https://github.com/DeepGraphLearning/LiteratureDL4Graph
A comprehensive collection of recent papers on graph deep learning
https://github.com/DeepGraphLearning/LiteratureDL4Graph
arxiv deep-learning machine-learning papers
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A comprehensive collection of recent papers on graph deep learning
- Host: GitHub
- URL: https://github.com/DeepGraphLearning/LiteratureDL4Graph
- Owner: DeepGraphLearning
- License: mit
- Created: 2019-06-20T06:38:05.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2020-12-20T23:21:04.000Z (over 4 years ago)
- Last Synced: 2024-07-31T19:17:32.751Z (9 months ago)
- Topics: arxiv, deep-learning, machine-learning, papers
- Homepage:
- Size: 187 KB
- Stars: 3,064
- Watchers: 191
- Forks: 565
- Open Issues: 5
-
Metadata Files:
- Readme: README.rst
- License: LICENSE
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- data-science-awesome-reference - Literature of Deep Learning for Graphs
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- awesome-machine-learning-resources - **[List
- awesome-ai-datascience - LiteratureDL4Graph, A comprehensive collection of recent papers on graph deep learning
README
Literature of Deep Learning for Graphs
**************************************
This is a paper list about deep learning for graphs.
.. raw:: html
.. contents::
:local:
:depth: 2
.. sectnum::
:depth: 2
.. role:: authors(emphasis)
.. role:: venue(strong)
.. role:: keywords(emphasis)
Node Representation Learning
============================
Unsupervised Node Representation Learning
-----------------------------------------
`DeepWalk: Online Learning of Social Representations
`_
| :authors:`Bryan Perozzi, Rami Al-Rfou, Steven Skiena`
| :venue:`KDD 2014`
| :keywords:`Node classification, Random walk, Skip-gram`
`LINE: Large-scale Information Network Embedding
`_
| :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
`_
| :authors:`Shaosheng Cao, Wei Lu, Qiongkai Xu`
| :venue:`CIKM 2015`
| :keywords:`High-order, SVD`
`node2vec: Scalable Feature Learning for Networks
`_
| :authors:`Aditya Grover, Jure Leskovec`
| :venue:`KDD 2016`
| :keywords:`Breadth-first Search, Depth-first Search, Node Classification, Link Prediction`
`Variational Graph Auto-Encoders
`_
| :authors:`Thomas N. Kipf, Max Welling`
| :venue:`arXiv 2016`
`Scalable Graph Embedding for Asymmetric Proximity
`_
| :authors:`Chang Zhou, Yuqiong Liu, Xiaofei Liu, Zhongyi Liu, Jun Gao`
| :venue:`AAAI 2017`
`Fast Network Embedding Enhancement via High Order Proximity Approximation
`_
| :authors:`Cheng Yang, Maosong Sun, Zhiyuan Liu, Cunchao Tu`
| :venue:`IJCAI 2017`
`struc2vec: Learning Node Representations from Structural Identity
`_
| :authors:`Leonardo F. R. Ribeiro, Pedro H. P. Savarese, Daniel R. Figueiredo`
| :venue:`KDD 2017`
| :keywords:`Structural Identity`
`Poincaré Embeddings for Learning Hierarchical Representations
`_
| :authors:`Maximilian Nickel, Douwe Kiela`
| :venue:`NIPS 2017`
`VERSE: Versatile Graph Embeddings from Similarity Measures
`_
| :authors:`Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Emmanuel Müller`
| :venue:`WWW 2018`
`Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec
`_
| :authors:`Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, Jie Tang`
| :venue:`WSDM 2018`
`Learning Structural Node Embeddings via Diffusion Wavelets
`_
| :authors:`Claire Donnat, Marinka Zitnik, David Hallac, Jure Leskovec`
| :venue:`KDD 2018`
`Adversarial Network Embedding
`_
| :authors:`Quanyu Dai, Qiang Li, Jian Tang, Dan Wang`
| :venue:`AAAI 2018`
`GraphGAN: Graph Representation Learning with Generative Adversarial Nets
`_
| :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
`_
| :authors:`Xin Liu, Tsuyoshi Murata, Kyoung-Sook Kim, Chatchawan Kotarasu, Chenyi Zhuang`
| :venue:`WSDM 2019`
`Deep Graph Infomax
`_
| :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
`_
| :authors:`Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Chi Wang, Kuansan Wang, Jie Tang`
| :venue:`WWW 2019`
`Adversarial Training Methods for Network Embedding
`_
| :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
`_
| :authors:`Fan-Yun Sun, Meng Qu, Jordan Hoffmann, Chin-Wei Huang, Jian Tang`
| :venue:`NeurIPS 2019`
`ProGAN: Network Embedding via Proximity Generative Adversarial Network
`_
| :authors:`Hongchang Gao, Jian Pei, Heng Huang`
| :venue:`KDD 2019`
`GraphZoom: A Multi-level Spectral Approach for Accurate and Scalable Graph Embedding
`_
| :authors:`Chenhui Deng, Zhiqiang Zhao, Yongyu Wang, Zhiru Zhang, Zhuo Feng`
| :venue:`ICLR 2020`
Node Representation Learning in Heterogeneous Graphs
----------------------------------------------------
`Learning Latent Representations of Nodes for Classifying in Heterogeneous Social Networks
`_
| :authors:`Yann Jacob, Ludovic Denoyer, Patrick Gallinari`
| :venue:`WSDM 2014`
`PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks
`_
| :authors:`Jian Tang, Meng Qu, Qiaozhu Mei`
| :venue:`KDD 2015`
| :keywords:`Text Embedding, Heterogeneous Text Graphs`
`Heterogeneous Network Embedding via Deep Architectures
`_
| :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
`_
| :authors:`Cheng Yang, Zhiyuan Liu, Deli Zhao, Maosong Sun, Edward Chang`
| :venue:`AAAI 2015`
`Max-Margin DeepWalk: Discriminative Learning of Network Representation
`_
| :authors:`Cunchao Tu, Weicheng Zhang, Zhiyuan Liu, Maosong Sun`
| :venue:`IJCAI 2016`
`metapath2vec: Scalable Representation Learning for Heterogeneous Networks
`_
| :authors:`Yuxiao Dong, Nitesh V. Chawla, Ananthram Swami`
| :venue:`KDD 2017`
`Meta-Path Guided Embedding for Similarity Search in Large-Scale Heterogeneous Information Networks
`_
| :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
`_
| :authors:`Tao-yang Fu, Wang-Chien Lee, Zhen Lei`
| :venue:`CIKM 2017`
`An Attention-based Collaboration Framework for Multi-View Network Representation Learning
`_
| :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
`_
| :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
`_
| :authors:`Suhang Wang, Charu Aggarwal, Jiliang Tang, Huan Liu`
| :venue:`CIKM 2017`
`CANE: Context-Aware Network Embedding for Relation Modeling
`_
| :authors:`Cunchao Tu, Han Liu, Zhiyuan Liu, Maosong Sun`
| :venue:`ACL 2017`
`PME: Projected Metric Embedding on Heterogeneous Networks for Link Prediction
`_
| :authors:`Hongxu Chen, Hongzhi Yin, Weiqing Wang, Hao Wang, Quoc Viet Hung Nguyen, Xue Li`
| :venue:`KDD 2018`
`BiNE: Bipartite Network Embedding
`_
| :authors:`Ming Gao, Leihui Chen, Xiangnan He, Aoying Zhou`
| :venue:`SIGIR 2018`
`StarSpace: Embed All The Things
`_
| :authors:`Ledell Wu, Adam Fisch, Sumit Chopra, Keith Adams, Antoine Bordes, Jason Weston`
| :venue:`AAAI 2018`
`Exploring Expert Cognition for Attributed Network Embedding
`_
| :authors:`Xiao Huang, Qingquan Song, Jundong Li, Xia Hu`
| :venue:`WSDM 2018`
`SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction
`_
| :authors:`Hongwei Wang, Fuzheng Zhang, Min Hou, Xing Xie, Minyi Guo, Qi Liu`
| :venue:`WSDM 2018`
`Multidimensional Network Embedding with Hierarchical Structures
`_
| :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
`_
| :authors:`Meng Qu, Jian Tang, Jiawei Han`
| :venue:`WSDM 2018`
`Generative Adversarial Network based Heterogeneous Bibliographic Network Representation for Personalized Citation Recommendation
`_
| :authors:`Xiaoyan Cai, Junwei Han, Libin Yang`
| :venue:`AAAI 2018`
`ANRL: Attributed Network Representation Learning via Deep Neural Networks
`_
| :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
`_
| :authors:`Wei Wu, Bin Li, Ling Chen, Chengqi Zhang`
| :venue:`IJCAI 2018`
`Deep Attributed Network Embedding
`_
| :authors:`Hongchang Gao, Heng Huang`
| :venue:`IJCAI 2018`
`Co-Regularized Deep Multi-Network Embedding
`_
| :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
`_
| :authors:`Yu Shi, Qi Zhu, Fang Guo, Chao Zhang, Jiawei Han`
| :venue:`KDD 2018`
`Meta-Graph Based HIN Spectral Embedding: Methods, Analyses, and Insights
`_
| :authors:`Carl Yang, Yichen Feng, Pan Li, Yu Shi, Jiawei Han`
| :venue:`ICDM 2018`
`SIDE: Representation Learning in Signed Directed Networks
`_
| :authors:`Junghwan Kim, Haekyu Park, Ji-Eun Lee, U Kang`
| :venue:`WWW 2018`
`Learning Network-to-Network Model for Content-rich Network Embedding
`_
| :authors:` Zhicheng He, Jie Liu, Na Li, Yalou Huang`
| :venue:`KDD 2019`
Node Representation Learning in Dynamic Graphs
----------------------------------------------
`Know-evolve: Deep temporal reasoning for dynamic knowledge graphs
`_
| :authors:`Rakshit Trivedi, Hanjun Dai, Yichen Wang, Le Song`
| :venue:`ICML 2017`
`Dyngem: Deep embedding method for dynamic graphs
`_
| :authors:`Palash Goyal, Nitin Kamra, Xinran He, Yan Liu`
| :venue:`ICLR 2017 Workshop`
`Attributed network embedding for learning in a dynamic environment
`_
| :authors:`Jundong Li, Harsh Dani, Xia Hu, Jiliang Tang, Yi Chang, Huan Liu`
| :venue:`CIKM 2017`
`Dynamic Network Embedding by Modeling Triadic Closure Process
`_
| :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
`_
| :authors:`Jianxin Ma, Peng Cui, Wenwu Zhu`
| :venue:`AAAI 2018`
`TIMERS: Error-Bounded SVD Restart on Dynamic Networks
`_
| :authors:`Ziwei Zhang, Peng Cui, Jian Pei, Xiao Wang, Wenwu Zhu`
| :venue:`AAAI 2018`
`Dynamic Embeddings for User Profiling in Twitter
`_
| :authors:`Shangsong Liang, Xiangliang Zhang, Zhaochun Ren, Evangelos Kanoulas`
| :venue:`KDD 2018`
`Dynamic Network Embedding : An Extended Approach for Skip-gram based Network Embedding
`_
| :authors:`Lun Du, Yun Wang, Guojie Song, Zhicong Lu, Junshan Wang`
| :venue:`IJCAI 2018`
`DyRep: Learning Representations over Dynamic Graphs
`_
| :authors:`Rakshit Trivedi, Mehrdad Farajtabar, Prasenjeet Biswal, Hongyuan Zha`
| :venue:`ICLR 2019`
`Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks
`_
| :authors:`Srijan Kumar, Xikun Zhang, Jure Leskovec`
| :venue:`KDD 2019`
`Variational Graph Recurrent Neural Networks
`_
| :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
`_
| :authors:`Vineet Kosaraju, Amir Sadeghian, Roberto Martín-Martín, Ian Reid, S. Hamid Rezatofighi, Silvio Savarese`
| :venue:`NeurIPS 2019`
Knowledge Graph Embedding
=========================
`A Three-Way Model for Collective Learning on Multi-Relational Data.
`_
| :authors:`Maximilian Nickel, Volker Tresp, Hans-Peter Kriegel`
| :venue:`ICML 2011`
`Translating Embeddings for Modeling Multi-relational Data
`_
| :authors:`Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, Oksana Yakhnenko`
| :venue:`NIPS 2013`
`Knowledge Graph Embedding by Translating on Hyperplanes
`_
| :authors:`Zhen Wang, Jianwen Zhang, Jianlin Feng, Zheng Chen`
| :venue:`AAAI 2014`
`Reducing the Rank of Relational Factorization Models by Including Observable Patterns
`_
| :authors:`Maximilian Nickel, Xueyan Jiang, Volker Tresp`
| :venue:`NIPS 2014`
`Learning Entity and Relation Embeddings for Knowledge Graph Completion
`_
| :authors:`Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, Xuan Zhu`
| :venue:`AAAI 2015`
`A Review of Relational Machine Learning for Knowledge Graph
`_
| :authors:`Maximilian Nickel, Kevin Murphy, Volker Tresp, Evgeniy Gabrilovich`
| :venue:`IEEE 2015`
`Knowledge Graph Embedding via Dynamic Mapping Matrix
`_
| :authors:`Guoliang Ji, Shizhu He, Liheng Xu, Kang Liu, Jun Zha`
| :venue:`ACL 2015`
`Modeling Relation Paths for Representation Learning of Knowledge Bases
`_
| :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
`_
| :authors:`Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, Li Deng`
| :venue:`ICLR 2015`
`Holographic Embeddings of Knowledge Graphs
`_
| :authors:`Maximilian Nickel, Lorenzo Rosasco, Tomaso Poggio`
| :venue:`AAAI 2016`
`Complex Embeddings for Simple Link Prediction
`_
| :authors:`Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, Guillaume Bouchard`
| :venue:`ICML 2016`
`Modeling Relational Data with Graph Convolutional Networks
`_
| :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
`_
| :authors:`Armand Joulin, Edouard Grave, Piotr Bojanowski, Maximilian Nickel, Tomas Mikolov`
| :venue:`arXiv 2017`
`Convolutional 2D Knowledge Graph Embeddings
`_
| :authors:`Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, Sebastian Riedel`
| :venue:`AAAI 2018`
`Knowledge Graph Embedding With Iterative Guidance From Soft Rules
`_
| :authors:`Shu Guo, Quan Wang, Lihong Wang, Bin Wang, Li Guo`
| :venue:`AAAI 2018`
`KBGAN: Adversarial Learning for Knowledge Graph Embeddings
`_
| :authors:`Liwei Cai, William Yang Wang`
| :venue:`NAACL 2018`
`Improving Knowledge Graph Embedding Using Simple Constraints
`_
| :authors:`Boyang Ding, Quan Wang, Bin Wang, Li Guo`
| :venue:`ACL 2018`
`SimplE Embedding for Link Prediction in Knowledge Graphs
`_
| :authors:`Seyed Mehran Kazemi, David Poole`
| :venue:`NeurIPS 2018`
`A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network
`_
| :authors:`Dai Quoc Nguyen, Tu Dinh Nguyen, Dat Quoc Nguyen, Dinh Phung`
| :venue:`NAACL 2018`
`Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning
`_
| :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
`_
| :authors:`Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, Jian Tang`
| :venue:`ICLR 2019`
`Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs
`_
| :authors:`Deepak Nathani, Jatin Chauhan, Charu Sharma, Manohar Kaul`
| :venue:`ACL 2019`
`Probabilistic Logic Neural Networks for Reasoning
`_
| :authors:`Meng Qu, Jian Tang`
| :venue:`NeurIPS 2019`
`Quaternion Knowledge Graph Embeddings
`_
| :authors:`Shuai Zhang, Yi Tay, Lina Yao, Qi Liu`
| :venue:`NeurIPS 2019`
`Quantum Embedding of Knowledge for Reasoning
`_
| :authors:`Dinesh Garg, Santosh K. Srivastava, Hima Karanam`
| :venue:`NeurIPS 2019`
`Multi-relational Poincaré Graph Embeddings
`_
| :authors:`Ivana Balaževic, Carl Allen, Timothy Hospedales`
| :venue:`NeurIPS 2019`
`Dynamically Pruned Message Passing Networks for Large-scale Knowledge Graph Reasoning
`_
| :authors:`Xiaoran Xu, Wei Feng, Yunsheng Jiang, Xiaohui Xie, Zhiqing Sun, Zhi-Hong Deng`
| :venue:`ICLR 2020`
Graph Neural Networks
=====================
`Revisiting Semi-supervised Learning with Graph Embeddings
`_
| :authors:`Zhilin Yang, William W. Cohen, Ruslan Salakhutdinov`
| :venue:`ICML 2016`
`Semi-Supervised Classification with Graph Convolutional Networks
`_
| :authors:`Thomas N. Kipf, Max Welling`
| :venue:`ICLR 2017`
`Neural Message Passing for Quantum Chemistry
`_
| :authors:`Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, George E. Dahl`
| :venue:`ICML 2017`
`Motif-Aware Graph Embeddings
`_
| :authors:`Hoang Nguyen, Tsuyoshi Murata`
| :venue:`IJCAI 2017`
`Learning Graph Representations with Embedding Propagation
`_
| :authors:`Alberto Garcia-Duran, Mathias Niepert`
| :venue:`NIPS 2017`
`Inductive Representation Learning on Large Graphs
`_
| :authors:`William L. Hamilton, Rex Ying, Jure Leskovec`
| :venue:`NIPS 2017`
`Graph Attention Networks
`_
| :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
`_
| :authors:`Jie Chen, Tengfei Ma, Cao Xiao`
| :venue:`ICLR 2018`
`Representation Learning on Graphs with Jumping Knowledge Networks
`_
| :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
`_
| :authors:`Jianfei Chen, Jun Zhu, Le Song`
| :venue:`ICML 2018`
`Large-Scale Learnable Graph Convolutional Networks
`_
| :authors:`Hongyang Gao, Zhengyang Wang, Shuiwang Ji`
| :venue:`KDD 2018`
`Adaptive Sampling Towards Fast Graph Representation Learning
`_
| :authors:`Wenbing Huang, Tong Zhang, Yu Rong, Junzhou Huang`
| :venue:`NeurIPS 2018`
`Hierarchical Graph Representation Learning with Differentiable Pooling
`_
| :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
`_
| :authors:`Yin Cheng Ng, Nicolò Colombo, Ricardo Silva`
| :venue:`NeurIPS 2018`
`Pitfalls of Graph Neural Network Evaluation
`_
| :authors:`Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, Stephan Günnemann`
| :venue:`arXiv 2018`
`Heterogeneous Graph Attention Network
`_
| :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
`_
| :authors:`Yingxue Zhang, Soumyasundar Pal, Mark Coates, Deniz Üstebay`
| :venue:`AAAI 2019`
`How Powerful are Graph Neural Networks?
`_
| :authors:`Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka`
| :venue:`ICLR 2019`
`LanczosNet: Multi-Scale Deep Graph Convolutional Networks
`_
| :authors:`Renjie Liao, Zhizhen Zhao, Raquel Urtasun, Richard S. Zemel`
| :venue:`ICLR 2019`
`Graph Wavelet Neural Network
`_
| :authors:`Bingbing Xu, Huawei Shen, Qi Cao, Yunqi Qiu, Xueqi Cheng`
| :venue:`ICLR 2019`
`Supervised Community Detection with Line Graph Neural Networks
`_
| :authors:`Zhengdao Chen, Xiang Li, Joan Bruna`
| :venue:`ICLR 2019`
`Predict then Propagate: Graph Neural Networks meet Personalized PageRank
`_
| :authors:`Johannes Klicpera, Aleksandar Bojchevski, Stephan Günnemann`
| :venue:`ICLR 2019`
`Invariant and Equivariant Graph Networks
`_
| :authors:`Haggai Maron, Heli Ben-Hamu, Nadav Shamir, Yaron Lipman`
| :venue:`ICLR 2019`
`Capsule Graph Neural Network
`_
| :authors:`Zhang Xinyi, Lihui Chen`
| :venue:`ICLR 2019`
`MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing
`_
| :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
`_
| :authors:`Hongyang Gao, Shuiwang Ji`
| :venue:`ICML 2019`
`Disentangled Graph Convolutional Networks
`_
| :authors:`Jianxin Ma, Peng Cui, Kun Kuang, Xin Wang, Wenwu Zhu`
| :venue:`ICML 2019`
`GMNN: Graph Markov Neural Networks
`_
| :authors:`Meng Qu, Yoshua Bengio, Jian Tang`
| :venue:`ICML 2019`
`Simplifying Graph Convolutional Networks
`_
| :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
`_
| :authors:`Jiaxuan You, Rex Ying, Jure Leskovec`
| :venue:`ICML 2019`
`Self-Attention Graph Pooling
`_
| :authors:`Junhyun Lee, Inyeop Lee, Jaewoo Kang`
| :venue:`ICML 2019`
`Relational Pooling for Graph Representations
`_
| :authors:`Ryan L. Murphy, Balasubramaniam Srinivasan, Vinayak Rao, Bruno Ribeiro`
| :venue:`ICML 2019`
`Graph Representation Learning via Hard and Channel-Wise Attention Networks
`_
| :authors:`Hongyang Gao, Shuiwang Ji`
| :venue:`KDD 2019`
`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
`_
| :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
`_
| :authors:`Jun Wu, Jingrui He, Jiejun Xu`
| :venue:`KDD 2019`
`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
`_
| :authors:`Xiao Huang, Qingquan Song, Yuening Li, Xia Hu`
| :venue:`KDD 2019`
`Graph Convolutional Networks with EigenPooling
`_
| :authors:`Yao Ma, Suhang Wang, Charu Aggarwal, Jiliang Tang`
| :venue:`KDD 2019`
`DFNets: Spectral CNNs for Graphs with Feedback-Looped Filters
`_
| :authors:`Asiri Wijesinghe, Qing Wang`
| :venue:`NeurIPS 2019`
`Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology
`_
| :authors:`Nima Dehmamy, Albert-László Barabási, Rose Yu`
| :venue:`NeurIPS 2019`
`A Flexible Generative Framework for Graph-based Semi-supervised Learning
`_
| :authors:`Jiaqi Ma, Weijing Tang, Ji Zhu, Qiaozhu Mei`
| :venue:`NeurIPS 2019`
`Rethinking Kernel Methods for Node Representation Learning on Graphs
`_
| :authors:`Yu Tian, Long Zhao, Xi Peng, Dimitris N. Metaxas`
| :venue:`NeurIPS 2019`
`Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
`_
| :authors:`Sitao Luan, Mingde Zhao, Xiao-Wen Chang, Doina Precup`
| :venue:`NeurIPS 2019`
`N-Gram Graph: A Simple Unsupervised Representation for Molecules
`_
| :authors:`Shengchao Liu, Thevaa Chandereng, Yingyu Liang`
| :venue:`NeurIPS 2019`
`DeepGCNs: Can GCNs Go as Deep as CNNs?
`_
| :authors:`Guohao Li, Matthias Muller, Ali Thabet, Bernard Ghanem`
| :venue:`ICCV 2019`
`Continuous Graph Neural Networks
`_
| :authors:`Louis-Pascal A. C. Xhonneux, Meng Qu, Jian Tang`
| :venue:`arXiv 2019`
`Curvature Graph Network
`_
| :authors:`Ze Ye, Kin Sum Liu, Tengfei Ma, Jie Gao, Chao Chen`
| :venue:`ICLR 2020`
`Memory-based Graph Networks
`_
| :authors:`Amir hosein Khasahmadi, Kaveh Hassani, Parsa Moradi, Leo Lee, Quaid Morris`
| :venue:`ICLR 2020`
`Strategies for Pre-training Graph Neural Networks
`_
| :authors:`Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, Jure Leskovec`
| :venue:`ICLR 2020`
Applications of Graph Deep Learning
=================================
Natural Language Processing
---------------------------
`Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling
`_
| :authors:`Diego Marcheggiani, Ivan Titov`
| :venue:`EMNLP 2017`
`Graph Convolutional Encoders for Syntax-aware Neural Machine Translation
`_
| :authors:`Joost Bastings, Ivan Titov, Wilker Aziz, Diego Marcheggiani, Khalil Sima’an`
| :venue:`EMNLP 2017`
`Graph-based Neural Multi-Document Summarization
`_
| :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
`_
| :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
`_
| :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
`_
| :authors:`Daniil Sorokin, Iryna Gurevych`
| :venue:`COLING 2018`
`Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks
`_
| :authors:`Diego Marcheggiani, Joost Bastings, Ivan Titov`
| :venue:`NAACL 2018`
`Linguistically-Informed Self-Attention for Semantic Role Labeling
`_
| :authors:`Emma Strubell, Patrick Verga, Daniel Andor, David Weiss, Andrew McCallum`
| :venue:`EMNLP 2018`
`Graph Convolution over Pruned Dependency Trees Improves Relation Extraction
`_
| :authors:`Yuhao Zhang, Peng Qi, Christopher D. Manning`
| :venue:`EMNLP 2018`
`A Graph-to-Sequence Model for AMR-to-Text Generation
`_
| :authors:`Linfeng Song, Yue Zhang, Zhiguo Wang, Daniel Gildea`
| :venue:`ACL 2018`
`Graph-to-Sequence Learning using Gated Graph Neural Networks
`_
| :authors:`Daniel Beck, Gholamreza Haffari, Trevor Cohn`
| :venue:`ACL 2018`
`Graph Convolutional Networks for Text Classification
`_
| :authors:`Liang Yao, Chengsheng Mao, Yuan Luo`
| :venue:`AAAI 2019`
`Differentiable Perturb-and-Parse: Semi-Supervised Parsing with a Structured Variational Autoencoder
`_
| :authors:`Caio Corro, Ivan Titov`
| :venue:`ICLR 2019`
`Structured Neural Summarization
`_
| :authors:`Patrick Fernandes, Miltiadis Allamanis, Marc Brockschmid`
| :venue:`ICLR 2019`
`Multi-task Learning over Graph Structures
`_
| :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
`_
| :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
`_
| :authors:`Yang Liu, Ivan Titov, Mirella Lapata`
| :venue:`NAACL 2019`
`Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks
`_
| :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
`_
| :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
`_
| :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
`_
| :authors:`Chang Li, Dan Goldwasser`
| :venue:`ACL 2019`
`Attention Guided Graph Convolutional Networks for Relation Extraction
`_
| :authors:`Zhijiang Guo, Yan Zhang, Wei Lu`
| :venue:`ACL 2019`
`Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks
`_
| :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
`_
| :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
`_
| :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
`_
| :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
`_
| :authors:`Wei Li, Jingjing Xu, Yancheng He, Shengli Yan, Yunfang Wu, Xu Sun`
| :venue:`ACL 2019`
`Matching Article Pairs with Graphical Decomposition and Convolutions
`_
| :authors:`Bang Liu, Di Niu, Haojie Wei, Jinghong Lin, Yancheng He, Kunfeng Lai, Yu Xu`
| :venue:`ACL 2019`
`Embedding Imputation with Grounded Language Information
`_
| :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
`_
| :authors:`Chang Li, Dan Goldwasser`
| :venue:`ACL 2019`
`A Neural Multi-digraph Model for Chinese NER with Gazetteers
`_
| :authors:`Ruixue Ding, Pengjun Xie, Xiaoyan Zhang, Wei Lu, Linlin Li, Luo Si`
| :venue:`ACL 2019`
`Tree Communication Models for Sentiment Analysis
`_
| :authors:`Yuan Zhang, Yue Zhang`
| :venue:`ACL 2019`
`A2N: Attending to Neighbors for Knowledge Graph Inference
`_
| :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
`_
| :authors:`Daesik Kim, Seonhoon Kim, Nojun Kwak`
| :venue:`ACL 2019`
`Look Again at the Syntax: Relational Graph Convolutional Network for Gendered Ambiguous Pronoun Resolution
`_
| :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
`_
| :authors:`Hongyang Gao, Yongjun Chen, Shuiwang Ji`
| :venue:`WWW 2019`
`Learning to Create Sentence Semantic Relation Graphs for Multi-Document Summarization
`_
| :authors:`Diego Antognini, Boi Faltings`
| :venue:`EMNLP 2019`
`Dependency-Guided LSTM-CRF for Named Entity Recognition
`_
| :authors:`Zhanming Jie, Wei Lu`
| :venue:`EMNLP 2019`
`Modeling Conversation Structure and Temporal Dynamics for Jointly Predicting Rumor Stance and Veracity
`_
| :authors:`Penghui Wei, Nan Xu, Wenji Mao`
| :venue:`EMNLP 2019`
`DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation
`_
| :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
`_
| :authors:`Jie Zhu, Junhui Li, Muhua Zhu, Longhua Qian, Min Zhang, Guodong Zhou`
| :venue:`EMNLP 2019`
`KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning
`_
| :authors:`Bill Yuchen Lin, Xinyue Chen, Jamin Chen, Xiang Ren`
| :venue:`EMNLP 2019`
Computer Vision
---------------
`3D Graph Neural Networks for RGBD Semantic Segmentation
`_
| :authors:`Xiaojuan Qi, Renjie Liao, Jiaya Jia, Sanja Fidler, Raquel Urtasun`
| :venue:`ICCV 2017`
`Situation Recognition With Graph Neural Networks
`_
| :authors:`Ruiyu Li, Makarand Tapaswi, Renjie Liao, Jiaya Jia, Raquel Urtasun, Sanja Fidler`
| :venue:`ICCV 2017`
`Graph-Based Classification of Omnidirectional Images
`_
| :authors:`Renata Khasanova, Pascal Frossard`
| :venue:`ICCV 2017`
`Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
`_
| :authors:`Sijie Yan, Yuanjun Xiong, Dahua Lin`
| :venue:`AAAI 2018`
`Image Generation from Scene Graphs
`_
| :authors:`Justin Johnson, Agrim Gupta, Li Fei-Fei`
| :venue:`CVPR 2018`
`FoldingNet: Point Cloud Auto-Encoder via Deep Grid Deformation
`_
| :authors:`Yaoqing Yang, Chen Feng, Yiru Shen, Dong Tian`
| :venue:`CVPR 2018`
`PPFNet: Global Context Aware Local Features for Robust 3D Point Matching
`_
| :authors:`Haowen Deng, Tolga Birdal, Slobodan Ilic`
| :venue:`CVPR 2018`
`Iterative Visual Reasoning Beyond Convolutions
`_
| :authors:`Xinlei Chen, Li-Jia Li, Li Fei-Fei, Abhinav Gupta`
| :venue:`CVPR 2018`
`Surface Networks
`_
| :authors:`Ilya Kostrikov, Zhongshi Jiang, Daniele Panozzo, Denis Zorin, Joan Bruna`
| :venue:`CVPR 2018`
`FeaStNet: Feature-Steered Graph Convolutions for 3D Shape Analysis
`_
| :authors:`Nitika Verma, Edmond Boyer, Jakob Verbeek`
| :venue:`CVPR 2018`
`Learning to Act Properly: Predicting and Explaining Affordances From Images
`_
| :authors:`Ching-Yao Chuang, Jiaman Li, Antonio Torralba, Sanja Fidler`
| :venue:`CVPR 2018`
`Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling
`_
| :authors:`Yiru Shen, Chen Feng, Yaoqing Yang, Dong Tian`
| :venue:`CVPR 2018`
`Deformable Shape Completion With Graph Convolutional Autoencoders
`_
| :authors:`Or Litany, Alex Bronstein, Michael Bronstein, Ameesh Makadia`
| :venue:`CVPR 2018`
`Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images
`_
| :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
`_
| :authors:`Siyuan Qi, Wenguan Wang, Baoxiong Jia, Jianbing Shen, Song-Chun Zhu`
| :venue:`ECCV 2018`
`Generating 3D Faces using Convolutional Mesh Autoencoders
`_
| :authors:`Anurag Ranjan, Timo Bolkart, Soubhik Sanyal, Michael J. Black`
| :venue:`ECCV 2018`
`Learning SO(3) Equivariant Representations with Spherical CNNs
`_
| :authors:`Carlos Esteves, Christine Allen-Blanchette, Ameesh Makadia, Kostas Daniilidis`
| :venue:`ECCV 2018`
`Neural Graph Matching Networks for Fewshot 3D Action Recognition
`_
| :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
`_
| :authors:`Lasse Hansen, Jasper Diesel, Mattias P. Heinrich`
| :venue:`ECCV 2018`
`Hierarchical Video Frame Sequence Representation with Deep Convolutional Graph Network
`_
| :authors:`Feng Mao, Xiang Wu, Hui Xue, Rong Zhang`
| :venue:`ECCV 2018`
`Graph R-CNN for Scene Graph Generation
`_
| :authors:`Jianwei Yang, Jiasen Lu, Stefan Lee, Dhruv Batra, Devi Parikh`
| :venue:`ECCV 2018`
`Exploring Visual Relationship for Image Captioning
`_
| :authors:`Ting Yao, Yingwei Pan, Yehao Li, Tao Mei`
| :venue:`ECCV 2018`
`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
`_
| :authors:`Will Norcliffe-Brown, Efstathios Vafeias, Sarah Parisot`
| :venue:`NeurIPS 2018`
`LinkNet: Relational Embedding for Scene Graph
`_
| :authors:`Sanghyun Woo, Dahun Kim, Donghyeon Cho, In So Kweon`
| :venue:`NeurIPS 2018`
`Flexible Neural Representation for Physics Prediction
`_
| :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
`_
| :authors:`Diego Valsesia, Giulia Fracastoro, Enrico Magli`
| :venue:`ICLR 2019`
`Graph-Based Global Reasoning Networks
`_
| :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
`_
| :authors:`Jin Zeng, Jiahao Pang, Wenxiu Sun, Gene Cheung`
| :venue:`CVPR 2019`
`Learning Context Graph for Person Search
`_
| :authors:`Yichao Yan, Qiang Zhang, Bingbing Ni, Wendong Zhang, Minghao Xu, Xiaokang Yang`
| :venue:`CVPR 2019`
`Graphonomy: Universal Human Parsing via Graph Transfer Learning
`_
| :authors:`Ke Gong, Yiming Gao, Xiaodan Liang, Xiaohui Shen, Meng Wang, Liang Lin`
| :venue:`CVPR 2019`
`Masked Graph Attention Network for Person Re-Identification
`_
for_Person_Re-Identification_CVPRW_2019_paper.html>`_
| :authors:`Liqiang Bao, Bingpeng Ma, Hong Chang, Xilin Chen`
| :venue:`CVPR 2019`
`Learning to Cluster Faces on an Affinity Graph
`_
| :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
`_
| :authors:`Maosen Li, Siheng Chen, Xu Chen, Ya Zhang, Yanfeng Wang, Qi Tian`
| :venue:`CVPR 2019`
`Adaptively Connected Neural Networks
`_
| :authors:`Guangrun Wang, Keze Wang, Liang Lin`
| :venue:`CVPR 2019`
`Reasoning Visual Dialogs with Structural and Partial Observations
`_
| :authors:`Zilong Zheng, Wenguan Wang, Siyuan Qi, Song-Chun Zhu`
| :venue:`CVPR 2019`
`MeshCNN: A Network with an Edge
`_
| :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
`_
| :authors:`Jiwoong Park, Minsik Lee, Hyung Jin Chang, Kyuewang Lee, Jin Young Choi`
| :venue:`ICCV 2019`
`Pixel2Mesh++: Multi-View 3D Mesh Generation via Deformation
`_
| :authors:`Chao Wen, Yinda Zhang, Zhuwen Li, Yanwei Fu`
| :venue:`ICCV 2019`
`Learning Trajectory Dependencies for Human Motion Prediction
`_
| :authors:`Wei Mao, Miaomiao Liu, Mathieu Salzmann, Hongdong Li`
| :venue:`ICCV 2019`
`Graph-Based Object Classification for Neuromorphic Vision Sensing
`_
| :authors:`Yin Bi, Aaron Chadha, Alhabib Abbas, Eirina Bourtsoulatze, Yiannis Andreopoulos`
| :venue:`ICCV 2019`
`Fashion Retrieval via Graph Reasoning Networks on a Similarity Pyramid
`_
| :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
`_
| :authors:`Lifeng Fan, Wenguan Wang, Siyuan Huang, Xinyu Tang, Song-Chun Zhu`
| :venue:`ICCV 2019`
`Visual Semantic Reasoning for Image-Text Matching
`_
| :authors:`Kunpeng Li, Yulun Zhang, Kai Li, Yuanyuan Li, Yun Fu`
| :venue:`ICCV 2019`
`Graph Convolutional Networks for Temporal Action Localization
`_
| :authors:`Runhao Zeng, Wenbing Huang, Mingkui Tan, Yu Rong, Peilin Zhao, Junzhou Huang, Chuang Gan`
| :venue:`ICCV 2019`
`Semantically-Regularized Logic Graph Embeddings
`_
| :authors:`Yaqi Xie, Ziwei Xu, Kuldeep Meel, Mohan S Kankanhalli, Harold Soh`
| :venue:`NeurIPS 2019`
Recommender Systems
-------------------
`Graph Convolutional Neural Networks for Web-Scale Recommender Systems
`_
| :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
`_
| :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
`_
| :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
`_
| :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
`_
| :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
`_
| :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
`_
| :authors:`Le Wu, Peijie Sun, Yanjie Fu, Richang Hong, Xiting Wang, Meng Wang`
| :venue:`SIGIR 2019`
| :keywords:`Social Recommendation, diffusion`
`Neural Graph Collaborative Filtering
`_
| :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
`_
| :authors:`Haoyu Wang, Defu Lian, Yong Ge`
| :venue:`IJCAI 2019`
`IntentGC: A Scalable Graph Convolution Framework Fusing Heterogeneous Information for Recommendation
`_
| :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
`_
| :authors:`Yanru Qu, Ting Bai, Weinan Zhang, Jianyun Nie, Jian Tang`
| :venue:`KDD 2019 Workshop`
Link Prediction
---------------
`Link Prediction Based on Graph Neural Networks
`_
| :authors:`Muhan Zhang, Yixin Chen`
| :venue:`NeurIPS 2018`
`Link Prediction via Subgraph Embedding-Based Convex Matrix Completion
`_
| :authors:`Zhu Cao, Linlin Wang, Gerard de Melo`
| :venue:`AAAI 2018`
`Graph Convolutional Matrix Completion
`_
| :authors:`Rianne van den Berg, Thomas N. Kipf, Max Welling`
| :venue:`KDD 2018 Workshop`
`Semi-Implicit Graph Variational Auto-Encoders
`_
| :authors:`Arman Hasanzadeh, Ehsan Hajiramezanali, Nick Duffield , Krishna Narayanan, Mingyuan Zhou, Xiaoning Qian`
| :venue:`NeurIPS 2019`
Influence Prediction
--------------------
`DeepInf: Social Influence Prediction with Deep Learning
`_
| :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
`_
| :authors:`Namyong Park, Andrey Kan, Xin Luna Dong, Tong Zhao, Christos Faloutsos`
| :venue:`KDD 2019`
Neural Architecture Search
--------------------------
`Graph HyperNetworks for Neural Architecture Search
`_
| :authors:`Chris Zhang, Mengye Ren, Raquel Urtasun`
| :venue:`ICLR 2019`
`D-VAE: A Variational Autoencoder for Directed Acyclic Graphs
`_
| :authors:`Muhan Zhang, Shali Jiang, Zhicheng Cui, Roman Garnett, Yixin Chen`
| :venue:`NeurIPS 2019`
Reinforcement Learning
----------------------
`Action Schema Networks: Generalised Policies with Deep Learning
`_
| :authors:`Sam Toyer, Felipe Trevizan, Sylvie Thiebaux, Lexing Xie`
| :venue:`AAAI 2018`
`NerveNet: Learning Structured Policy with Graph Neural Networks
`_
| :authors:`Tingwu Wang, Renjie Liao, Jimmy Ba, Sanja Fidler`
| :venue:`ICLR 2018`
`Graph Networks as Learnable Physics Engines for Inference and Control
`_
| :authors:`Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg, Josh Merel, Martin Riedmiller`
| :venue:`ICML 2018`
`Learning Policy Representations in Multiagent Systems
`_
| :authors:`Aditya Grover, Maruan Al-Shedivat, Jayesh K. Gupta, Yura Burda, Harrison Edwards`
| :venue:`ICML 2018`
`Relational recurrent neural networks
`_
| :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
`_
| :authors:`Aniket Bajpai, Sankalp Garg, Mausam`
| :venue:`NeurIPS 2018`
`Neural Graph Evolution: Towards Efficient Automatic Robot Design
`_
| :authors:`Tingwu Wang, Yuhao Zhou, Sanja Fidler, Jimmy Ba`
| :venue:`ICLR 2019`
`No Press Diplomacy: Modeling Multi-Agent Gameplay
`_
| :authors:`Philip Paquette, Yuchen Lu, Steven Bocco, Max O. Smith, Satya Ortiz-Gagne, Jonathan K. Kummerfeld, Satinder Singh, Joelle Pineau, Aaron Courville`
| :venue:`NeurIPS 2019`
Combinatorial Optimization
--------------------------
`Learning Combinatorial Optimization Algorithms over Graphs
`_
| :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
`_
| :authors:`Zhuwen Li, Qifeng Chen, Vladlen Koltun`
| :venue:`NeurIPS 2018`
`Reinforcement Learning for Solving the Vehicle Routing Problem
`_
| :authors:`Mohammadreza Nazari, Afshin Oroojlooy, Lawrence V. Snyder, Martin Takáč`
| :venue:`NeurIPS 2018`
`Attention, Learn to Solve Routing Problems!
`_
| :authors:`Wouter Kool, Herke van Hoof, Max Welling`
| :venue:`ICLR 2019`
`Learning a SAT Solver from Single-Bit Supervision