep temporal reasoning for dynamic knowledge graphs <https://arxiv.org/pdf/1705.05742.pdf>
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| :authors:Rakshit Trivedi, Hanjun Dai, Yichen Wang, Le Song
| :venue:ICML 2017
Dyngem: Deep embedding method for dynamic graphs <https://arxiv.org/pdf/1805.11273.pdf>
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| :authors:Palash Goyal, Nitin Kamra, Xinran He, Yan Liu
| :venue:ICLR 2017 Workshop
Attributed network embedding for learning in a dynamic environment <https://arxiv.org/pdf/1706.01860.pdf>
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| :authors:Jundong Li, Harsh Dani, Xia Hu, Jiliang Tang, Yi Chang, Huan Liu
| :venue:CIKM 2017
Dynamic Network Embedding by Modeling Triadic Closure Process <http://yangy.org/works/dynamictriad/dynamic_triad.pdf>
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| :authors:Lekui Zhou, Yang Yang, Xiang Ren, Fei Wu, Yueting Zhuang
| :venue:AAAI 2018
DepthLGP: Learning Embeddings of Out-of-Sample Nodes in Dynamic Networks <https://pdfs.semanticscholar.org/9499/b38866b1eb87ae43fa5be02f9d08cd3c20a8.pdf?_ga=2.6780794.935636364.1561139530-1831876308.1523264869>
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| :authors:Jianxin Ma, Peng Cui, Wenwu Zhu
| :venue:AAAI 2018
TIMERS: Error-Bounded SVD Restart on Dynamic Networks <https://arxiv.org/pdf/1711.09541.pdf>
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| :authors:Ziwei Zhang, Peng Cui, Jian Pei, Xiao Wang, Wenwu Zhu
| :venue:AAAI 2018
Dynamic Embeddings for User Profiling in Twitter <https://dl.acm.org/citation.cfm?id=3219819.3220043>
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| :authors:Shangsong Liang, Xiangliang Zhang, Zhaochun Ren, Evangelos Kanoulas
| :venue:KDD 2018
Dynamic Network Embedding : An Extended Approach for Skip-gram based Network Embedding <https://www.ijcai.org/proceedings/2018/0288.pdf>
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| :authors:Lun Du, Yun Wang, Guojie Song, Zhicong Lu, Junshan Wang
| :venue:IJCAI 2018
DyRep: Learning Representations over Dynamic Graphs <https://openreview.net/pdf?id=HyePrhR5KX>
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| :authors:Rakshit Trivedi, Mehrdad Farajtabar, Prasenjeet Biswal, Hongyuan Zha
| :venue:ICLR 2019
Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks <https://cs.stanford.edu/~srijan/pubs/jodie-kdd2019.pdf>
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| :authors:Srijan Kumar, Xikun Zhang, Jure Leskovec
| :venue:KDD 2019
Variational Graph Recurrent Neural Networks <https://arxiv.org/pdf/1908.09710.pdf>
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| :authors:Ehsan Hajiramezanali, Arman Hasanzadeh, Nick Duffield, Krishna R Narayanan, Mingyuan Zhou, Xiaoning Qian
| :venue:NeurIPS 2019
Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks <https://arxiv.org/pdf/1907.03395.pdf>
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| :authors:Vineet Kosaraju, Amir Sadeghian, Roberto Martín-Martín, Ian Reid, S. Hamid Rezatofighi, Silvio Savarese
| :venue:NeurIPS 2019
A Three-Way Model for Collective Learning on Multi-Relational Data. <http://www.icml-2011.org/papers/438_icmlpaper.pdf>
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| :authors:Maximilian Nickel, Volker Tresp, Hans-Peter Kriegel
| :venue:ICML 2011
Translating Embeddings for Modeling Multi-relational Data <https://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data.pdf>
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| :authors:Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, Oksana Yakhnenko
| :venue:NIPS 2013
Knowledge Graph Embedding by Translating on Hyperplanes <https://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/viewFile/8531/8546>
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| :authors:Zhen Wang, Jianwen Zhang, Jianlin Feng, Zheng Chen
| :venue:AAAI 2014
Reducing the Rank of Relational Factorization Models by Including Observable Patterns <http://papers.nips.cc/paper/5448-reducing-the-rank-in-relational-factorization-models-by-including-observable-patterns.pdf>
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| :authors:Maximilian Nickel, Xueyan Jiang, Volker Tresp
| :venue:NIPS 2014
Learning Entity and Relation Embeddings for Knowledge Graph Completion <https://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/viewFile/9571/9523>
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| :authors:Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, Xuan Zhu
| :venue:AAAI 2015
A Review of Relational Machine Learning for Knowledge Graph <https://arxiv.org/pdf/1503.00759.pdf>
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| :authors:Maximilian Nickel, Kevin Murphy, Volker Tresp, Evgeniy Gabrilovich
| :venue:IEEE 2015
Knowledge Graph Embedding via Dynamic Mapping Matrix <https://www.aclweb.org/anthology/P15-1067>
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| :authors:Guoliang Ji, Shizhu He, Liheng Xu, Kang Liu, Jun Zha
| :venue:ACL 2015
Modeling Relation Paths for Representation Learning of Knowledge Bases <https://arxiv.org/pdf/1506.00379>
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| :authors:Yankai Lin, Zhiyuan Liu, Huanbo Luan, Maosong Sun, Siwei Rao, Song Liu
| :venue:EMNLP 2015
Embedding Entities and Relations for Learning and Inference in Knowledge Bases <https://arxiv.org/pdf/1412.6575>
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| :authors:Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, Li Deng
| :venue:ICLR 2015
Holographic Embeddings of Knowledge Graphs <https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/viewPDFInterstitial/12484/11828>
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| :authors:Maximilian Nickel, Lorenzo Rosasco, Tomaso Poggio
| :venue:AAAI 2016
Complex Embeddings for Simple Link Prediction <http://www.jmlr.org/proceedings/papers/v48/trouillon16.pdf>
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| :authors:Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, Guillaume Bouchard
| :venue:ICML 2016
Modeling Relational Data with Graph Convolutional Networks <https://arxiv.org/pdf/1703.06103>
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| :authors:Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, Max Welling
| :venue:arXiv 2017
Fast Linear Model for Knowledge Graph Embeddings <https://arxiv.org/pdf/1710.10881>
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| :authors:Armand Joulin, Edouard Grave, Piotr Bojanowski, Maximilian Nickel, Tomas Mikolov
| :venue:arXiv 2017
Convolutional 2D Knowledge Graph Embeddings <https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/download/17366/15884>
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| :authors:Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, Sebastian Riedel
| :venue:AAAI 2018
Knowledge Graph Embedding With Iterative Guidance From Soft Rules <https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/download/16369/16011>
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| :authors:Shu Guo, Quan Wang, Lihong Wang, Bin Wang, Li Guo
| :venue:AAAI 2018
KBGAN: Adversarial Learning for Knowledge Graph Embeddings <https://arxiv.org/abs/1711.04071>
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| :authors:Liwei Cai, William Yang Wang
| :venue:NAACL 2018
Improving Knowledge Graph Embedding Using Simple Constraints <https://arxiv.org/abs/1805.02408>
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| :authors:Boyang Ding, Quan Wang, Bin Wang, Li Guo
| :venue:ACL 2018
SimplE Embedding for Link Prediction in Knowledge Graphs <https://arxiv.org/abs/1802.04868>
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| :authors:Seyed Mehran Kazemi, David Poole
| :venue:NeurIPS 2018
A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network <https://aclweb.org/anthology/papers/N/N18/N18-2053/>
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| :authors:Dai Quoc Nguyen, Tu Dinh Nguyen, Dat Quoc Nguyen, Dinh Phung
| :venue:NAACL 2018
Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning <https://arxiv.org/abs/1903.08948>
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| :authors:Wen Zhang, Bibek Paudel, Liang Wang, Jiaoyan Chen, Hai Zhu, Wei Zhang, Abraham Bernstein, Huajun Chen
| :venue:WWW 2019
RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space <https://arxiv.org/abs/1902.10197>
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| :authors:Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, Jian Tang
| :venue:ICLR 2019
Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs <https://arxiv.org/abs/1906.01195>
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| :authors:Deepak Nathani, Jatin Chauhan, Charu Sharma, Manohar Kaul
| :venue:ACL 2019
Probabilistic Logic Neural Networks for Reasoning <https://arxiv.org/pdf/1906.08495.pdf>
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| :authors:Meng Qu, Jian Tang
| :venue:NeurIPS 2019
Quaternion Knowledge Graph Embeddings <https://arxiv.org/pdf/1904.10281.pdf>
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| :authors:Shuai Zhang, Yi Tay, Lina Yao, Qi Liu
| :venue:NeurIPS 2019
Quantum Embedding of Knowledge for Reasoning <https://papers.nips.cc/paper/8797-quantum-embedding-of-knowledge-for-reasoning.pdf>
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| :authors:Dinesh Garg, Santosh K. Srivastava, Hima Karanam
| :venue:NeurIPS 2019
Multi-relational Poincaré Graph Embeddings <https://arxiv.org/pdf/1905.09791.pdf>
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| :authors:Ivana Balaževic, Carl Allen, Timothy Hospedales
| :venue:NeurIPS 2019
Dynamically Pruned Message Passing Networks for Large-scale Knowledge Graph Reasoning <https://openreview.net/forum?id=rkeuAhVKvB>
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| :authors:Xiaoran Xu, Wei Feng, Yunsheng Jiang, Xiaohui Xie, Zhiqing Sun, Zhi-Hong Deng
| :venue:ICLR 2020
Revisiting Semi-supervised Learning with Graph Embeddings <https://arxiv.org/pdf/1603.08861>
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| :authors:Zhilin Yang, William W. Cohen, Ruslan Salakhutdinov
| :venue:ICML 2016
Semi-Supervised Classification with Graph Convolutional Networks <https://arxiv.org/pdf/1609.02907>
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| :authors:Thomas N. Kipf, Max Welling
| :venue:ICLR 2017
Neural Message Passing for Quantum Chemistry <https://arxiv.org/pdf/1704.01212>
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| :authors:Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, George E. Dahl
| :venue:ICML 2017
Motif-Aware Graph Embeddings <http://gearons.org/assets/docs/motif-aware-graph-final.pdf>
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| :authors:Hoang Nguyen, Tsuyoshi Murata
| :venue:IJCAI 2017
Learning Graph Representations with Embedding Propagation <https://arxiv.org/pdf/1710.03059>
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| :authors:Alberto Garcia-Duran, Mathias Niepert
| :venue:NIPS 2017
Inductive Representation Learning on Large Graphs <https://arxiv.org/pdf/1706.02216>
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| :authors:William L. Hamilton, Rex Ying, Jure Leskovec
| :venue:NIPS 2017
Graph Attention Networks <https://arxiv.org/pdf/1710.10903>
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| :authors:Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, Yoshua Bengio
| :venue:ICLR 2018
FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling <https://arxiv.org/pdf/1801.10247>
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| :authors:Jie Chen, Tengfei Ma, Cao Xiao
| :venue:ICLR 2018
Representation Learning on Graphs with Jumping Knowledge Networks <https://arxiv.org/pdf/1806.03536>
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| :authors:Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, Stefanie Jegelka
| :venue:ICML 2018
Stochastic Training of Graph Convolutional Networks with Variance Reduction <https://arxiv.org/pdf/1710.10568>
_
| :authors:Jianfei Chen, Jun Zhu, Le Song
| :venue:ICML 2018
Large-Scale Learnable Graph Convolutional Networks <https://arxiv.org/pdf/1808.03965>
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| :authors:Hongyang Gao, Zhengyang Wang, Shuiwang Ji
| :venue:KDD 2018
Adaptive Sampling Towards Fast Graph Representation Learning <https://papers.nips.cc/paper/7707-adaptive-sampling-towards-fast-graph-representation-learning.pdf>
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| :authors:Wenbing Huang, Tong Zhang, Yu Rong, Junzhou Huang
| :venue:NeurIPS 2018
Hierarchical Graph Representation Learning with Differentiable Pooling <https://arxiv.org/pdf/1806.08804>
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| :authors:Rex Ying, Jiaxuan You, Christopher Morris, Xiang Ren, William L. Hamilton, Jure Leskovec
| :venue:NeurIPS 2018
Bayesian Semi-supervised Learning with Graph Gaussian Processes <https://papers.nips.cc/paper/7440-bayesian-semi-supervised-learning-with-graph-gaussian-processes.pdf>
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| :authors:Yin Cheng Ng, Nicolò Colombo, Ricardo Silva
| :venue:NeurIPS 2018
Pitfalls of Graph Neural Network Evaluation <https://arxiv.org/pdf/1811.05868>
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| :authors:Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, Stephan Günnemann
| :venue:arXiv 2018
Heterogeneous Graph Attention Network <https://arxiv.org/pdf/1903.07293>
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| :authors:Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Peng Cui, P. Yu, Yanfang Ye
| :venue:WWW 2019
Bayesian graph convolutional neural networks for semi-supervised classification <https://arxiv.org/pdf/1811.11103.pdf>
_
| :authors:Yingxue Zhang, Soumyasundar Pal, Mark Coates, Deniz Üstebay
| :venue:AAAI 2019
How Powerful are Graph Neural Networks? <https://arxiv.org/pdf/1810.00826>
_
| :authors:Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka
| :venue:ICLR 2019
LanczosNet: Multi-Scale Deep Graph Convolutional Networks <https://arxiv.org/pdf/1901.01484>
_
| :authors:Renjie Liao, Zhizhen Zhao, Raquel Urtasun, Richard S. Zemel
| :venue:ICLR 2019
Graph Wavelet Neural Network <https://arxiv.org/pdf/1904.07785>
_
| :authors:Bingbing Xu, Huawei Shen, Qi Cao, Yunqi Qiu, Xueqi Cheng
| :venue:ICLR 2019
Supervised Community Detection with Line Graph Neural Networks <https://openreview.net/pdf?id=H1g0Z3A9Fm>
_
| :authors:Zhengdao Chen, Xiang Li, Joan Bruna
| :venue:ICLR 2019
Predict then Propagate: Graph Neural Networks meet Personalized PageRank <https://arxiv.org/pdf/1810.05997>
_
| :authors:Johannes Klicpera, Aleksandar Bojchevski, Stephan Günnemann
| :venue:ICLR 2019
Invariant and Equivariant Graph Networks <https://arxiv.org/pdf/1812.09902>
_
| :authors:Haggai Maron, Heli Ben-Hamu, Nadav Shamir, Yaron Lipman
| :venue:ICLR 2019
Capsule Graph Neural Network <https://openreview.net/pdf?id=Byl8BnRcYm>
_
| :authors:Zhang Xinyi, Lihui Chen
| :venue:ICLR 2019
MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing <https://arxiv.org/pdf/1905.00067>
_
| :authors:Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, Aram Galstyan
| :venue:ICML 2019
Graph U-Nets <https://arxiv.org/pdf/1905.05178>
_
| :authors:Hongyang Gao, Shuiwang Ji
| :venue:ICML 2019
Disentangled Graph Convolutional Networks <http://proceedings.mlr.press/v97/ma19a/ma19a.pdf>
_
| :authors:Jianxin Ma, Peng Cui, Kun Kuang, Xin Wang, Wenwu Zhu
| :venue:ICML 2019
GMNN: Graph Markov Neural Networks <https://arxiv.org/pdf/1905.06214>
_
| :authors:Meng Qu, Yoshua Bengio, Jian Tang
| :venue:ICML 2019
Simplifying Graph Convolutional Networks <https://arxiv.org/pdf/1902.07153>
_
| :authors:Felix Wu, Tianyi Zhang, Amauri Holanda de Souza Jr., Christopher Fifty, Tao Yu, Kilian Q. Weinberger
| :venue:ICML 2019
Position-aware Graph Neural Networks <https://arxiv.org/pdf/1906.04817>
_
| :authors:Jiaxuan You, Rex Ying, Jure Leskovec
| :venue:ICML 2019
Self-Attention Graph Pooling <https://arxiv.org/pdf/1904.08082>
_
| :authors:Junhyun Lee, Inyeop Lee, Jaewoo Kang
| :venue:ICML 2019
Relational Pooling for Graph Representations <https://arxiv.org/pdf/1903.02541>
_
| :authors:Ryan L. Murphy, Balasubramaniam Srinivasan, Vinayak Rao, Bruno Ribeiro
| :venue:ICML 2019
Graph Representation Learning via Hard and Channel-Wise Attention Networks <https://arxiv.org/pdf/1907.04652.pdf>
_
| :authors:Hongyang Gao, Shuiwang Ji
| :venue:KDD 2019
Conditional Random Field Enhanced Graph Convolutional Neural Networks <https://www.kdd.org/kdd2019/accepted-papers/view/conditional-random-field-enhanced-graph-convolutional-neural-networks>
_
| :authors:Hongchang Gao, Jian Pei, Heng Huang
| :venue:KDD 2019
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks <https://arxiv.org/abs/1905.07953>
_
| :authors:Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, Cho-Jui Hsieh
| :venue:KDD 2019
DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification <https://arxiv.org/abs/1906.02319>
_
| :authors:Jun Wu, Jingrui He, Jiejun Xu
| :venue:KDD 2019
HetGNN: Heterogeneous Graph Neural Network <https://www.kdd.org/kdd2019/accepted-papers/view/hetgnn-heterogeneous-graph-neural-network>
_
| :authors:Chuxu Zhang, Dongjin Song, Chao Huang, Ananthram Swami, Nitesh V. Chawla
| :venue:KDD 2019
Graph Recurrent Networks with Attributed Random Walks <https://dl.acm.org/citation.cfm?id=3292500.3330941>
_
| :authors:Xiao Huang, Qingquan Song, Yuening Li, Xia Hu
| :venue:KDD 2019
Graph Convolutional Networks with EigenPooling <https://arxiv.org/abs/1904.13107>
_
| :authors:Yao Ma, Suhang Wang, Charu Aggarwal, Jiliang Tang
| :venue:KDD 2019
DFNets: Spectral CNNs for Graphs with Feedback-Looped Filters <http://users.cecs.anu.edu.au/~u5170295/papers/nips-wijesinghe-2019.pdf>
_
| :authors:Asiri Wijesinghe, Qing Wang
| :venue:NeurIPS 2019
Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology <https://arxiv.org/pdf/1907.05008.pdf>
_
| :authors:Nima Dehmamy, Albert-László Barabási, Rose Yu
| :venue:NeurIPS 2019
A Flexible Generative Framework for Graph-based Semi-supervised Learning <https://arxiv.org/pdf/1905.10769.pdf>
_
| :authors:Jiaqi Ma, Weijing Tang, Ji Zhu, Qiaozhu Mei
| :venue:NeurIPS 2019
Rethinking Kernel Methods for Node Representation Learning on Graphs <https://arxiv.org/pdf/1910.02548.pdf>
_
| :authors:Yu Tian, Long Zhao, Xi Peng, Dimitris N. Metaxas
| :venue:NeurIPS 2019
Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks <https://arxiv.org/pdf/1906.02174.pdf>
_
| :authors:Sitao Luan, Mingde Zhao, Xiao-Wen Chang, Doina Precup
| :venue:NeurIPS 2019
N-Gram Graph: A Simple Unsupervised Representation for Molecules <https://arxiv.org/pdf/1806.09206.pdf>
_
| :authors:Shengchao Liu, Thevaa Chandereng, Yingyu Liang
| :venue:NeurIPS 2019
DeepGCNs: Can GCNs Go as Deep as CNNs? <https://arxiv.org/pdf/1904.03751.pdf>
_
| :authors:Guohao Li, Matthias Muller, Ali Thabet, Bernard Ghanem
| :venue:ICCV 2019
Continuous Graph Neural Networks <https://arxiv.org/pdf/1912.00967.pdf>
_
| :authors:Louis-Pascal A. C. Xhonneux, Meng Qu, Jian Tang
| :venue:arXiv 2019
Curvature Graph Network <https://openreview.net/pdf?id=BylEqnVFDB>
_
| :authors:Ze Ye, Kin Sum Liu, Tengfei Ma, Jie Gao, Chao Chen
| :venue:ICLR 2020
Memory-based Graph Networks <https://openreview.net/pdf?id=r1laNeBYPB>
_
| :authors:Amir hosein Khasahmadi, Kaveh Hassani, Parsa Moradi, Leo Lee, Quaid Morris
| :venue:ICLR 2020
Strategies for Pre-training Graph Neural Networks <https://openreview.net/pdf?id=HJlWWJSFDH>
_
| :authors:Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, Jure Leskovec
| :venue:ICLR 2020
Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling <https://www.aclweb.org/anthology/D17-1159>
_
| :authors:Diego Marcheggiani, Ivan Titov
| :venue:EMNLP 2017
Graph Convolutional Encoders for Syntax-aware Neural Machine Translation <https://www.aclweb.org/anthology/D17-1209>
_
| :authors:Joost Bastings, Ivan Titov, Wilker Aziz, Diego Marcheggiani, Khalil Sima’an
| :venue:EMNLP 2017
Graph-based Neural Multi-Document Summarization <https://www.aclweb.org/anthology/K17-1045>
_
| :authors:Michihiro Yasunaga, Rui Zhang, Kshitijh Meelu, Ayush Pareek, Krishnan Srinivasan, Dragomir Radev
| :venue:CoNLL 2017
QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension <https://arxiv.org/pdf/1804.09541.pdf>
_
| :authors:Adams Wei Yu, David Dohan, Minh-Thang Luong, Rui Zhao, Kai Chen, Mohammad Norouzi, Quoc V. Le
| :venue:ICLR 2018
A Structured Self-attentive Sentence Embedding <https://arxiv.org/pdf/1703.03130.pdf>
_
| :authors:Zhouhan Lin, Minwei Feng, Cicero Nogueira dos Santos, Mo Yu, Bing Xiang, Bowen Zhou, Yoshua Bengio
| :venue:ICLR 2018
Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering <https://aclweb.org/anthology/C18-1280>
_
| :authors:Daniil Sorokin, Iryna Gurevych
| :venue:COLING 2018
Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks <https://www.aclweb.org/anthology/N18-2078>
_
| :authors:Diego Marcheggiani, Joost Bastings, Ivan Titov
| :venue:NAACL 2018
Linguistically-Informed Self-Attention for Semantic Role Labeling <https://www.aclweb.org/anthology/D18-1548>
_
| :authors:Emma Strubell, Patrick Verga, Daniel Andor, David Weiss, Andrew McCallum
| :venue:EMNLP 2018
Graph Convolution over Pruned Dependency Trees Improves Relation Extraction <https://aclweb.org/anthology/D18-1244>
_
| :authors:Yuhao Zhang, Peng Qi, Christopher D. Manning
| :venue:EMNLP 2018
A Graph-to-Sequence Model for AMR-to-Text Generation <https://www.aclweb.org/anthology/P18-1150>
_
| :authors:Linfeng Song, Yue Zhang, Zhiguo Wang, Daniel Gildea
| :venue:ACL 2018
Graph-to-Sequence Learning using Gated Graph Neural Networks <https://www.aclweb.org/anthology/P18-1026>
_
| :authors:Daniel Beck, Gholamreza Haffari, Trevor Cohn
| :venue:ACL 2018
Graph Convolutional Networks for Text Classification <https://arxiv.org/pdf/1809.05679.pdf>
_
| :authors:Liang Yao, Chengsheng Mao, Yuan Luo
| :venue:AAAI 2019
Differentiable Perturb-and-Parse: Semi-Supervised Parsing with a Structured Variational Autoencoder <https://openreview.net/pdf?id=BJlgNh0qKQ>
_
| :authors:Caio Corro, Ivan Titov
| :venue:ICLR 2019
Structured Neural Summarization <https://arxiv.org/pdf/1811.01824.pdf>
_
| :authors:Patrick Fernandes, Miltiadis Allamanis, Marc Brockschmid
| :venue:ICLR 2019
Multi-task Learning over Graph Structures <https://arxiv.org/pdf/1811.10211.pdf>
_
| :authors:Pengfei Liu, Jie Fu, Yue Dong, Xipeng Qiu, Jackie Chi Kit Cheung
| :venue:AAAI 2019
Imposing Label-Relational Inductive Bias for Extremely Fine-Grained Entity Typing <https://arxiv.org/pdf/1903.02591.pdf>
_
| :authors:Wenhan Xiong, Jiawei Wu, Deren Lei, Mo Yu, Shiyu Chang, Xiaoxiao Guo, William Yang Wang
| :venue:NAACL 2019
Single Document Summarization as Tree Induction <https://www.aclweb.org/anthology/N19-1173>
_
| :authors:Yang Liu, Ivan Titov, Mirella Lapata
| :venue:NAACL 2019
Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks <https://arxiv.org/pdf/1903.01306.pdf>
_
| :authors:Ningyu Zhang, Shumin Deng, Zhanlin Sun, Guanying Wang, Xi Chen, Wei Zhang, Huajun Chen
| :venue:NAACL 2019
Graph Neural Networks with Generated Parameters for Relation Extraction <https://arxiv.org/pdf/1902.00756.pdf>
_
| :authors:Hao Zhu, Yankai Lin, Zhiyuan Liu, Jie Fu, Tat-seng Chua, Maosong Sun
| :venue:ACL 2019
Dynamically Fused Graph Network for Multi-hop Reasoning <https://arxiv.org/pdf/1905.06933.pdf>
_
| :authors:Yunxuan Xiao, Yanru Qu, Lin Qiu, Hao Zhou, Lei Li, Weinan Zhang, Yong Yu
| :venue:ACL 2019
Encoding Social Information with Graph Convolutional Networks for Political Perspective Detection in News Media <https://www.cs.purdue.edu/homes/dgoldwas//downloads/papers/LiG_acl_2019.pdf>
_
| :authors:Chang Li, Dan Goldwasser
| :venue:ACL 2019
Attention Guided Graph Convolutional Networks for Relation Extraction <https://arxiv.org/pdf/1906.07510.pdf>
_
| :authors:Zhijiang Guo, Yan Zhang, Wei Lu
| :venue:ACL 2019
Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks <https://arxiv.org/pdf/1809.04283.pdf>
_
| :authors:Shikhar Vashishth, Manik Bhandari, Prateek Yadav, Piyush Rai, Chiranjib Bhattacharyya, Partha Talukdar
| :venue:ACL 2019
GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction <https://tsujuifu.github.io/pubs/acl19_graph-rel.pdf>
_
| :authors:Tsu-Jui Fu, Peng-Hsuan Li, Wei-Yun Ma
| :venue:ACL 2019
Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs <https://arxiv.org/pdf/1905.07374.pdf>
_
| :authors:Ming Tu, Guangtao Wang, Jing Huang, Yun Tang, Xiaodong He, Bowen Zhou
| :venue:ACL 2019
Cognitive Graph for Multi-Hop Reading Comprehension at Scale <https://arxiv.org/pdf/1905.05460.pdf>
_
| :authors:Ming Ding, Chang Zhou, Qibin Chen, Hongxia Yang, Jie Tang
| :venue:ACL 2019
Coherent Comment Generation for Chinese Articles with a Graph-to-Sequence Model <https://arxiv.org/pdf/1906.01231.pdf>
_
| :authors:Wei Li, Jingjing Xu, Yancheng He, Shengli Yan, Yunfang Wu, Xu Sun
| :venue:ACL 2019
Matching Article Pairs with Graphical Decomposition and Convolutions <https://arxiv.org/pdf/1802.07459.pdf>
_
| :authors:Bang Liu, Di Niu, Haojie Wei, Jinghong Lin, Yancheng He, Kunfeng Lai, Yu Xu
| :venue:ACL 2019
Embedding Imputation with Grounded Language Information <https://arxiv.org/pdf/1906.03753.pdf>
_
| :authors:Ziyi Yang, Chenguang Zhu, Vin Sachidananda, Eric Darve
| :venue:ACL 2019
Encoding Social Information with Graph Convolutional Networks forPolitical Perspective Detection in News Media <https://www.aclweb.org/anthology/P19-1247.pdf>
_
| :authors:Chang Li, Dan Goldwasser
| :venue:ACL 2019
A Neural Multi-digraph Model for Chinese NER with Gazetteers <https://www.aclweb.org/anthology/P19-1141.pdf>
_
| :authors:Ruixue Ding, Pengjun Xie, Xiaoyan Zhang, Wei Lu, Linlin Li, Luo Si
| :venue:ACL 2019
Tree Communication Models for Sentiment Analysis <https://www.aclweb.org/anthology/P19-1342.pdf>
_
| :authors:Yuan Zhang, Yue Zhang
| :venue:ACL 2019
A2N: Attending to Neighbors for Knowledge Graph Inference <https://www.aclweb.org/anthology/P19-1431.pdf>
_
| :authors:Trapit Bansal, Da-Cheng Juan, Sujith Ravi, Andrew McCallum
| :venue:ACL 2019
Textbook Question Answering with Multi-modal Context Graph Understanding and Self-supervised Open-set Comprehension <https://www.aclweb.org/anthology/P19-1347.pdf>
_
| :authors:Daesik Kim, Seonhoon Kim, Nojun Kwak
| :venue:ACL 2019
Look Again at the Syntax: Relational Graph Convolutional Network for Gendered Ambiguous Pronoun Resolution <https://arxiv.org/pdf/1905.08868.pdf>
_
| :authors:Yinchuan Xu, Junlin Yang
| :venue:ACL 2019 Workshop
| :keywords:https://github.com/ianycxu/RGCN-with-BERT
Learning Graph Pooling and Hybrid Convolutional Operations for Text Representations <https://arxiv.org/pdf/1901.06965.pdf>
_
| :authors:Hongyang Gao, Yongjun Chen, Shuiwang Ji
| :venue:WWW 2019
Learning to Create Sentence Semantic Relation Graphs for Multi-Document Summarization <https://arxiv.org/pdf/1909.12231.pdf>
_
| :authors:Diego Antognini, Boi Faltings
| :venue:EMNLP 2019
Dependency-Guided LSTM-CRF for Named Entity Recognition <https://arxiv.org/pdf/1909.10148.pdf>
_
| :authors:Zhanming Jie, Wei Lu
| :venue:EMNLP 2019
Modeling Conversation Structure and Temporal Dynamics for Jointly Predicting Rumor Stance and Veracity <https://arxiv.org/pdf/1909.08211.pdf>
_
| :authors:Penghui Wei, Nan Xu, Wenji Mao
| :venue:EMNLP 2019
DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation <https://arxiv.org/pdf/1908.11540.pdf>
_
| :authors:Deepanway Ghosal, Navonil Majumder, Soujanya Poria, Niyati Chhaya, Alexander Gelbukh
| :venue:EMNLP 2019
Modeling Graph Structure in Transformer for Better AMR-to-Text Generation <https://arxiv.org/pdf/1909.00136.pdf>
_
| :authors:Jie Zhu, Junhui Li, Muhua Zhu, Longhua Qian, Min Zhang, Guodong Zhou
| :venue:EMNLP 2019
KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning <https://arxiv.org/pdf/1909.02151.pdf>
_
| :authors:Bill Yuchen Lin, Xinyue Chen, Jamin Chen, Xiang Ren
| :venue:EMNLP 2019
3D Graph Neural Networks for RGBD Semantic Segmentation <http://www.cs.toronto.edu/~rjliao/papers/iccv_2017_3DGNN.pdf>
_
| :authors:Xiaojuan Qi, Renjie Liao, Jiaya Jia, Sanja Fidler, Raquel Urtasun
| :venue:ICCV 2017
Situation Recognition With Graph Neural Networks <https://arxiv.org/abs/1708.04320>
_
| :authors:Ruiyu Li, Makarand Tapaswi, Renjie Liao, Jiaya Jia, Raquel Urtasun, Sanja Fidler
| :venue:ICCV 2017
Graph-Based Classification of Omnidirectional Images <https://arxiv.org/abs/1707.08301>
_
| :authors:Renata Khasanova, Pascal Frossard
| :venue:ICCV 2017
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition <https://arxiv.org/abs/1801.07455>
_
| :authors:Sijie Yan, Yuanjun Xiong, Dahua Lin
| :venue:AAAI 2018
Image Generation from Scene Graphs <https://arxiv.org/abs/1804.01622>
_
| :authors:Justin Johnson, Agrim Gupta, Li Fei-Fei
| :venue:CVPR 2018
FoldingNet: Point Cloud Auto-Encoder via Deep Grid Deformation <https://arxiv.org/abs/1712.07262>
_
| :authors:Yaoqing Yang, Chen Feng, Yiru Shen, Dong Tian
| :venue:CVPR 2018
PPFNet: Global Context Aware Local Features for Robust 3D Point Matching <https://arxiv.org/abs/1802.02669>
_
| :authors:Haowen Deng, Tolga Birdal, Slobodan Ilic
| :venue:CVPR 2018
Iterative Visual Reasoning Beyond Convolutions <https://arxiv.org/abs/1803.11189>
_
| :authors:Xinlei Chen, Li-Jia Li, Li Fei-Fei, Abhinav Gupta
| :venue:CVPR 2018
Surface Networks <https://arxiv.org/abs/1705.10819>
_
| :authors:Ilya Kostrikov, Zhongshi Jiang, Daniele Panozzo, Denis Zorin, Joan Bruna
| :venue:CVPR 2018
FeaStNet: Feature-Steered Graph Convolutions for 3D Shape Analysis <https://arxiv.org/abs/1706.05206>
_
| :authors:Nitika Verma, Edmond Boyer, Jakob Verbeek
| :venue:CVPR 2018
Learning to Act Properly: Predicting and Explaining Affordances From Images <https://arxiv.org/abs/1712.07576>
_
| :authors:Ching-Yao Chuang, Jiaman Li, Antonio Torralba, Sanja Fidler
| :venue:CVPR 2018
Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling <https://arxiv.org/abs/1712.06760>
_
| :authors:Yiru Shen, Chen Feng, Yaoqing Yang, Dong Tian
| :venue:CVPR 2018
Deformable Shape Completion With Graph Convolutional Autoencoders <https://arxiv.org/abs/1712.00268>
_
| :authors:Or Litany, Alex Bronstein, Michael Bronstein, Ameesh Makadia
| :venue:CVPR 2018
Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images <https://arxiv.org/abs/1804.01654>
_
| :authors:Nanyang Wang, Yinda Zhang, Zhuwen Li, Yanwei Fu, Wei Liu, Yu-Gang Jiang
| :venue:ECCV 2018
Learning Human-Object Interactions by Graph Parsing Neural Networks <https://arxiv.org/abs/1808.07962>
_
| :authors:Siyuan Qi, Wenguan Wang, Baoxiong Jia, Jianbing Shen, Song-Chun Zhu
| :venue:ECCV 2018
Generating 3D Faces using Convolutional Mesh Autoencoders <https://arxiv.org/abs/1807.10267>
_
| :authors:Anurag Ranjan, Timo Bolkart, Soubhik Sanyal, Michael J. Black
| :venue:ECCV 2018
Learning SO(3) Equivariant Representations with Spherical CNNs <https://arxiv.org/abs/1711.06721>
_
| :authors:Carlos Esteves, Christine Allen-Blanchette, Ameesh Makadia, Kostas Daniilidis
| :venue:ECCV 2018
Neural Graph Matching Networks for Fewshot 3D Action Recognition <http://openaccess.thecvf.com/content_ECCV_2018/papers/Michelle_Guo_Neural_Graph_Matching_ECCV_2018_paper.pdf>
_
| :authors:Michelle Guo, Edward Chou, De-An Huang, Shuran Song, Serena Yeung, Li Fei-Fei
| :venue:ECCV 2018
Multi-Kernel Diffusion CNNs for Graph-Based Learning on Point Clouds <https://arxiv.org/abs/1809.05370>
_
| :authors:Lasse Hansen, Jasper Diesel, Mattias P. Heinrich
| :venue:ECCV 2018
Hierarchical Video Frame Sequence Representation with Deep Convolutional Graph Network <https://arxiv.org/abs/1906.00377>
_
| :authors:Feng Mao, Xiang Wu, Hui Xue, Rong Zhang
| :venue:ECCV 2018
Graph R-CNN for Scene Graph Generation <https://arxiv.org/abs/1808.00191>
_
| :authors:Jianwei Yang, Jiasen Lu, Stefan Lee, Dhruv Batra, Devi Parikh
| :venue:ECCV 2018
Exploring Visual Relationship for Image Captioning <https://arxiv.org/abs/1809.07041>
_
| :authors:Ting Yao, Yingwei Pan, Yehao Li, Tao Mei
| :venue:ECCV 2018
Beyond Grids: Learning Graph Representations for Visual Recognition <https://papers.nips.cc/paper/8135-beyond-grids-learning-graph-representations-for-visual-recognition>
_
| :authors:Yin Li, Abhinav Gupta
| :venue:NeurIPS 2018
Learning Conditioned Graph Structures for Interpretable Visual Question Answering <https://arxiv.org/abs/1806.07243>
_
| :authors:Will Norcliffe-Brown, Efstathios Vafeias, Sarah Parisot
| :venue:NeurIPS 2018
LinkNet: Relational Embedding for Scene Graph <https://arxiv.org/abs/1811.06410>
_
| :authors:Sanghyun Woo, Dahun Kim, Donghyeon Cho, In So Kweon
| :venue:NeurIPS 2018
Flexible Neural Representation for Physics Prediction <https://arxiv.org/abs/1806.08047>
_
| :authors:Damian Mrowca, Chengxu Zhuang, Elias Wang, Nick Haber, Li Fei-Fei, Joshua B. Tenenbaum, Daniel L. K. Yamins
| :venue:NeurIPS 2018
Learning Localized Generative Models for 3D Point Clouds via Graph Convolution <https://openreview.net/forum?id=SJeXSo09FQ>
_
| :authors:Diego Valsesia, Giulia Fracastoro, Enrico Magli
| :venue:ICLR 2019
Graph-Based Global Reasoning Networks <https://arxiv.org/abs/1811.12814>
_
| :authors:Yunpeng Chen, Marcus Rohrbach, Zhicheng Yan, Shuicheng Yan, Jiashi Feng, Yannis Kalantidis
| :venue:CVPR 2019
Deep Graph Laplacian Regularization for Robust Denoising of Real Images <https://arxiv.org/abs/1807.11637>
_
| :authors:Jin Zeng, Jiahao Pang, Wenxiu Sun, Gene Cheung
| :venue:CVPR 2019
Learning Context Graph for Person Search <https://arxiv.org/abs/1904.01830>
_
| :authors:Yichao Yan, Qiang Zhang, Bingbing Ni, Wendong Zhang, Minghao Xu, Xiaokang Yang
| :venue:CVPR 2019
Graphonomy: Universal Human Parsing via Graph Transfer Learning <https://arxiv.org/abs/1904.04536>
_
| :authors:Ke Gong, Yiming Gao, Xiaodan Liang, Xiaohui Shen, Meng Wang, Liang Lin
| :venue:CVPR 2019
Masked Graph Attention Network for Person Re-Identification <http://openaccess.thecvf.com/content_CVPRW_2019/papers/TRMTMCT/Bao_Masked_Graph_Attention_Network_for_Person_Re-Identification_CVPRW_2019_paper.pdf>
_
for_Person_Re-Identification_CVPRW_2019paper.html>`
| :authors:Liqiang Bao, Bingpeng Ma, Hong Chang, Xilin Chen
| :venue:CVPR 2019
Learning to Cluster Faces on an Affinity Graph <https://arxiv.org/abs/1904.02749>
_
| :authors:Lei Yang, Xiaohang Zhan, Dapeng Chen, Junjie Yan, Chen Change Loy, Dahua Lin
| :venue:CVPR 2019
Actional-Structural Graph Convolutional Networks for Skeleton-Based Action Recognition <https://arxiv.org/abs/1904.12659>
_
| :authors:Maosen Li, Siheng Chen, Xu Chen, Ya Zhang, Yanfeng Wang, Qi Tian
| :venue:CVPR 2019
Adaptively Connected Neural Networks <https://arxiv.org/abs/1904.03579>
_
| :authors:Guangrun Wang, Keze Wang, Liang Lin
| :venue:CVPR 2019
Reasoning Visual Dialogs with Structural and Partial Observations <https://arxiv.org/abs/1904.03579>
_
| :authors:Zilong Zheng, Wenguan Wang, Siyuan Qi, Song-Chun Zhu
| :venue:CVPR 2019
MeshCNN: A Network with an Edge <https://arxiv.org/pdf/1809.05910.pdf>
_
| :authors:Rana Hanocka, Amir Hertz, Noa Fish, Raja Giryes, Shachar Fleishman, Daniel Cohen-Or
| :venue:SIGGRAPH 2019
| :keywords:https://ranahanocka.github.io/MeshCNN/
Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learning <https://arxiv.org/pdf/1908.02441.pdf>
_
| :authors:Jiwoong Park, Minsik Lee, Hyung Jin Chang, Kyuewang Lee, Jin Young Choi
| :venue:ICCV 2019
Pixel2Mesh++: Multi-View 3D Mesh Generation via Deformation <https://arxiv.org/pdf/1908.01491.pdf>
_
| :authors:Chao Wen, Yinda Zhang, Zhuwen Li, Yanwei Fu
| :venue:ICCV 2019
Learning Trajectory Dependencies for Human Motion Prediction <https://arxiv.org/pdf/1908.05436.pdf>
_
| :authors:Wei Mao, Miaomiao Liu, Mathieu Salzmann, Hongdong Li
| :venue:ICCV 2019
Graph-Based Object Classification for Neuromorphic Vision Sensing <https://arxiv.org/pdf/1908.06648.pdf>
_
| :authors:Yin Bi, Aaron Chadha, Alhabib Abbas, Eirina Bourtsoulatze, Yiannis Andreopoulos
| :venue:ICCV 2019
Fashion Retrieval via Graph Reasoning Networks on a Similarity Pyramid <https://arxiv.org/pdf/1908.11754.pdf>
_
| :authors:Zhanghui Kuang, Yiming Gao, Guanbin Li, Ping Luo, Yimin Chen, Liang Lin, Wayne Zhang
| :venue:ICCV 2019
Understanding Human Gaze Communication by Spatio-Temporal Graph Reasoning <https://arxiv.org/pdf/1909.02144.pdf>
_
| :authors:Lifeng Fan, Wenguan Wang, Siyuan Huang, Xinyu Tang, Song-Chun Zhu
| :venue:ICCV 2019
Visual Semantic Reasoning for Image-Text Matching <https://arxiv.org/pdf/1909.02701.pdf>
_
| :authors:Kunpeng Li, Yulun Zhang, Kai Li, Yuanyuan Li, Yun Fu
| :venue:ICCV 2019
Graph Convolutional Networks for Temporal Action Localization <https://arxiv.org/pdf/1909.03252.pdf>
_
| :authors:Runhao Zeng, Wenbing Huang, Mingkui Tan, Yu Rong, Peilin Zhao, Junzhou Huang, Chuang Gan
| :venue:ICCV 2019
Semantically-Regularized Logic Graph Embeddings <https://arxiv.org/pdf/1909.01161.pdf>
_
| :authors:Yaqi Xie, Ziwei Xu, Kuldeep Meel, Mohan S Kankanhalli, Harold Soh
| :venue:NeurIPS 2019
Graph Convolutional Neural Networks for Web-Scale Recommender Systems <https://arxiv.org/pdf/1806.01973.pdf>
_
| :authors:Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, Jure Leskovec
| :venue:KDD 2018
| :keywords:PinSage
SocialGCN: An Efficient Graph Convolutional Network based Model for Social Recommendation <https://arxiv.org/pdf/1811.02815.pdf>
_
| :authors:Le Wu, Peijie Sun, Richang Hong, Yanjie Fu, Xiting Wang, Meng Wang
| :venue:AAAI 2018
| :keywords:GCN, Social recommendation
Session-based Social Recommendation via Dynamic Graph Attention Networks <https://arxiv.org/pdf/1902.09362.pdf>
_
| :authors:Weiping Song, Zhiping Xiao, Yifan Wang, Laurent Charlin, Ming Zhang, Jian Tang
| :venue:WSDM 2019
| :keywords:Social recommendation, session-based, GAT
Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems <https://arxiv.org/pdf/1903.10433.pdf>
_
| :authors:Qitian Wu, Hengrui Zhang, Xiaofeng Gao, Peng He, Paul Weng, Han Gao, Guihai Chen
| :venue:WWW 2019
| :keywords:Social recommendation, GAT
Graph Neural Networks for Social Recommendation <https://arxiv.org/pdf/1902.07243.pdf>
_
| :authors:Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, Dawei Yin
| :venue:WWW 2019
| :keywords:Social recommendation, GNN
Session-based Recommendation with Graph Neural Networks <https://arxiv.org/pdf/1811.00855.pdf>
_
| :authors:Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, Tieniu Tan
| :venue:AAAI 2019
| :keywords:Session-based recommendation, GNN
A Neural Influence Diffusion Model for Social Recommendation <https://arxiv.org/pdf/1904.10322.pdf>
_
| :authors:Le Wu, Peijie Sun, Yanjie Fu, Richang Hong, Xiting Wang, Meng Wang
| :venue:SIGIR 2019
| :keywords:Social Recommendation, diffusion
Neural Graph Collaborative Filtering <https://arxiv.org/pdf/1905.08108.pdf>
_
| :authors:Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, Tat-Seng Chua
| :venue:SIGIR 2019
| :keywords:Collaborative Filtering, GNN
Binarized Collaborative Filtering with Distilling Graph Convolutional Networks <https://arxiv.org/pdf/1906.01829.pdf>
_
| :authors:Haoyu Wang, Defu Lian, Yong Ge
| :venue:IJCAI 2019
IntentGC: A Scalable Graph Convolution Framework Fusing Heterogeneous Information for Recommendation <https://dl.acm.org/citation.cfm?id=3330686>
_
| :authors:Jun Zhao, Zhou Zhou, Ziyu Guan, Wei Zhao, Wei Ning, Guang Qiu, Xiaofei He
| :venue:KDD 2019
An End-to-End Neighborhood-based Interaction Model for Knowledge-enhanced Recommendation <https://arxiv.org/pdf/1908.04032.pdf>
_
| :authors:Yanru Qu, Ting Bai, Weinan Zhang, Jianyun Nie, Jian Tang
| :venue:KDD 2019 Workshop
Link Prediction Based on Graph Neural Networks <https://papers.nips.cc/paper/7763-link-prediction-based-on-graph-neural-networks.pdf>
_
| :authors:Muhan Zhang, Yixin Chen
| :venue:NeurIPS 2018
Link Prediction via Subgraph Embedding-Based Convex Matrix Completion <http://iiis.tsinghua.edu.cn/~weblt/papers/link-prediction-subgraphembeddings.pdf>
_
| :authors:Zhu Cao, Linlin Wang, Gerard de Melo
| :venue:AAAI 2018
Graph Convolutional Matrix Completion <https://www.kdd.org/kdd2018/files/deep-learning-day/DLDay18_paper_32.pdf>
_
| :authors:Rianne van den Berg, Thomas N. Kipf, Max Welling
| :venue:KDD 2018 Workshop
Semi-Implicit Graph Variational Auto-Encoders <https://arxiv.org/pdf/1908.07078.pdf>
_
| :authors:Arman Hasanzadeh, Ehsan Hajiramezanali, Nick Duffield , Krishna Narayanan, Mingyuan Zhou, Xiaoning Qian
| :venue:NeurIPS 2019
DeepInf: Social Influence Prediction with Deep Learning <https://arxiv.org/pdf/1807.05560.pdf>
_
| :authors:Jiezhong Qiu, Jian Tang, Hao Ma, Yuxiao Dong, Kuansan Wang, Jie Tang
| :venue:KDD 2018
Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks <https://arxiv.org/pdf/1905.08865.pdf>
_
| :authors:Namyong Park, Andrey Kan, Xin Luna Dong, Tong Zhao, Christos Faloutsos
| :venue:KDD 2019
Graph HyperNetworks for Neural Architecture Search <https://openreview.net/pdf?id=rkgW0oA9FX>
_
| :authors:Chris Zhang, Mengye Ren, Raquel Urtasun
| :venue:ICLR 2019
D-VAE: A Variational Autoencoder for Directed Acyclic Graphs <https://arxiv.org/pdf/1904.11088.pdf>
_
| :authors:Muhan Zhang, Shali Jiang, Zhicheng Cui, Roman Garnett, Yixin Chen
| :venue:NeurIPS 2019
Action Schema Networks: Generalised Policies with Deep Learning <https://arxiv.org/pdf/1709.04271.pdf>
_
| :authors:Sam Toyer, Felipe Trevizan, Sylvie Thiebaux, Lexing Xie
| :venue:AAAI 2018
NerveNet: Learning Structured Policy with Graph Neural Networks <https://openreview.net/pdf?id=S1sqHMZCb>
_
| :authors:Tingwu Wang, Renjie Liao, Jimmy Ba, Sanja Fidler
| :venue:ICLR 2018
Graph Networks as Learnable Physics Engines for Inference and Control <https://arxiv.org/pdf/1806.01242.pdf>
_
| :authors:Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg, Josh Merel, Martin Riedmiller
| :venue:ICML 2018
Learning Policy Representations in Multiagent Systems <https://arxiv.org/pdf/1806.06464.pdf>
_
| :authors:Aditya Grover, Maruan Al-Shedivat, Jayesh K. Gupta, Yura Burda, Harrison Edwards
| :venue:ICML 2018
Relational recurrent neural networks <https://papers.nips.cc/paper/7960-relational-recurrent-neural-networks.pdf>
_
| :authors:Adam Santoro, Ryan Faulkner, David Raposo, Jack Rae, Mike Chrzanowski,Théophane Weber, Daan Wierstra, Oriol Vinyals, Razvan Pascanu, Timothy Lillicrap
| :venue:NeurIPS 2018
Transfer of Deep Reactive Policies for MDP Planning <http://www.cse.iitd.ac.in/~mausam/papers/nips18.pdf>
_
| :authors:Aniket Bajpai, Sankalp Garg, Mausam
| :venue:NeurIPS 2018
Neural Graph Evolution: Towards Efficient Automatic Robot Design <https://openreview.net/pdf?id=BkgWHnR5tm>
_
| :authors:Tingwu Wang, Yuhao Zhou, Sanja Fidler, Jimmy Ba
| :venue:ICLR 2019
No Press Diplomacy: Modeling Multi-Agent Gameplay <https://arxiv.org/pdf/1909.02128.pdf>
_
| :authors:Philip Paquette, Yuchen Lu, Steven Bocco, Max O. Smith, Satya Ortiz-Gagne, Jonathan K. Kummerfeld, Satinder Singh, Joelle Pineau, Aaron Courville
| :venue:NeurIPS 2019
Learning Combinatorial Optimization Algorithms over Graphs <https://arxiv.org/abs/1704.01665>
_
| :authors:Hanjun Dai, Elias B. Khalil, Yuyu Zhang, Bistra Dilkina, Le Song
| :venue:NeurIPS 2017
Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search <https://arxiv.org/abs/1810.10659>
_
| :authors:Zhuwen Li, Qifeng Chen, Vladlen Koltun
| :venue:NeurIPS 2018
Reinforcement Learning for Solving the Vehicle Routing Problem <https://arxiv.org/abs/1802.04240>
_
| :authors:Mohammadreza Nazari, Afshin Oroojlooy, Lawrence V. Snyder, Martin Takáč
| :venue:NeurIPS 2018
Attention, Learn to Solve Routing Problems! <https://arxiv.org/abs/1803.08475>
_
| :authors:Wouter Kool, Herke van Hoof, Max Welling
| :venue:ICLR 2019
Learning a SAT Solver from Single-Bit Supervision <https://arxiv.org/abs/1802.03685>
_
| :authors:Daniel Selsam, Matthew Lamm, Benedikt Bünz, Percy Liang, Leonardo de Moura, David L. Dill
| :venue:ICLR 2019
An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem <https://arxiv.org/abs/1906.01227>
_
| :authors:Chaitanya K. Joshi, Thomas Laurent, Xavier Bresson
| :venue:arXiv 2019
Approximation Ratios of Graph Neural Networks for Combinatorial Problems <https://arxiv.org/pdf/1905.10261.pdf>
_
| :authors:Ryoma Sato, Makoto Yamada, Hisashi Kashima
| :venue:NeurIPS 2019
Exact Combinatorial Optimization with Graph Convolutional Neural Networks <https://arxiv.org/pdf/1906.01629.pdf>
_
| :authors:Maxime Gasse, Didier Chételat, Nicola Ferroni, Laurent Charlin, Andrea Lodi
| :venue:NeurIPS 2019
On Learning Paradigms for the Travelling Salesman Problem <https://arxiv.org/pdf/1910.07210.pdf>
_
| :authors:Chaitanya K. Joshi, Thomas Laurent, Xavier Bresson
| :venue:NeurIPS 2019 Workshop
Adversarial Attack on Graph Structured Data <https://arxiv.org/abs/1806.02371>
_
| :authors:Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, Le Song
| :venue:ICML 2018
Adversarial Attacks on Neural Networks for Graph Data <https://arxiv.org/abs/1805.07984>
_
| :authors:Daniel Zügner, Amir Akbarnejad, Stephan Günnemann
| :venue:KDD 2018
Adversarial Attacks on Graph Neural Networks via Meta Learning <https://arxiv.org/abs/1902.08412>
_
| :authors:Daniel Zügner, Stephan Günnemann
| :venue:ICLR 2019
Robust Graph Convolutional Networks Against Adversarial Attacks <http://pengcui.thumedialab.com/papers/RGCN.pdf>
_
| :authors:Dingyuan Zhu, Ziwei Zhang, Peng Cui, Wenwu Zhu
| :venue:KDD 2019
Certifiable Robustness and Robust Training for Graph Convolutional Networks <https://arxiv.org/pdf/1906.12269.pdf>
_
| :authors:Daniel Zügner, Stephan Günnemann
| :venue:KDD 2019
REGAL: Representation Learning-based Graph Alignment <https://arxiv.org/pdf/1802.06257.pdf>
_
| :authors:Mark Heimann, Haoming Shen, Tara Safavi, Danai Koutra
| :venue:CIKM 2018
Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks <https://www.aclweb.org/anthology/D18-1032.pdf>
_
| :authors:Zhichun Wang, Qingsong Lv, Xiaohan Lan, Yu Zhang
| :venue:EMNLP 2018
Learning Combinatorial Embedding Networks for Deep Graph Matching <http://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Learning_Combinatorial_Embedding_Networks_for_Deep_Graph_Matching_ICCV_2019_paper.pdf>
_
| :authors:Runzhong Wang, Junchi Yan, Xiaokang Yang
| :venue:ICCV 2019
Deep Graph Matching Consensus <https://openreview.net/pdf?id=HyeJf1HKvS>
_
| :authors:Matthias Fey, Jan E. Lenssen, Christopher Morris, Jonathan Masci, Nils M. Kriege
| :venue:ICLR 2020
Few-Shot Learning with Graph Neural Networks <https://arxiv.org/abs/1711.04043>
_
| :authors:Victor Garcia, Joan Bruna
| :venue:ICLR 2018
Learning Steady-States of Iterative Algorithms over Graphs <http://proceedings.mlr.press/v80/dai18a.html>
_
| :authors:Hanjun Dai, Zornitsa Kozareva, Bo Dai, Alex Smola, Le Song
| :venue:ICML 2018
Learning to Propagate for Graph Meta-Learning <https://arxiv.org/pdf/1909.05024.pdf>
_
| :authors:Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang
| :venue:NeurIPS 2019
Few-Shot Learning on Graphs via Super-Classes based on Graph Spectral Measures <https://openreview.net/forum?id=Bkeeca4Kvr>
_
| :authors:Jatin Chauhan, Deepak Nathani, Manohar Kaul
| :venue:ICLR 2020
Automated Relational Meta-learning <https://openreview.net/pdf?id=rklp93EtwH>
_
| :authors:Huaxiu Yao, Xian Wu, Zhiqiang Tao, Yaliang Li, Bolin Ding, Ruirui Li, Zhenhui Li
| :venue:ICLR 2020
Neural Relational Inference for Interacting Systems <https://arxiv.org/abs/1802.04687>
_
| :authors:Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, Richard Zemel
| :venue:ICML 2018
Brain Signal Classification via Learning Connectivity Structure <https://arxiv.org/abs/1905.11678>
_
| :authors:Soobeom Jang, Seong-Eun Moon, Jong-Seok Lee
| :venue:arXiv 2019
A Flexible Generative Framework for Graph-based Semi-supervised Learning <https://arxiv.org/abs/1905.10769>
_
| :authors:Jiaqi Ma, Weijing Tang, Ji Zhu, Qiaozhu Mei
| :venue:NeurIPS 2019
Joint embedding of structure and features via graph convolutional networks <https://arxiv.org/abs/1905.08636>
_
| :authors:Sébastien Lerique, Jacob Levy Abitbol, Márton Karsai
| :venue:arXiv 2019
Variational Spectral Graph Convolutional Networks <https://arxiv.org/abs/1906.01852>
_
| :authors:Louis Tiao, Pantelis Elinas, Harrison Nguyen, Edwin V. Bonilla
| :venue:arXiv 2019
Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning <https://arxiv.org/abs/1805.10002>
_
| :authors:Yanbin Liu, Juho Lee, Minseop Park, Saehoon Kim, Eunho Yang, Sung Ju Hwang, Yi Yang
| :venue:ICLR 2019
Graph Learning Network: A Structure Learning Algorithm <https://arxiv.org/abs/1905.12665>
_
| :authors:Darwin Saire Pilco, Adín Ramírez Rivera
| :venue:ICML 2019 Workshop
Learning Discrete Structures for Graph Neural Networks <https://arxiv.org/abs/1903.11960>
_
| :authors:Luca Franceschi, Mathias Niepert, Massimiliano Pontil, Xiao He
| :venue:ICML 2019
Graphite: Iterative Generative Modeling of Graphs <https://arxiv.org/abs/1803.10459>
_
| :authors:Aditya Grover, Aaron Zweig, Stefano Ermon
| :venue:ICML 2019
Protein Interface Prediction using Graph Convolutional Networks <https://papers.nips.cc/paper/7231-protein-interface-prediction-using-graph-convolutional-networks.pdf>
_
| :authors:Alex Fout, Jonathon Byrd, Basir Shariat, Asa Ben-Hur
| :venue:NeurIPS 2017
Modeling Polypharmacy Side Effects with Graph Convolutional Networks <https://arxiv.org/abs/1802.00543>
_
| :authors:Marinka Zitnik, Monica Agrawal, Jure Leskovec
| :venue:Bioinformatics 2018
NeoDTI: Neural Integration of Neighbor Information from a Heterogeneous Network for Discovering New Drug–target Interactions <https://academic.oup.com/bioinformatics/article-abstract/35/1/104/5047760?redirectedFrom=fulltext>
_
| :authors:Fangping Wan, Lixiang Hong, An Xiao, Tao Jiang, Jianyang Zeng
| :venue:Bioinformatics 2018
SELFIES: a Robust Representation of Semantically Constrained Graphs with an Example Application in Chemistry <https://arxiv.org/pdf/1905.13741.pdf>
_
| :authors:Mario Krenn, Florian Häse, AkshatKumar Nigam, Pascal Friederich, Alán Aspuru-Guzik
| :venue:arXiv 2019
Drug-Drug Adverse Effect Prediction with Graph Co-Attention <https://arxiv.org/pdf/1905.00534.pdf>
_
| :authors:Andreea Deac, Yu-Hsiang Huang, Petar Veličković, Pietro Liò, Jian Tang
| :venue:ICML 2019 Workshop
GCN-MF: Disease-Gene Association Identification By Graph Convolutional Networks and Matrix Factorization <https://www.kdd.org/kdd2019/accepted-papers/view/gcn-mf-disease-gene-association-identification-by-graph-convolutional-netwo>
_
| :authors:Peng Han, Peng Yang, Peilin Zhao, Shuo Shang, Yong Liu, Jiayu Zhou, Xin Gao, Panos Kalnis
| :venue:KDD 2019
Detecting drug-drug interactions using artificial neural networks and classic graph similarity measures <https://arxiv.org/pdf/1903.04571.pdf>
_
| :authors:Guy Shtar, Lior Rokach, Bracha Shapira
| :venue:arXiv 2019
PGCN: Disease gene prioritization by disease and gene embedding through graph convolutional neural networks <https://www.biorxiv.org/content/biorxiv/early/2019/01/28/532226.full.pdf>
_
| :authors:Yu Li, Hiroyuki Kuwahara, Peng Yang, Le Song, Xin Gao
| :venue:bioRxiv 2019
Identifying Protein-Protein Interaction using Tree LSTM and Structured Attention <https://ieeexplore.ieee.org/abstract/document/8665584>
_
| :authors:Mahtab Ahmed, Jumayel Islam, Muhammad Rifayat Samee, Robert E. Mercer
| :venue:ICSC 2019
GCN-MF: Disease-Gene Association Identification By Graph Convolutional Networks and Matrix Factorization <https://dl.acm.org/citation.cfm?id=3330912>
_
| :authors:Peng Han, Peng Yang, Peilin Zhao, Shuo Shang, Yong Liu, Jiayu Zhou, Xin Gao, Panos Kalnis
| :venue:KDD 2019
Towards perturbation prediction of biological networks using deep learning <https://www.nature.com/articles/s41598-019-48391-y>
_
| :authors:Diya Li, Jianxi Gao
| :venue:Nature 2019
Directional Message Passing for Molecular Graphs <https://openreview.net/pdf?id=B1eWbxStPH>
_
| :authors:Johannes Klicpera, Janek Groß, Stephan Günnemann
| :venue:ICLR 2020
Neural Execution of Graph Algorithms <https://openreview.net/pdf?id=SkgKO0EtvS>
_
| :authors:Petar Veličković, Rex Ying, Matilde Padovano, Raia Hadsell, Charles Blundell
| :venue:ICLR 2020
Premise Selection for Theorem Proving by Deep Graph Embedding <https://arxiv.org/abs/1709.09994>
_
| :authors:Mingzhe Wang, Yihe Tang, Jian Wang, Jia Deng
| :venue:NeurIPS 2017
GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models <https://arxiv.org/abs/1802.08773>
_
| :authors:Jiaxuan You, Rex Ying, Xiang Ren, William L. Hamilton, Jure Leskovec
| :venue:ICML 2018
NetGAN: Generating Graphs via Random Walks <https://arxiv.org/abs/1803.00816>
_
| :authors:Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, Stephan Günnemann
| :venue:ICML 2018
Learning Deep Generative Models of Graphs <https://arxiv.org/abs/1803.03324>
_
| :authors:Yujia Li, Oriol Vinyals, Chris Dyer, Razvan Pascanu, Peter Battaglia
| :venue:ICML 2018
Junction Tree Variational Autoencoder for Molecular Graph Generation <https://arxiv.org/abs/1802.04364>
_
| :authors:Wengong Jin, Regina Barzilay, Tommi Jaakkola
| :venue:ICML 2018
MolGAN: An implicit generative model for small molecular graphs <https://arxiv.org/abs/1805.11973>
_
| :authors:Nicola De Cao, Thomas Kipf
| :venue:arXiv 2018
Generative Modeling for Protein Structures <https://papers.nips.cc/paper/7978-generative-modeling-for-protein-structures.pdf>
_
| :authors:Namrata Anand, Po-Ssu Huang
| :venue:NeurIPS 2018
Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders <https://arxiv.org/abs/1809.02630>
_
| :authors:Tengfei Ma, Jie Chen, Cao Xiao
| :venue:NeurIPS 2018
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation <https://arxiv.org/abs/1806.02473>
_
| :authors:Jiaxuan You, Bowen Liu, Rex Ying, Vijay Pande, Jure Leskovec
| :venue:NeurIPS 2018
Constrained Graph Variational Autoencoders for Molecule Design <https://arxiv.org/abs/1805.09076>
_
| :authors:Qi Liu, Miltiadis Allamanis, Marc Brockschmidt, Alexander L. Gaunt
| :venue:NeurIPS 2018
Learning Multimodal Graph-to-Graph Translation for Molecule Optimization <https://arxiv.org/abs/1812.01070>
_
| :authors:Wengong Jin, Kevin Yang, Regina Barzilay, Tommi Jaakkola
| :venue:ICLR 2019
Generative Code Modeling with Graphs <https://openreview.net/forum?id=Bke4KsA5FX>
_
| :authors:Marc Brockschmidt, Miltiadis Allamanis, Alexander L. Gaunt, Oleksandr Polozov
| :venue:ICLR 2019
DAG-GNN: DAG Structure Learning with Graph Neural Networks <https://arxiv.org/abs/1904.10098>
_
| :authors:Yue Yu, Jie Chen, Tian Gao, Mo Yu
| :venue:ICML 2019
Graph to Graph: a Topology Aware Approach for Graph Structures Learning and Generation <http://proceedings.mlr.press/v89/sun19c.html>
_
| :authors:Mingming Sun, Ping Li
| :venue:AISTATS 2019
Graph Normalizing Flows <https://arxiv.org/abs/1905.13177>
_
| :authors:Jenny Liu, Aviral Kumar, Jimmy Ba, Jamie Kiros, Kevin Swersky
| :venue:NeurIPS 2019
Conditional Structure Generation through Graph Variational Generative Adversarial Nets <http://jiyang3.web.engr.illinois.edu/files/condgen.pdf>
_
| :authors:Carl Yang, Peiye Zhuang, Wenhan Shi, Alan Luu, Pan Li
| :venue:NeurIPS 2019
Efficient Graph Generation with Graph Recurrent Attention Networks <https://arxiv.org/pdf/1910.00760.pdf>
_
| :authors:Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, Charlie Nash, William L. Hamilton, David Duvenaud, Raquel Urtasun, Richard Zemel
| :venue:NeurIPS 2019
GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation <https://openreview.net/pdf?id=S1esMkHYPr>
_
| :authors:Chence Shi, Minkai Xu, Zhaocheng Zhu, Weinan Zhang, Ming Zhang, Jian Tang
| :venue:ICLR 2020
Visualizing Data using t-SNE <http://www.jmlr.org/papers/volume9/vandermaaten08a/vandermaaten08a.pdf>
_
| :authors:Laurens van der Maaten, Geoffrey Hinton
| :venue:JMLR 2008
Visualizing non-metric similarities in multiple maps <https://link.springer.com/content/pdf/10.1007/s10994-011-5273-4.pdf>
_
| :authors:Laurens van der Maaten, Geoffrey Hinton
| :venue:ML 2012
Visualizing Large-scale and High-dimensional Data <https://arxiv.org/pdf/1602.00370>
_
| :authors:Jian Tang, Jingzhou Liu, Ming Zhang, Qiaozhu Mei
| :venue:WWW 2016
GraphTSNE: A Visualization Technique for Graph-Structured Data <https://arxiv.org/pdf/1904.06915.pdf>
_
| :authors:Yao Yang Leow, Thomas Laurent, Xavier Bresson
| :venue:ICLR 2019 Workshop
GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding <https://arxiv.org/pdf/1903.00757>
_
| :authors:Zhaocheng Zhu, Shizhen Xu, Meng Qu, Jian Tang
| :venue:WWW 2019
PyTorch-BigGraph: A Large-scale Graph Embedding System <https://arxiv.org/pdf/1903.12287>
_
| :authors:Adam Lerer, Ledell Wu, Jiajun Shen, Timothee Lacroix, Luca Wehrstedt, Abhijit Bose, Alex Peysakhovich
| :venue:SysML 2019
AliGraph: A Comprehensive Graph Neural Network Platform <https://arxiv.org/pdf/1902.08730>
_
| :authors:Rong Zhu, Kun Zhao, Hongxia Yang, Wei Lin, Chang Zhou, Baole Ai, Yong Li, Jingren Zhou
| :venue:VLDB 2019
Deep Graph Library <https://www.dgl.ai>
_
| :authors:DGL Team
AmpliGraph <https://github.com/Accenture/AmpliGraph>
_
| :authors:Luca Costabello, Sumit Pai, Chan Le Van, Rory McGrath, Nicholas McCarthy, Pedro Tabacof
Euler <https://github.com/alibaba/euler>
_
| :authors:Alimama Engineering Platform Team, Alimama Search Advertising Algorithm Team
ATOMIC: an atlas of machine commonsense for if-then reasoning <https://wvvw.aaai.org/ojs/index.php/AAAI/article/download/4160/4038>
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| :authors:Maarten Sap, Ronan Le Bras, Emily Allaway, Chandra Bhagavatula, Nicholas Lourie, Hannah Rashkin, Brendan Roof, Noah A. Smith, Yejin Choi
| :venue:AAAI 2019