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Literature of Deep Learning for Graphs

This is a paper list about deep learning for graphs.

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 1611

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
arXiv 1906

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
AAAI 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

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
KDD2019

2   Knowledge Graph Embedding

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

Learning Entity and Relation Embeddings for Knowledge Graph Completion

Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, Xuan Zhu
AAAI 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 2017.03

Fast Linear Model for Knowledge Graph Embeddings

Armand Joulin, Edouard Grave, Piotr Bojanowski, Maximilian Nickel, Tomas Mikolov
arXiv 2017.10

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
arXiv 1906

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 2018.11

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

:author: Ryan L. Murphy, Balasubramaniam Srinivasan, Vinayak Rao, Bruno Ribeiro
ICML 2019

Graph Representation Learning via Hard and Channel-Wise Attention Networks

:author: Hongyang Gao, Shuiwang Ji
KDD 2019

4   Applications of Graph Neural Networks

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

Learning Graph Pooling and Hybrid Convolutional Operations for Text Representations

Hongyang Gao, Yongjun Chen, Shuiwang Ji
WWW 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

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

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

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

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

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

4.8   Combinatorial Optimization

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

4.9   Adversarial Attack

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

4.10   Meta Learning

Learning Steady-States of Iterative Algorithms over Graphs

Hanjun Dai, Zornitsa Kozareva, Bo Dai, Alex Smola, Le Song
ICML 2018

4.11   Structure Learning

Few-Shot Learning with Graph Neural Networks

Victor Garcia, Joan Bruna
ICLR 2018

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 1905

A Flexible Generative Framework for Graph-based Semi-supervised Learning

Jiaqi Ma, Weijing Tang, Ji Zhu, Qiaozhu Mei
arXiv 1905

Joint embedding of structure and features via graph convolutional networks

Sébastien Lerique, Jacob Levy Abitbol, Márton Karsai
arXiv 1905

Variational Spectral Graph Convolutional Networks

Louis Tiao, Pantelis Elinas, Harrison Nguyen, Edwin V. Bonilla
arXiv 1906

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

4.12   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 1905

Drug-Drug Adverse Effect Prediction with Graph Co-Attention

Andreea Deac, Yu-Hsiang Huang, Petar Veličković, Pietro Liò, Jian Tang
arXiv 1905

4.13   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

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 1805

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

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

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

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