Literature of Deep Learning for Graphs
This is a paper list about deep learning for graphs.
- 1 Node Representation Learning
- 2 Knowledge Graph Embedding
- 3 Graph Neural Networks
- 4 Applications of Graph Deep Learning
- 4.1 Natural Language Processing
- 4.2 Computer Vision
- 4.3 Recommender Systems
- 4.4 Link Prediction
- 4.5 Influence Prediction
- 4.6 Neural Architecture Search
- 4.7 Reinforcement Learning
- 4.8 Combinatorial Optimization
- 4.9 Adversarial Attack and Robustness
- 4.10 Graph Matching
- 4.11 Meta Learning and Few-shot Learning
- 4.12 Structure Learning
- 4.13 Bioinformatics and Chemistry
- 4.14 Graph Algorithms
- 4.15 Theorem Proving
- 5 Graph Generation
- 6 Graph Layout and High-dimensional Data Visualization
- 7 Graph Representation Learning Systems
- 8 Datasets
1 Node Representation Learning
1.1 Unsupervised Node Representation Learning
DeepWalk: Online Learning of Social Representations
Bryan Perozzi, Rami Al-Rfou, Steven Skiena
KDD 2014
Node classification, Random walk, Skip-gram
LINE: Large-scale Information Network Embedding
Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, Qiaozhu Mei
WWW 2015
First-order, Second-order, Node classification
GraRep: Learning Graph Representations with Global Structural Information
Shaosheng Cao, Wei Lu, Qiongkai Xu
CIKM 2015
High-order, SVD
node2vec: Scalable Feature Learning for Networks
Aditya Grover, Jure Leskovec
KDD 2016
Breadth-first Search, Depth-first Search, Node Classification, Link Prediction
Variational Graph Auto-Encoders
Thomas N. Kipf, Max Welling
arXiv 2016
Scalable Graph Embedding for Asymmetric Proximity
Chang Zhou, Yuqiong Liu, Xiaofei Liu, Zhongyi Liu, Jun Gao
AAAI 2017
Fast Network Embedding Enhancement via High Order Proximity Approximation
Cheng Yang, Maosong Sun, Zhiyuan Liu, Cunchao Tu
IJCAI 2017
struc2vec: Learning Node Representations from Structural Identity
Leonardo F. R. Ribeiro, Pedro H. P. Savarese, Daniel R. Figueiredo
KDD 2017
Structural Identity
Poincaré Embeddings for Learning Hierarchical Representations
Maximilian Nickel, Douwe Kiela
NIPS 2017
VERSE: Versatile Graph Embeddings from Similarity Measures
Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Emmanuel Müller
WWW 2018
Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec
Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, Jie Tang
WSDM 2018
Learning Structural Node Embeddings via Diffusion Wavelets
Claire Donnat, Marinka Zitnik, David Hallac, Jure Leskovec
KDD 2018
Quanyu Dai, Qiang Li, Jian Tang, Dan Wang
AAAI 2018
GraphGAN: Graph Representation Learning with Generative Adversarial Nets
Hongwei Wang, Jia Wang, Jialin Wang, Miao Zhao, Weinan Zhang, Fuzheng Zhang, Xing Xie, Minyi Guo
AAAI 2018
A General View for Network Embedding as Matrix Factorization
Xin Liu, Tsuyoshi Murata, Kyoung-Sook Kim, Chatchawan Kotarasu, Chenyi Zhuang
WSDM 2019
Petar Veličković, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, R Devon Hjelm
ICLR 2019
NetSMF: Large-Scale Network Embedding as Sparse Matrix Factorization
Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Chi Wang, Kuansan Wang, Jie Tang
WWW 2019
Adversarial Training Methods for Network Embedding
Quanyu Dai, Xiao Shen, Liang Zhang, Qiang Li, Dan Wang
WWW 2019
vGraph: A Generative Model for Joint Community Detection and Node Representation Learning
Fan-Yun Sun, Meng Qu, Jordan Hoffmann, Chin-Wei Huang, Jian Tang
NeurIPS 2019
ProGAN: Network Embedding via Proximity Generative Adversarial Network
Hongchang Gao, Jian Pei, Heng Huang
KDD 2019
GraphZoom: A Multi-level Spectral Approach for Accurate and Scalable Graph Embedding
Chenhui Deng, Zhiqiang Zhao, Yongyu Wang, Zhiru Zhang, Zhuo Feng
ICLR 2020
1.2 Node Representation Learning in Heterogeneous Graphs
Learning Latent Representations of Nodes for Classifying in Heterogeneous Social Networks
Yann Jacob, Ludovic Denoyer, Patrick Gallinari
WSDM 2014
PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks
Jian Tang, Meng Qu, Qiaozhu Mei
KDD 2015
Text Embedding, Heterogeneous Text Graphs
Heterogeneous Network Embedding via Deep Architectures
Shiyu Chang, Wei Han, Jiliang Tang, Guo-Jun Qi, Charu C. Aggarwal, Thomas S. Huang
KDD 2015
Network Representation Learning with Rich Text Information
Cheng Yang, Zhiyuan Liu, Deli Zhao, Maosong Sun, Edward Chang
AAAI 2015
Max-Margin DeepWalk: Discriminative Learning of Network Representation
Cunchao Tu, Weicheng Zhang, Zhiyuan Liu, Maosong Sun
IJCAI 2016
metapath2vec: Scalable Representation Learning for Heterogeneous Networks
Yuxiao Dong, Nitesh V. Chawla, Ananthram Swami
KDD 2017
Meta-Path Guided Embedding for Similarity Search in Large-Scale Heterogeneous Information Networks
Jingbo Shang, Meng Qu, Jialu Liu, Lance M. Kaplan, Jiawei Han, Jian Peng
arXiv 2016
HIN2Vec: Explore Meta-paths in Heterogeneous Information Networks for Representation Learning
Tao-yang Fu, Wang-Chien Lee, Zhen Lei
CIKM 2017
An Attention-based Collaboration Framework for Multi-View Network Representation Learning
Meng Qu, Jian Tang, Jingbo Shang, Xiang Ren, Ming Zhang, Jiawei Han
CIKM 2017
Multi-view Clustering with Graph Embedding for Connectome Analysis
Guixiang Ma, Lifang He, Chun-Ta Lu, Weixiang Shao, Philip S. Yu, Alex D. Leow, Ann B. Ragin
CIKM 2017
Attributed Signed Network Embedding
Suhang Wang, Charu Aggarwal, Jiliang Tang, Huan Liu
CIKM 2017
CANE: Context-Aware Network Embedding for Relation Modeling
Cunchao Tu, Han Liu, Zhiyuan Liu, Maosong Sun
ACL 2017
PME: Projected Metric Embedding on Heterogeneous Networks for Link Prediction
Hongxu Chen, Hongzhi Yin, Weiqing Wang, Hao Wang, Quoc Viet Hung Nguyen, Xue Li
KDD 2018
BiNE: Bipartite Network Embedding
Ming Gao, Leihui Chen, Xiangnan He, Aoying Zhou
SIGIR 2018
StarSpace: Embed All The Things
Ledell Wu, Adam Fisch, Sumit Chopra, Keith Adams, Antoine Bordes, Jason Weston
AAAI 2018
Exploring Expert Cognition for Attributed Network Embedding
Xiao Huang, Qingquan Song, Jundong Li, Xia Hu
WSDM 2018
SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction
Hongwei Wang, Fuzheng Zhang, Min Hou, Xing Xie, Minyi Guo, Qi Liu
WSDM 2018
Multidimensional Network Embedding with Hierarchical Structures
Yao Ma, Zhaochun Ren, Ziheng Jiang, Jiliang Tang, Dawei Yin
WSDM 2018
Curriculum Learning for Heterogeneous Star Network Embedding via Deep Reinforcement Learning
Meng Qu, Jian Tang, Jiawei Han
WSDM 2018
Xiaoyan Cai, Junwei Han, Libin Yang
AAAI 2018
ANRL: Attributed Network Representation Learning via Deep Neural Networks
Zhen Zhang, Hongxia Yang, Jiajun Bu, Sheng Zhou, Pinggang Yu, Jianwei Zhang, Martin Ester, Can Wang
IJCAI 2018
Efficient Attributed Network Embedding via Recursive Randomized Hashing
Wei Wu, Bin Li, Ling Chen, Chengqi Zhang
IJCAI 2018
Deep Attributed Network Embedding
Hongchang Gao, Heng Huang
IJCAI 2018
Co-Regularized Deep Multi-Network Embedding
Jingchao Ni, Shiyu Chang, Xiao Liu, Wei Cheng, Haifeng Chen, Dongkuan Xu, Xiang Zhang
WWW 2018
Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks
Yu Shi, Qi Zhu, Fang Guo, Chao Zhang, Jiawei Han
KDD 2018
Meta-Graph Based HIN Spectral Embedding: Methods, Analyses, and Insights
Carl Yang, Yichen Feng, Pan Li, Yu Shi, Jiawei Han
ICDM 2018
SIDE: Representation Learning in Signed Directed Networks
Junghwan Kim, Haekyu Park, Ji-Eun Lee, U Kang
WWW 2018
Learning Network-to-Network Model for Content-rich Network Embedding
:authors:` Zhicheng He, Jie Liu, Na Li, Yalou Huang`
KDD 2019
1.3 Node Representation Learning in Dynamic Graphs
Know-evolve: Deep temporal reasoning for dynamic knowledge graphs
Rakshit Trivedi, Hanjun Dai, Yichen Wang, Le Song
ICML 2017
Dyngem: Deep embedding method for dynamic graphs
Palash Goyal, Nitin Kamra, Xinran He, Yan Liu
ICLR 2017 Workshop
Attributed network embedding for learning in a dynamic environment
Jundong Li, Harsh Dani, Xia Hu, Jiliang Tang, Yi Chang, Huan Liu
CIKM 2017
Dynamic Network Embedding by Modeling Triadic Closure Process
Lekui Zhou, Yang Yang, Xiang Ren, Fei Wu, Yueting Zhuang
AAAI 2018
DepthLGP: Learning Embeddings of Out-of-Sample Nodes in Dynamic Networks
Jianxin Ma, Peng Cui, Wenwu Zhu
AAAI 2018
TIMERS: Error-Bounded SVD Restart on Dynamic Networks
Ziwei Zhang, Peng Cui, Jian Pei, Xiao Wang, Wenwu Zhu
AAAI 2018
Dynamic Embeddings for User Profiling in Twitter
Shangsong Liang, Xiangliang Zhang, Zhaochun Ren, Evangelos Kanoulas
KDD 2018
Dynamic Network Embedding : An Extended Approach for Skip-gram based Network Embedding
Lun Du, Yun Wang, Guojie Song, Zhicong Lu, Junshan Wang
IJCAI 2018
DyRep: Learning Representations over Dynamic Graphs
Rakshit Trivedi, Mehrdad Farajtabar, Prasenjeet Biswal, Hongyuan Zha
ICLR 2019
Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks
Srijan Kumar, Xikun Zhang, Jure Leskovec
KDD 2019
Variational Graph Recurrent Neural Networks
Ehsan Hajiramezanali, Arman Hasanzadeh, Nick Duffield, Krishna R Narayanan, Mingyuan Zhou, Xiaoning Qian
NeurIPS 2019
Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks
Vineet Kosaraju, Amir Sadeghian, Roberto Martín-Martín, Ian Reid, S. Hamid Rezatofighi, Silvio Savarese
NeurIPS 2019
2 Knowledge Graph Embedding
A Three-Way Model for Collective Learning on Multi-Relational Data.
Maximilian Nickel, Volker Tresp, Hans-Peter Kriegel
ICML 2011
Translating Embeddings for Modeling Multi-relational Data
Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, Oksana Yakhnenko
NIPS 2013
Knowledge Graph Embedding by Translating on Hyperplanes
Zhen Wang, Jianwen Zhang, Jianlin Feng, Zheng Chen
AAAI 2014
Reducing the Rank of Relational Factorization Models by Including Observable Patterns
Maximilian Nickel, Xueyan Jiang, Volker Tresp
NIPS 2014
Learning Entity and Relation Embeddings for Knowledge Graph Completion
Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, Xuan Zhu
AAAI 2015
A Review of Relational Machine Learning for Knowledge Graph
Maximilian Nickel, Kevin Murphy, Volker Tresp, Evgeniy Gabrilovich
IEEE 2015
Knowledge Graph Embedding via Dynamic Mapping Matrix
Guoliang Ji, Shizhu He, Liheng Xu, Kang Liu, Jun Zha
ACL 2015
Modeling Relation Paths for Representation Learning of Knowledge Bases
Yankai Lin, Zhiyuan Liu, Huanbo Luan, Maosong Sun, Siwei Rao, Song Liu
EMNLP 2015
Embedding Entities and Relations for Learning and Inference in Knowledge Bases
Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, Li Deng
ICLR 2015
Holographic Embeddings of Knowledge Graphs
Maximilian Nickel, Lorenzo Rosasco, Tomaso Poggio
AAAI 2016
Complex Embeddings for Simple Link Prediction
Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, Guillaume Bouchard
ICML 2016
Modeling Relational Data with Graph Convolutional Networks
Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, Max Welling
arXiv 2017
Fast Linear Model for Knowledge Graph Embeddings
Armand Joulin, Edouard Grave, Piotr Bojanowski, Maximilian Nickel, Tomas Mikolov
arXiv 2017
Convolutional 2D Knowledge Graph Embeddings
Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, Sebastian Riedel
AAAI 2018
Knowledge Graph Embedding With Iterative Guidance From Soft Rules
Shu Guo, Quan Wang, Lihong Wang, Bin Wang, Li Guo
AAAI 2018
KBGAN: Adversarial Learning for Knowledge Graph Embeddings
Liwei Cai, William Yang Wang
NAACL 2018
Improving Knowledge Graph Embedding Using Simple Constraints
Boyang Ding, Quan Wang, Bin Wang, Li Guo
ACL 2018
SimplE Embedding for Link Prediction in Knowledge Graphs
Seyed Mehran Kazemi, David Poole
NeurIPS 2018
A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network
Dai Quoc Nguyen, Tu Dinh Nguyen, Dat Quoc Nguyen, Dinh Phung
NAACL 2018
Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning
Wen Zhang, Bibek Paudel, Liang Wang, Jiaoyan Chen, Hai Zhu, Wei Zhang, Abraham Bernstein, Huajun Chen
WWW 2019
RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, Jian Tang
ICLR 2019
Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs
Deepak Nathani, Jatin Chauhan, Charu Sharma, Manohar Kaul
ACL 2019
Probabilistic Logic Neural Networks for Reasoning
Meng Qu, Jian Tang
NeurIPS 2019
Quaternion Knowledge Graph Embeddings
Shuai Zhang, Yi Tay, Lina Yao, Qi Liu
NeurIPS 2019
Quantum Embedding of Knowledge for Reasoning
Dinesh Garg, Santosh K. Srivastava, Hima Karanam
NeurIPS 2019
Multi-relational Poincaré Graph Embeddings
Ivana Balaževic, Carl Allen, Timothy Hospedales
NeurIPS 2019
Dynamically Pruned Message Passing Networks for Large-scale Knowledge Graph Reasoning
Xiaoran Xu, Wei Feng, Yunsheng Jiang, Xiaohui Xie, Zhiqing Sun, Zhi-Hong Deng
ICLR 2020
3 Graph Neural Networks
Revisiting Semi-supervised Learning with Graph Embeddings
Zhilin Yang, William W. Cohen, Ruslan Salakhutdinov
ICML 2016
Semi-Supervised Classification with Graph Convolutional Networks
Thomas N. Kipf, Max Welling
ICLR 2017
Neural Message Passing for Quantum Chemistry
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, George E. Dahl
ICML 2017
Hoang Nguyen, Tsuyoshi Murata
IJCAI 2017
Learning Graph Representations with Embedding Propagation
Alberto Garcia-Duran, Mathias Niepert
NIPS 2017
Inductive Representation Learning on Large Graphs
William L. Hamilton, Rex Ying, Jure Leskovec
NIPS 2017
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, Yoshua Bengio
ICLR 2018
FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling
Jie Chen, Tengfei Ma, Cao Xiao
ICLR 2018
Representation Learning on Graphs with Jumping Knowledge Networks
Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, Stefanie Jegelka
ICML 2018
Stochastic Training of Graph Convolutional Networks with Variance Reduction
Jianfei Chen, Jun Zhu, Le Song
ICML 2018
Large-Scale Learnable Graph Convolutional Networks
Hongyang Gao, Zhengyang Wang, Shuiwang Ji
KDD 2018
Adaptive Sampling Towards Fast Graph Representation Learning
Wenbing Huang, Tong Zhang, Yu Rong, Junzhou Huang
NeurIPS 2018
Hierarchical Graph Representation Learning with Differentiable Pooling
Rex Ying, Jiaxuan You, Christopher Morris, Xiang Ren, William L. Hamilton, Jure Leskovec
NeurIPS 2018
Bayesian Semi-supervised Learning with Graph Gaussian Processes
Yin Cheng Ng, Nicolò Colombo, Ricardo Silva
NeurIPS 2018
Pitfalls of Graph Neural Network Evaluation
Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, Stephan Günnemann
arXiv 2018
Heterogeneous Graph Attention Network
Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Peng Cui, P. Yu, Yanfang Ye
WWW 2019
Bayesian graph convolutional neural networks for semi-supervised classification
Yingxue Zhang, Soumyasundar Pal, Mark Coates, Deniz Üstebay
AAAI 2019
How Powerful are Graph Neural Networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka
ICLR 2019
LanczosNet: Multi-Scale Deep Graph Convolutional Networks
Renjie Liao, Zhizhen Zhao, Raquel Urtasun, Richard S. Zemel
ICLR 2019
Bingbing Xu, Huawei Shen, Qi Cao, Yunqi Qiu, Xueqi Cheng
ICLR 2019
Supervised Community Detection with Line Graph Neural Networks
Zhengdao Chen, Xiang Li, Joan Bruna
ICLR 2019
Predict then Propagate: Graph Neural Networks meet Personalized PageRank
Johannes Klicpera, Aleksandar Bojchevski, Stephan Günnemann
ICLR 2019
Invariant and Equivariant Graph Networks
Haggai Maron, Heli Ben-Hamu, Nadav Shamir, Yaron Lipman
ICLR 2019
Zhang Xinyi, Lihui Chen
ICLR 2019
MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing
Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, Aram Galstyan
ICML 2019
Hongyang Gao, Shuiwang Ji
ICML 2019
Disentangled Graph Convolutional Networks
Jianxin Ma, Peng Cui, Kun Kuang, Xin Wang, Wenwu Zhu
ICML 2019
GMNN: Graph Markov Neural Networks
Meng Qu, Yoshua Bengio, Jian Tang
ICML 2019
Simplifying Graph Convolutional Networks
Felix Wu, Tianyi Zhang, Amauri Holanda de Souza Jr., Christopher Fifty, Tao Yu, Kilian Q. Weinberger
ICML 2019
Position-aware Graph Neural Networks
Jiaxuan You, Rex Ying, Jure Leskovec
ICML 2019
Junhyun Lee, Inyeop Lee, Jaewoo Kang
ICML 2019
Relational Pooling for Graph Representations
Ryan L. Murphy, Balasubramaniam Srinivasan, Vinayak Rao, Bruno Ribeiro
ICML 2019
Graph Representation Learning via Hard and Channel-Wise Attention Networks
Hongyang Gao, Shuiwang Ji
KDD 2019
Conditional Random Field Enhanced Graph Convolutional Neural Networks
Hongchang Gao, Jian Pei, Heng Huang
KDD 2019
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, Cho-Jui Hsieh
KDD 2019
DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification
Jun Wu, Jingrui He, Jiejun Xu
KDD 2019
HetGNN: Heterogeneous Graph Neural Network
Chuxu Zhang, Dongjin Song, Chao Huang, Ananthram Swami, Nitesh V. Chawla
KDD 2019
Graph Recurrent Networks with Attributed Random Walks
Xiao Huang, Qingquan Song, Yuening Li, Xia Hu
KDD 2019
Graph Convolutional Networks with EigenPooling
Yao Ma, Suhang Wang, Charu Aggarwal, Jiliang Tang
KDD 2019
DFNets: Spectral CNNs for Graphs with Feedback-Looped Filters
Asiri Wijesinghe, Qing Wang
NeurIPS 2019
Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology
Nima Dehmamy, Albert-László Barabási, Rose Yu
NeurIPS 2019
A Flexible Generative Framework for Graph-based Semi-supervised Learning
Jiaqi Ma, Weijing Tang, Ji Zhu, Qiaozhu Mei
NeurIPS 2019
Rethinking Kernel Methods for Node Representation Learning on Graphs
Yu Tian, Long Zhao, Xi Peng, Dimitris N. Metaxas
NeurIPS 2019
Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
Sitao Luan, Mingde Zhao, Xiao-Wen Chang, Doina Precup
NeurIPS 2019
N-Gram Graph: A Simple Unsupervised Representation for Molecules
Shengchao Liu, Thevaa Chandereng, Yingyu Liang
NeurIPS 2019
DeepGCNs: Can GCNs Go as Deep as CNNs?
Guohao Li, Matthias Muller, Ali Thabet, Bernard Ghanem
ICCV 2019
Continuous Graph Neural Networks
Louis-Pascal A. C. Xhonneux, Meng Qu, Jian Tang
arXiv 2019
Ze Ye, Kin Sum Liu, Tengfei Ma, Jie Gao, Chao Chen
ICLR 2020
Amir hosein Khasahmadi, Kaveh Hassani, Parsa Moradi, Leo Lee, Quaid Morris
ICLR 2020
Strategies for Pre-training Graph Neural Networks
Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, Jure Leskovec
ICLR 2020
4 Applications of Graph Deep Learning
4.1 Natural Language Processing
Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling
Diego Marcheggiani, Ivan Titov
EMNLP 2017
Graph Convolutional Encoders for Syntax-aware Neural Machine Translation
Joost Bastings, Ivan Titov, Wilker Aziz, Diego Marcheggiani, Khalil Sima’an
EMNLP 2017
Graph-based Neural Multi-Document Summarization
Michihiro Yasunaga, Rui Zhang, Kshitijh Meelu, Ayush Pareek, Krishnan Srinivasan, Dragomir Radev
CoNLL 2017
QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension
Adams Wei Yu, David Dohan, Minh-Thang Luong, Rui Zhao, Kai Chen, Mohammad Norouzi, Quoc V. Le
ICLR 2018
A Structured Self-attentive Sentence Embedding
Zhouhan Lin, Minwei Feng, Cicero Nogueira dos Santos, Mo Yu, Bing Xiang, Bowen Zhou, Yoshua Bengio
ICLR 2018
Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering
Daniil Sorokin, Iryna Gurevych
COLING 2018
Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks
Diego Marcheggiani, Joost Bastings, Ivan Titov
NAACL 2018
Linguistically-Informed Self-Attention for Semantic Role Labeling
Emma Strubell, Patrick Verga, Daniel Andor, David Weiss, Andrew McCallum
EMNLP 2018
Graph Convolution over Pruned Dependency Trees Improves Relation Extraction
Yuhao Zhang, Peng Qi, Christopher D. Manning
EMNLP 2018
A Graph-to-Sequence Model for AMR-to-Text Generation
Linfeng Song, Yue Zhang, Zhiguo Wang, Daniel Gildea
ACL 2018
Graph-to-Sequence Learning using Gated Graph Neural Networks
Daniel Beck, Gholamreza Haffari, Trevor Cohn
ACL 2018
Graph Convolutional Networks for Text Classification
Liang Yao, Chengsheng Mao, Yuan Luo
AAAI 2019
Differentiable Perturb-and-Parse: Semi-Supervised Parsing with a Structured Variational Autoencoder
Caio Corro, Ivan Titov
ICLR 2019
Structured Neural Summarization
Patrick Fernandes, Miltiadis Allamanis, Marc Brockschmid
ICLR 2019
Multi-task Learning over Graph Structures
Pengfei Liu, Jie Fu, Yue Dong, Xipeng Qiu, Jackie Chi Kit Cheung
AAAI 2019
Imposing Label-Relational Inductive Bias for Extremely Fine-Grained Entity Typing
Wenhan Xiong, Jiawei Wu, Deren Lei, Mo Yu, Shiyu Chang, Xiaoxiao Guo, William Yang Wang
NAACL 2019
Single Document Summarization as Tree Induction
Yang Liu, Ivan Titov, Mirella Lapata
NAACL 2019
Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks
Ningyu Zhang, Shumin Deng, Zhanlin Sun, Guanying Wang, Xi Chen, Wei Zhang, Huajun Chen
NAACL 2019
Graph Neural Networks with Generated Parameters for Relation Extraction
Hao Zhu, Yankai Lin, Zhiyuan Liu, Jie Fu, Tat-seng Chua, Maosong Sun
ACL 2019
Dynamically Fused Graph Network for Multi-hop Reasoning
Yunxuan Xiao, Yanru Qu, Lin Qiu, Hao Zhou, Lei Li, Weinan Zhang, Yong Yu
ACL 2019
Chang Li, Dan Goldwasser
ACL 2019
Attention Guided Graph Convolutional Networks for Relation Extraction
Zhijiang Guo, Yan Zhang, Wei Lu
ACL 2019
Shikhar Vashishth, Manik Bhandari, Prateek Yadav, Piyush Rai, Chiranjib Bhattacharyya, Partha Talukdar
ACL 2019
GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction
Tsu-Jui Fu, Peng-Hsuan Li, Wei-Yun Ma
ACL 2019
Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs
Ming Tu, Guangtao Wang, Jing Huang, Yun Tang, Xiaodong He, Bowen Zhou
ACL 2019
Cognitive Graph for Multi-Hop Reading Comprehension at Scale
Ming Ding, Chang Zhou, Qibin Chen, Hongxia Yang, Jie Tang
ACL 2019
Coherent Comment Generation for Chinese Articles with a Graph-to-Sequence Model
Wei Li, Jingjing Xu, Yancheng He, Shengli Yan, Yunfang Wu, Xu Sun
ACL 2019
Matching Article Pairs with Graphical Decomposition and Convolutions
Bang Liu, Di Niu, Haojie Wei, Jinghong Lin, Yancheng He, Kunfeng Lai, Yu Xu
ACL 2019
Embedding Imputation with Grounded Language Information
Ziyi Yang, Chenguang Zhu, Vin Sachidananda, Eric Darve
ACL 2019
Chang Li, Dan Goldwasser
ACL 2019
A Neural Multi-digraph Model for Chinese NER with Gazetteers
Ruixue Ding, Pengjun Xie, Xiaoyan Zhang, Wei Lu, Linlin Li, Luo Si
ACL 2019
Tree Communication Models for Sentiment Analysis
Yuan Zhang, Yue Zhang
ACL 2019
A2N: Attending to Neighbors for Knowledge Graph Inference
Trapit Bansal, Da-Cheng Juan, Sujith Ravi, Andrew McCallum
ACL 2019
Daesik Kim, Seonhoon Kim, Nojun Kwak
ACL 2019
Yinchuan Xu, Junlin Yang
ACL 2019 Workshop
Learning Graph Pooling and Hybrid Convolutional Operations for Text Representations
Hongyang Gao, Yongjun Chen, Shuiwang Ji
WWW 2019
Learning to Create Sentence Semantic Relation Graphs for Multi-Document Summarization
Diego Antognini, Boi Faltings
EMNLP 2019
Dependency-Guided LSTM-CRF for Named Entity Recognition
Zhanming Jie, Wei Lu
EMNLP 2019
Penghui Wei, Nan Xu, Wenji Mao
EMNLP 2019
DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation
Deepanway Ghosal, Navonil Majumder, Soujanya Poria, Niyati Chhaya, Alexander Gelbukh
EMNLP 2019
Modeling Graph Structure in Transformer for Better AMR-to-Text Generation
Jie Zhu, Junhui Li, Muhua Zhu, Longhua Qian, Min Zhang, Guodong Zhou
EMNLP 2019
KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning
Bill Yuchen Lin, Xinyue Chen, Jamin Chen, Xiang Ren
EMNLP 2019
4.2 Computer Vision
3D Graph Neural Networks for RGBD Semantic Segmentation
Xiaojuan Qi, Renjie Liao, Jiaya Jia, Sanja Fidler, Raquel Urtasun
ICCV 2017
Situation Recognition With Graph Neural Networks
Ruiyu Li, Makarand Tapaswi, Renjie Liao, Jiaya Jia, Raquel Urtasun, Sanja Fidler
ICCV 2017
Graph-Based Classification of Omnidirectional Images
Renata Khasanova, Pascal Frossard
ICCV 2017
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
Sijie Yan, Yuanjun Xiong, Dahua Lin
AAAI 2018
Image Generation from Scene Graphs
Justin Johnson, Agrim Gupta, Li Fei-Fei
CVPR 2018
FoldingNet: Point Cloud Auto-Encoder via Deep Grid Deformation
Yaoqing Yang, Chen Feng, Yiru Shen, Dong Tian
CVPR 2018
PPFNet: Global Context Aware Local Features for Robust 3D Point Matching
Haowen Deng, Tolga Birdal, Slobodan Ilic
CVPR 2018
Iterative Visual Reasoning Beyond Convolutions
Xinlei Chen, Li-Jia Li, Li Fei-Fei, Abhinav Gupta
CVPR 2018
Ilya Kostrikov, Zhongshi Jiang, Daniele Panozzo, Denis Zorin, Joan Bruna
CVPR 2018
FeaStNet: Feature-Steered Graph Convolutions for 3D Shape Analysis
Nitika Verma, Edmond Boyer, Jakob Verbeek
CVPR 2018
Learning to Act Properly: Predicting and Explaining Affordances From Images
Ching-Yao Chuang, Jiaman Li, Antonio Torralba, Sanja Fidler
CVPR 2018
Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling
Yiru Shen, Chen Feng, Yaoqing Yang, Dong Tian
CVPR 2018
Deformable Shape Completion With Graph Convolutional Autoencoders
Or Litany, Alex Bronstein, Michael Bronstein, Ameesh Makadia
CVPR 2018
Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images
Nanyang Wang, Yinda Zhang, Zhuwen Li, Yanwei Fu, Wei Liu, Yu-Gang Jiang
ECCV 2018
Learning Human-Object Interactions by Graph Parsing Neural Networks
Siyuan Qi, Wenguan Wang, Baoxiong Jia, Jianbing Shen, Song-Chun Zhu
ECCV 2018
Generating 3D Faces using Convolutional Mesh Autoencoders
Anurag Ranjan, Timo Bolkart, Soubhik Sanyal, Michael J. Black
ECCV 2018
Learning SO(3) Equivariant Representations with Spherical CNNs
Carlos Esteves, Christine Allen-Blanchette, Ameesh Makadia, Kostas Daniilidis
ECCV 2018
Neural Graph Matching Networks for Fewshot 3D Action Recognition
Michelle Guo, Edward Chou, De-An Huang, Shuran Song, Serena Yeung, Li Fei-Fei
ECCV 2018
Multi-Kernel Diffusion CNNs for Graph-Based Learning on Point Clouds
Lasse Hansen, Jasper Diesel, Mattias P. Heinrich
ECCV 2018
Hierarchical Video Frame Sequence Representation with Deep Convolutional Graph Network
Feng Mao, Xiang Wu, Hui Xue, Rong Zhang
ECCV 2018
Graph R-CNN for Scene Graph Generation
Jianwei Yang, Jiasen Lu, Stefan Lee, Dhruv Batra, Devi Parikh
ECCV 2018
Exploring Visual Relationship for Image Captioning
Ting Yao, Yingwei Pan, Yehao Li, Tao Mei
ECCV 2018
Beyond Grids: Learning Graph Representations for Visual Recognition
Yin Li, Abhinav Gupta
NeurIPS 2018
Learning Conditioned Graph Structures for Interpretable Visual Question Answering
Will Norcliffe-Brown, Efstathios Vafeias, Sarah Parisot
NeurIPS 2018
LinkNet: Relational Embedding for Scene Graph
Sanghyun Woo, Dahun Kim, Donghyeon Cho, In So Kweon
NeurIPS 2018
Flexible Neural Representation for Physics Prediction
Damian Mrowca, Chengxu Zhuang, Elias Wang, Nick Haber, Li Fei-Fei, Joshua B. Tenenbaum, Daniel L. K. Yamins
NeurIPS 2018
Learning Localized Generative Models for 3D Point Clouds via Graph Convolution
Diego Valsesia, Giulia Fracastoro, Enrico Magli
ICLR 2019
Graph-Based Global Reasoning Networks
Yunpeng Chen, Marcus Rohrbach, Zhicheng Yan, Shuicheng Yan, Jiashi Feng, Yannis Kalantidis
CVPR 2019
Deep Graph Laplacian Regularization for Robust Denoising of Real Images
Jin Zeng, Jiahao Pang, Wenxiu Sun, Gene Cheung
CVPR 2019
Learning Context Graph for Person Search
Yichao Yan, Qiang Zhang, Bingbing Ni, Wendong Zhang, Minghao Xu, Xiaokang Yang
CVPR 2019
Graphonomy: Universal Human Parsing via Graph Transfer Learning
Ke Gong, Yiming Gao, Xiaodan Liang, Xiaohui Shen, Meng Wang, Liang Lin
CVPR 2019
Masked Graph Attention Network for Person Re-Identification for_Person_Re-Identification_CVPRW_2019_paper.html>`_
Liqiang Bao, Bingpeng Ma, Hong Chang, Xilin Chen
CVPR 2019
Learning to Cluster Faces on an Affinity Graph
Lei Yang, Xiaohang Zhan, Dapeng Chen, Junjie Yan, Chen Change Loy, Dahua Lin
CVPR 2019
Actional-Structural Graph Convolutional Networks for Skeleton-Based Action Recognition
Maosen Li, Siheng Chen, Xu Chen, Ya Zhang, Yanfeng Wang, Qi Tian
CVPR 2019
Adaptively Connected Neural Networks
Guangrun Wang, Keze Wang, Liang Lin
CVPR 2019
Reasoning Visual Dialogs with Structural and Partial Observations
Zilong Zheng, Wenguan Wang, Siyuan Qi, Song-Chun Zhu
CVPR 2019
MeshCNN: A Network with an Edge
Rana Hanocka, Amir Hertz, Noa Fish, Raja Giryes, Shachar Fleishman, Daniel Cohen-Or
SIGGRAPH 2019
Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learning
Jiwoong Park, Minsik Lee, Hyung Jin Chang, Kyuewang Lee, Jin Young Choi
ICCV 2019
Pixel2Mesh++: Multi-View 3D Mesh Generation via Deformation
Chao Wen, Yinda Zhang, Zhuwen Li, Yanwei Fu
ICCV 2019
Learning Trajectory Dependencies for Human Motion Prediction
Wei Mao, Miaomiao Liu, Mathieu Salzmann, Hongdong Li
ICCV 2019
Graph-Based Object Classification for Neuromorphic Vision Sensing
Yin Bi, Aaron Chadha, Alhabib Abbas, Eirina Bourtsoulatze, Yiannis Andreopoulos
ICCV 2019
Fashion Retrieval via Graph Reasoning Networks on a Similarity Pyramid
Zhanghui Kuang, Yiming Gao, Guanbin Li, Ping Luo, Yimin Chen, Liang Lin, Wayne Zhang
ICCV 2019
Understanding Human Gaze Communication by Spatio-Temporal Graph Reasoning
Lifeng Fan, Wenguan Wang, Siyuan Huang, Xinyu Tang, Song-Chun Zhu
ICCV 2019
Visual Semantic Reasoning for Image-Text Matching
Kunpeng Li, Yulun Zhang, Kai Li, Yuanyuan Li, Yun Fu
ICCV 2019
Graph Convolutional Networks for Temporal Action Localization
Runhao Zeng, Wenbing Huang, Mingkui Tan, Yu Rong, Peilin Zhao, Junzhou Huang, Chuang Gan
ICCV 2019
Semantically-Regularized Logic Graph Embeddings
Yaqi Xie, Ziwei Xu, Kuldeep Meel, Mohan S Kankanhalli, Harold Soh
NeurIPS 2019
4.3 Recommender Systems
Graph Convolutional Neural Networks for Web-Scale Recommender Systems
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, Jure Leskovec
KDD 2018
PinSage
SocialGCN: An Efficient Graph Convolutional Network based Model for Social Recommendation
Le Wu, Peijie Sun, Richang Hong, Yanjie Fu, Xiting Wang, Meng Wang
AAAI 2018
GCN, Social recommendation
Session-based Social Recommendation via Dynamic Graph Attention Networks
Weiping Song, Zhiping Xiao, Yifan Wang, Laurent Charlin, Ming Zhang, Jian Tang
WSDM 2019
Social recommendation, session-based, GAT
Qitian Wu, Hengrui Zhang, Xiaofeng Gao, Peng He, Paul Weng, Han Gao, Guihai Chen
WWW 2019
Social recommendation, GAT
Graph Neural Networks for Social Recommendation
Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, Dawei Yin
WWW 2019
Social recommendation, GNN
Session-based Recommendation with Graph Neural Networks
Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, Tieniu Tan
AAAI 2019
Session-based recommendation, GNN
A Neural Influence Diffusion Model for Social Recommendation
Le Wu, Peijie Sun, Yanjie Fu, Richang Hong, Xiting Wang, Meng Wang
SIGIR 2019
Social Recommendation, diffusion
Neural Graph Collaborative Filtering
Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, Tat-Seng Chua
SIGIR 2019
Collaborative Filtering, GNN
Binarized Collaborative Filtering with Distilling Graph Convolutional Networks
Haoyu Wang, Defu Lian, Yong Ge
IJCAI 2019
IntentGC: A Scalable Graph Convolution Framework Fusing Heterogeneous Information for Recommendation
Jun Zhao, Zhou Zhou, Ziyu Guan, Wei Zhao, Wei Ning, Guang Qiu, Xiaofei He
KDD 2019
An End-to-End Neighborhood-based Interaction Model for Knowledge-enhanced Recommendation
Yanru Qu, Ting Bai, Weinan Zhang, Jianyun Nie, Jian Tang
KDD 2019 Workshop
4.4 Link Prediction
Link Prediction Based on Graph Neural Networks
Muhan Zhang, Yixin Chen
NeurIPS 2018
Link Prediction via Subgraph Embedding-Based Convex Matrix Completion
Zhu Cao, Linlin Wang, Gerard de Melo
AAAI 2018
Graph Convolutional Matrix Completion
Rianne van den Berg, Thomas N. Kipf, Max Welling
KDD 2018 Workshop
Semi-Implicit Graph Variational Auto-Encoders
Arman Hasanzadeh, Ehsan Hajiramezanali, Nick Duffield , Krishna Narayanan, Mingyuan Zhou, Xiaoning Qian
NeurIPS 2019
4.5 Influence Prediction
DeepInf: Social Influence Prediction with Deep Learning
Jiezhong Qiu, Jian Tang, Hao Ma, Yuxiao Dong, Kuansan Wang, Jie Tang
KDD 2018
Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks
Namyong Park, Andrey Kan, Xin Luna Dong, Tong Zhao, Christos Faloutsos
KDD 2019
4.6 Neural Architecture Search
Graph HyperNetworks for Neural Architecture Search
Chris Zhang, Mengye Ren, Raquel Urtasun
ICLR 2019
D-VAE: A Variational Autoencoder for Directed Acyclic Graphs
Muhan Zhang, Shali Jiang, Zhicheng Cui, Roman Garnett, Yixin Chen
NeurIPS 2019
4.7 Reinforcement Learning
Action Schema Networks: Generalised Policies with Deep Learning
Sam Toyer, Felipe Trevizan, Sylvie Thiebaux, Lexing Xie
AAAI 2018
NerveNet: Learning Structured Policy with Graph Neural Networks
Tingwu Wang, Renjie Liao, Jimmy Ba, Sanja Fidler
ICLR 2018
Graph Networks as Learnable Physics Engines for Inference and Control
Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg, Josh Merel, Martin Riedmiller
ICML 2018
Learning Policy Representations in Multiagent Systems
Aditya Grover, Maruan Al-Shedivat, Jayesh K. Gupta, Yura Burda, Harrison Edwards
ICML 2018
Relational recurrent neural networks
Adam Santoro, Ryan Faulkner, David Raposo, Jack Rae, Mike Chrzanowski,Théophane Weber, Daan Wierstra, Oriol Vinyals, Razvan Pascanu, Timothy Lillicrap
NeurIPS 2018
Transfer of Deep Reactive Policies for MDP Planning
Aniket Bajpai, Sankalp Garg, Mausam
NeurIPS 2018
Neural Graph Evolution: Towards Efficient Automatic Robot Design
Tingwu Wang, Yuhao Zhou, Sanja Fidler, Jimmy Ba
ICLR 2019
No Press Diplomacy: Modeling Multi-Agent Gameplay
Philip Paquette, Yuchen Lu, Steven Bocco, Max O. Smith, Satya Ortiz-Gagne, Jonathan K. Kummerfeld, Satinder Singh, Joelle Pineau, Aaron Courville
NeurIPS 2019
4.8 Combinatorial Optimization
Learning Combinatorial Optimization Algorithms over Graphs
Hanjun Dai, Elias B. Khalil, Yuyu Zhang, Bistra Dilkina, Le Song
NeurIPS 2017
Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search
Zhuwen Li, Qifeng Chen, Vladlen Koltun
NeurIPS 2018
Reinforcement Learning for Solving the Vehicle Routing Problem
Mohammadreza Nazari, Afshin Oroojlooy, Lawrence V. Snyder, Martin Takáč
NeurIPS 2018
Attention, Learn to Solve Routing Problems!
Wouter Kool, Herke van Hoof, Max Welling
ICLR 2019
Learning a SAT Solver from Single-Bit Supervision
Daniel Selsam, Matthew Lamm, Benedikt Bünz, Percy Liang, Leonardo de Moura, David L. Dill
ICLR 2019
An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem
Chaitanya K. Joshi, Thomas Laurent, Xavier Bresson
arXiv 2019
Approximation Ratios of Graph Neural Networks for Combinatorial Problems
Ryoma Sato, Makoto Yamada, Hisashi Kashima
NeurIPS 2019
Exact Combinatorial Optimization with Graph Convolutional Neural Networks
Maxime Gasse, Didier Chételat, Nicola Ferroni, Laurent Charlin, Andrea Lodi
NeurIPS 2019
On Learning Paradigms for the Travelling Salesman Problem
Chaitanya K. Joshi, Thomas Laurent, Xavier Bresson
NeurIPS 2019 Workshop
4.9 Adversarial Attack and Robustness
Adversarial Attack on Graph Structured Data
Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, Le Song
ICML 2018
Adversarial Attacks on Neural Networks for Graph Data
Daniel Zügner, Amir Akbarnejad, Stephan Günnemann
KDD 2018
Adversarial Attacks on Graph Neural Networks via Meta Learning
Daniel Zügner, Stephan Günnemann
ICLR 2019
Robust Graph Convolutional Networks Against Adversarial Attacks
Dingyuan Zhu, Ziwei Zhang, Peng Cui, Wenwu Zhu
KDD 2019
Certifiable Robustness and Robust Training for Graph Convolutional Networks
Daniel Zügner, Stephan Günnemann
KDD 2019
4.10 Graph Matching
REGAL: Representation Learning-based Graph Alignment
Mark Heimann, Haoming Shen, Tara Safavi, Danai Koutra
CIKM 2018
Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks
Zhichun Wang, Qingsong Lv, Xiaohan Lan, Yu Zhang
EMNLP 2018
Learning Combinatorial Embedding Networks for Deep Graph Matching
Runzhong Wang, Junchi Yan, Xiaokang Yang
ICCV 2019
Matthias Fey, Jan E. Lenssen, Christopher Morris, Jonathan Masci, Nils M. Kriege
ICLR 2020
4.11 Meta Learning and Few-shot Learning
Few-Shot Learning with Graph Neural Networks
Victor Garcia, Joan Bruna
ICLR 2018
Learning Steady-States of Iterative Algorithms over Graphs
Hanjun Dai, Zornitsa Kozareva, Bo Dai, Alex Smola, Le Song
ICML 2018
Learning to Propagate for Graph Meta-Learning
Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang
NeurIPS 2019
Few-Shot Learning on Graphs via Super-Classes based on Graph Spectral Measures
Jatin Chauhan, Deepak Nathani, Manohar Kaul
ICLR 2020
Automated Relational Meta-learning
Huaxiu Yao, Xian Wu, Zhiqiang Tao, Yaliang Li, Bolin Ding, Ruirui Li, Zhenhui Li
ICLR 2020
4.12 Structure Learning
Neural Relational Inference for Interacting Systems
Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, Richard Zemel
ICML 2018
Brain Signal Classification via Learning Connectivity Structure
Soobeom Jang, Seong-Eun Moon, Jong-Seok Lee
arXiv 2019
A Flexible Generative Framework for Graph-based Semi-supervised Learning
Jiaqi Ma, Weijing Tang, Ji Zhu, Qiaozhu Mei
NeurIPS 2019
Joint embedding of structure and features via graph convolutional networks
Sébastien Lerique, Jacob Levy Abitbol, Márton Karsai
arXiv 2019
Variational Spectral Graph Convolutional Networks
Louis Tiao, Pantelis Elinas, Harrison Nguyen, Edwin V. Bonilla
arXiv 2019
Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning
Yanbin Liu, Juho Lee, Minseop Park, Saehoon Kim, Eunho Yang, Sung Ju Hwang, Yi Yang
ICLR 2019
Graph Learning Network: A Structure Learning Algorithm
Darwin Saire Pilco, Adín Ramírez Rivera
ICML 2019 Workshop
Learning Discrete Structures for Graph Neural Networks
Luca Franceschi, Mathias Niepert, Massimiliano Pontil, Xiao He
ICML 2019
Graphite: Iterative Generative Modeling of Graphs
Aditya Grover, Aaron Zweig, Stefano Ermon
ICML 2019
4.13 Bioinformatics and Chemistry
Protein Interface Prediction using Graph Convolutional Networks
Alex Fout, Jonathon Byrd, Basir Shariat, Asa Ben-Hur
NeurIPS 2017
Modeling Polypharmacy Side Effects with Graph Convolutional Networks
Marinka Zitnik, Monica Agrawal, Jure Leskovec
Bioinformatics 2018
Fangping Wan, Lixiang Hong, An Xiao, Tao Jiang, Jianyang Zeng
Bioinformatics 2018
Mario Krenn, Florian Häse, AkshatKumar Nigam, Pascal Friederich, Alán Aspuru-Guzik
arXiv 2019
Drug-Drug Adverse Effect Prediction with Graph Co-Attention
Andreea Deac, Yu-Hsiang Huang, Petar Veličković, Pietro Liò, Jian Tang
ICML 2019 Workshop
Peng Han, Peng Yang, Peilin Zhao, Shuo Shang, Yong Liu, Jiayu Zhou, Xin Gao, Panos Kalnis
KDD 2019
Guy Shtar, Lior Rokach, Bracha Shapira
arXiv 2019
Yu Li, Hiroyuki Kuwahara, Peng Yang, Le Song, Xin Gao
bioRxiv 2019
Identifying Protein-Protein Interaction using Tree LSTM and Structured Attention
Mahtab Ahmed, Jumayel Islam, Muhammad Rifayat Samee, Robert E. Mercer
ICSC 2019
Peng Han, Peng Yang, Peilin Zhao, Shuo Shang, Yong Liu, Jiayu Zhou, Xin Gao, Panos Kalnis
KDD 2019
Towards perturbation prediction of biological networks using deep learning
Diya Li, Jianxi Gao
Nature 2019
Directional Message Passing for Molecular Graphs
Johannes Klicpera, Janek Groß, Stephan Günnemann
ICLR 2020
4.14 Graph Algorithms
Neural Execution of Graph Algorithms
Petar Veličković, Rex Ying, Matilde Padovano, Raia Hadsell, Charles Blundell
ICLR 2020
4.15 Theorem Proving
Premise Selection for Theorem Proving by Deep Graph Embedding
Mingzhe Wang, Yihe Tang, Jian Wang, Jia Deng
NeurIPS 2017
5 Graph Generation
GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models
Jiaxuan You, Rex Ying, Xiang Ren, William L. Hamilton, Jure Leskovec
ICML 2018
NetGAN: Generating Graphs via Random Walks
Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, Stephan Günnemann
ICML 2018
Learning Deep Generative Models of Graphs
Yujia Li, Oriol Vinyals, Chris Dyer, Razvan Pascanu, Peter Battaglia
ICML 2018
Junction Tree Variational Autoencoder for Molecular Graph Generation
Wengong Jin, Regina Barzilay, Tommi Jaakkola
ICML 2018
MolGAN: An implicit generative model for small molecular graphs
Nicola De Cao, Thomas Kipf
arXiv 2018
Generative Modeling for Protein Structures
Namrata Anand, Po-Ssu Huang
NeurIPS 2018
Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders
Tengfei Ma, Jie Chen, Cao Xiao
NeurIPS 2018
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
Jiaxuan You, Bowen Liu, Rex Ying, Vijay Pande, Jure Leskovec
NeurIPS 2018
Constrained Graph Variational Autoencoders for Molecule Design
Qi Liu, Miltiadis Allamanis, Marc Brockschmidt, Alexander L. Gaunt
NeurIPS 2018
Learning Multimodal Graph-to-Graph Translation for Molecule Optimization
Wengong Jin, Kevin Yang, Regina Barzilay, Tommi Jaakkola
ICLR 2019
Generative Code Modeling with Graphs
Marc Brockschmidt, Miltiadis Allamanis, Alexander L. Gaunt, Oleksandr Polozov
ICLR 2019
DAG-GNN: DAG Structure Learning with Graph Neural Networks
Yue Yu, Jie Chen, Tian Gao, Mo Yu
ICML 2019
Graph to Graph: a Topology Aware Approach for Graph Structures Learning and Generation
Mingming Sun, Ping Li
AISTATS 2019
Jenny Liu, Aviral Kumar, Jimmy Ba, Jamie Kiros, Kevin Swersky
NeurIPS 2019
Conditional Structure Generation through Graph Variational Generative Adversarial Nets
Carl Yang, Peiye Zhuang, Wenhan Shi, Alan Luu, Pan Li
NeurIPS 2019
Efficient Graph Generation with Graph Recurrent Attention Networks
Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, Charlie Nash, William L. Hamilton, David Duvenaud, Raquel Urtasun, Richard Zemel
NeurIPS 2019
GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation
Chence Shi, Minkai Xu, Zhaocheng Zhu, Weinan Zhang, Ming Zhang, Jian Tang
ICLR 2020
6 Graph Layout and High-dimensional Data Visualization
Laurens van der Maaten, Geoffrey Hinton
JMLR 2008
Visualizing non-metric similarities in multiple maps
Laurens van der Maaten, Geoffrey Hinton
ML 2012
Visualizing Large-scale and High-dimensional Data
Jian Tang, Jingzhou Liu, Ming Zhang, Qiaozhu Mei
WWW 2016
GraphTSNE: A Visualization Technique for Graph-Structured Data
Yao Yang Leow, Thomas Laurent, Xavier Bresson
ICLR 2019 Workshop
7 Graph Representation Learning Systems
GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding
Zhaocheng Zhu, Shizhen Xu, Meng Qu, Jian Tang
WWW 2019
PyTorch-BigGraph: A Large-scale Graph Embedding System
Adam Lerer, Ledell Wu, Jiajun Shen, Timothee Lacroix, Luca Wehrstedt, Abhijit Bose, Alex Peysakhovich
SysML 2019
AliGraph: A Comprehensive Graph Neural Network Platform
Rong Zhu, Kun Zhao, Hongxia Yang, Wei Lin, Chang Zhou, Baole Ai, Yong Li, Jingren Zhou
VLDB 2019
Luca Costabello, Sumit Pai, Chan Le Van, Rory McGrath, Nicholas McCarthy, Pedro Tabacof
Alimama Engineering Platform Team, Alimama Search Advertising Algorithm Team
8 Datasets
ATOMIC: an atlas of machine commonsense for if-then reasoning
Maarten Sap, Ronan Le Bras, Emily Allaway, Chandra Bhagavatula, Nicholas Lourie, Hannah Rashkin, Brendan Roof, Noah A. Smith, Yejin Choi
AAAI 2019