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Literature of Deep Learning for Graphs

This is a paper list about deep learning for graphs.

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• 1   Node Representation Learning
  • 1.1   Unsupervised Node Representation Learning
  • 1.2   Node Representation Learning in Heterogeneous Graphs
  • 1.3   Node Representation Learning in Dynamic Graphs
• 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

Adversarial Network Embedding

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

Deep Graph Infomax

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

Generative Adversarial Network based Heterogeneous Bibliographic Network Representation for Personalized Citation Recommendation

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

Motif-Aware Graph Embeddings

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

Graph Attention Networks

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

Graph Wavelet Neural Network

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

Capsule Graph Neural Network

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

Graph U-Nets

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

Self-Attention Graph Pooling

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

Curvature Graph Network

Ze Ye, Kin Sum Liu, Tengfei Ma, Jie Gao, Chao Chen

ICLR 2020

Memory-based Graph Networks

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

Encoding Social Information with Graph Convolutional Networks for Political Perspective Detection in News Media

Chang Li, Dan Goldwasser

ACL 2019

Attention Guided Graph Convolutional Networks for Relation Extraction

Zhijiang Guo, Yan Zhang, Wei Lu

ACL 2019

Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks

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

Encoding Social Information with Graph Convolutional Networks forPolitical Perspective Detection in News Media

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

Textbook Question Answering with Multi-modal Context Graph Understanding and Self-supervised Open-set Comprehension

Daesik Kim, Seonhoon Kim, Nojun Kwak

ACL 2019

Look Again at the Syntax: Relational Graph Convolutional Network for Gendered Ambiguous Pronoun Resolution

Yinchuan Xu, Junlin Yang

ACL 2019 Workshop

https://github.com/ianycxu/RGCN-with-BERT

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

Modeling Conversation Structure and Temporal Dynamics for Jointly Predicting Rumor Stance and Veracity

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

Surface Networks

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

https://ranahanocka.github.io/MeshCNN/

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

Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems

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

Deep Graph Matching Consensus

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

NeoDTI: Neural Integration of Neighbor Information from a Heterogeneous Network for Discovering New Drug–target Interactions

Fangping Wan, Lixiang Hong, An Xiao, Tao Jiang, Jianyang Zeng

Bioinformatics 2018

SELFIES: a Robust Representation of Semantically Constrained Graphs with an Example Application in Chemistry

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

GCN-MF: Disease-Gene Association Identification By Graph Convolutional Networks and Matrix Factorization

Peng Han, Peng Yang, Peilin Zhao, Shuo Shang, Yong Liu, Jiayu Zhou, Xin Gao, Panos Kalnis

KDD 2019

Detecting drug-drug interactions using artificial neural networks and classic graph similarity measures

Guy Shtar, Lior Rokach, Bracha Shapira

arXiv 2019

PGCN: Disease gene prioritization by disease and gene embedding through graph convolutional neural networks

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

GCN-MF: Disease-Gene Association Identification By Graph Convolutional Networks and Matrix Factorization

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

Graph Normalizing Flows

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

Visualizing Data using t-SNE

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

Deep Graph Library

DGL Team

AmpliGraph

Luca Costabello, Sumit Pai, Chan Le Van, Rory McGrath, Nicholas McCarthy, Pedro Tabacof

Euler

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