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 Neural Networks
- 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
- 4.10 Meta Learning
- 4.11 Structure Learning
- 4.12 Bioinformatics and Chemistry
- 4.13 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 SkienaKDD 2014Node classification, Random walk, Skip-gram
LINE: Large-scale Information Network Embedding
Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, Qiaozhu MeiWWW 2015First-order, Second-order, Node classification
GraRep: Learning Graph Representations with Global Structural Information
Shaosheng Cao, Wei Lu, Qiongkai XuCIKM 2015High-order, SVD
node2vec: Scalable Feature Learning for Networks
Aditya Grover, Jure LeskovecKDD 2016Breadth-first Search, Depth-first Search, Node Classification, Link Prediction
Variational Graph Auto-Encoders
Thomas N. Kipf, Max WellingarXiv 1611
Scalable Graph Embedding for Asymmetric Proximity
Chang Zhou, Yuqiong Liu, Xiaofei Liu, Zhongyi Liu, Jun GaoAAAI 2017
Fast Network Embedding Enhancement via High Order Proximity Approximation
Cheng Yang, Maosong Sun, Zhiyuan Liu, Cunchao TuIJCAI 2017
struc2vec: Learning Node Representations from Structural Identity
Leonardo F. R. Ribeiro, Pedro H. P. Savarese, Daniel R. FigueiredoKDD 2017Structural Identity
Poincaré Embeddings for Learning Hierarchical Representations
Maximilian Nickel, Douwe KielaNIPS 2017
VERSE: Versatile Graph Embeddings from Similarity Measures
Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Emmanuel MüllerWWW 2018
Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec
Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, Jie TangWSDM 2018
Learning Structural Node Embeddings via Diffusion Wavelets
Claire Donnat, Marinka Zitnik, David Hallac, Jure LeskovecKDD 2018
Quanyu Dai, Qiang Li, Jian Tang, Dan WangAAAI 2018
GraphGAN: Graph Representation Learning with Generative Adversarial Nets
Hongwei Wang, Jia Wang, Jialin Wang, Miao Zhao, Weinan Zhang, Fuzheng Zhang, Xing Xie, Minyi GuoAAAI 2018
A General View for Network Embedding as Matrix Factorization
Xin Liu, Tsuyoshi Murata, Kyoung-Sook Kim, Chatchawan Kotarasu, Chenyi ZhuangWSDM 2019
Petar Veličković, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, R Devon HjelmICLR 2019
NetSMF: Large-Scale Network Embedding as Sparse Matrix Factorization
Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Chi Wang, Kuansan Wang, Jie TangWWW 2019
Adversarial Training Methods for Network Embedding
Quanyu Dai, Xiao Shen, Liang Zhang, Qiang Li, Dan WangWWW 2019
vGraph: A Generative Model for Joint Community Detection and Node Representation Learning
Fan-Yun Sun, Meng Qu, Jordan Hoffmann, Chin-Wei Huang, Jian TangarXiv 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 GallinariWSDM 2014
PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks
Jian Tang, Meng Qu, Qiaozhu MeiKDD 2015Text Embedding, Heterogeneous Text Graphs
Heterogeneous Network Embedding via Deep Architectures
Shiyu Chang, Wei Han, Jiliang Tang, Guo-Jun Qi, Charu C. Aggarwal, Thomas S. HuangKDD 2015
Network Representation Learning with Rich Text Information
Cheng Yang, Zhiyuan Liu, Deli Zhao, Maosong Sun, Edward ChangAAAI 2015
Max-Margin DeepWalk: Discriminative Learning of Network Representation
Cunchao Tu, Weicheng Zhang, Zhiyuan Liu, Maosong SunIJCAI 2016
metapath2vec: Scalable Representation Learning for Heterogeneous Networks
Yuxiao Dong, Nitesh V. Chawla, Ananthram SwamiKDD 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 PengarXiv 2016
HIN2Vec: Explore Meta-paths in Heterogeneous Information Networks for Representation Learning
Tao-yang Fu, Wang-Chien Lee, Zhen LeiCIKM 2017
An Attention-based Collaboration Framework for Multi-View Network Representation Learning
Meng Qu, Jian Tang, Jingbo Shang, Xiang Ren, Ming Zhang, Jiawei HanCIKM 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. RaginCIKM 2017
Attributed Signed Network Embedding
Suhang Wang, Charu Aggarwal, Jiliang Tang, Huan LiuCIKM 2017
CANE: Context-Aware Network Embedding for Relation Modeling
Cunchao Tu, Han Liu, Zhiyuan Liu, Maosong SunACL 2017
PME: Projected Metric Embedding on Heterogeneous Networks for Link Prediction
Hongxu Chen, Hongzhi Yin, Weiqing Wang, Hao Wang, Quoc Viet Hung Nguyen, Xue LiKDD 2018
BiNE: Bipartite Network Embedding
Ming Gao, Leihui Chen, Xiangnan He, Aoying ZhouSIGIR 2018
StarSpace: Embed All The Things
Ledell Wu, Adam Fisch, Sumit Chopra, Keith Adams, Antoine Bordes, Jason WestonAAAI 2018
Exploring Expert Cognition for Attributed Network Embedding
Xiao Huang, Qingquan Song, Jundong Li, Xia HuWSDM 2018
SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction
Hongwei Wang, Fuzheng Zhang, Min Hou, Xing Xie, Minyi Guo, Qi LiuWSDM 2018
Multidimensional Network Embedding with Hierarchical Structures
Yao Ma, Zhaochun Ren, Ziheng Jiang, Jiliang Tang, Dawei YinWSDM 2018
Curriculum Learning for Heterogeneous Star Network Embedding via Deep Reinforcement Learning
Meng Qu, Jian Tang, Jiawei HanWSDM 2018
Xiaoyan Cai, Junwei Han, Libin YangAAAI 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 WangAAAI 2018
Efficient Attributed Network Embedding via Recursive Randomized Hashing
Wei Wu, Bin Li, Ling Chen, Chengqi ZhangIJCAI 2018
Deep Attributed Network Embedding
Hongchang Gao, Heng HuangIJCAI 2018
Co-Regularized Deep Multi-Network Embedding
Jingchao Ni, Shiyu Chang, Xiao Liu, Wei Cheng, Haifeng Chen, Dongkuan Xu, Xiang ZhangWWW 2018
Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks
Yu Shi, Qi Zhu, Fang Guo, Chao Zhang, Jiawei HanKDD 2018
Meta-Graph Based HIN Spectral Embedding: Methods, Analyses, and Insights
Carl Yang, Yichen Feng, Pan Li, Yu Shi, Jiawei HanICDM 2018
SIDE: Representation Learning in Signed Directed Networks
Junghwan Kim, Haekyu Park, Ji-Eun Lee, U KangWWW 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 SongICML 2017
Dyngem: Deep embedding method for dynamic graphs
Palash Goyal, Nitin Kamra, Xinran He, Yan LiuICLR 2017 Workshop
Attributed network embedding for learning in a dynamic environment
Jundong Li, Harsh Dani, Xia Hu, Jiliang Tang, Yi Chang, Huan LiuCIKM 2017
Dynamic Network Embedding by Modeling Triadic Closure Process
Lekui Zhou, Yang Yang, Xiang Ren, Fei Wu, Yueting ZhuangAAAI 2018
DepthLGP: Learning Embeddings of Out-of-Sample Nodes in Dynamic Networks
Jianxin Ma, Peng Cui, Wenwu ZhuAAAI 2018
TIMERS: Error-Bounded SVD Restart on Dynamic Networks
Ziwei Zhang, Peng Cui, Jian Pei, Xiao Wang, Wenwu ZhuAAAI 2018
Dynamic Embeddings for User Profiling in Twitter
Shangsong Liang, Xiangliang Zhang, Zhaochun Ren, Evangelos KanoulasKDD 2018
Dynamic Network Embedding : An Extended Approach for Skip-gram based Network Embedding
Lun Du, Yun Wang, Guojie Song, Zhicong Lu, Junshan WangIJCAI 2018
DyRep: Learning Representations over Dynamic Graphs
Rakshit Trivedi, Mehrdad Farajtabar, Prasenjeet Biswal, Hongyuan ZhaICLR 2019
Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks
Srijan Kumar, Xikun Zhang, Jure LeskovecKDD2019
2 Knowledge Graph Embedding
Translating Embeddings for Modeling Multi-relational Data
Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, Oksana YakhnenkoNIPS 2013
Knowledge Graph Embedding by Translating on Hyperplanes
Zhen Wang, Jianwen Zhang, Jianlin Feng, Zheng ChenAAAI 2014
Learning Entity and Relation Embeddings for Knowledge Graph Completion
Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, Xuan ZhuAAAI 2015
Knowledge Graph Embedding via Dynamic Mapping Matrix
Guoliang Ji, Shizhu He, Liheng Xu, Kang Liu, Jun ZhaACL 2015
Modeling Relation Paths for Representation Learning of Knowledge Bases
Yankai Lin, Zhiyuan Liu, Huanbo Luan, Maosong Sun, Siwei Rao, Song LiuEMNLP 2015
Embedding Entities and Relations for Learning and Inference in Knowledge Bases
Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, Li DengICLR 2015
Holographic Embeddings of Knowledge Graphs
Maximilian Nickel, Lorenzo Rosasco, Tomaso PoggioAAAI 2016
Complex Embeddings for Simple Link Prediction
Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, Guillaume BouchardICML 2016
Modeling Relational Data with Graph Convolutional Networks
Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, Max WellingarXiv 2017.03
Fast Linear Model for Knowledge Graph Embeddings
Armand Joulin, Edouard Grave, Piotr Bojanowski, Maximilian Nickel, Tomas MikolovarXiv 2017.10
Convolutional 2D Knowledge Graph Embeddings
Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, Sebastian RiedelAAAI 2018
Knowledge Graph Embedding With Iterative Guidance From Soft Rules
Shu Guo, Quan Wang, Lihong Wang, Bin Wang, Li GuoAAAI 2018
KBGAN: Adversarial Learning for Knowledge Graph Embeddings
Liwei Cai, William Yang WangNAACL 2018
Improving Knowledge Graph Embedding Using Simple Constraints
Boyang Ding, Quan Wang, Bin Wang, Li GuoACL 2018
SimplE Embedding for Link Prediction in Knowledge Graphs
Seyed Mehran Kazemi, David PooleNeurIPS 2018
A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network
Dai Quoc Nguyen, Tu Dinh Nguyen, Dat Quoc Nguyen, Dinh PhungNAACL 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 ChenWWW 2019
RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, Jian TangICLR 2019
Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs
Deepak Nathani, Jatin Chauhan, Charu Sharma, Manohar KaulACL 2019
Probabilistic Logic Neural Networks for Reasoning
Meng Qu, Jian TangarXiv 1906
3 Graph Neural Networks
Revisiting Semi-supervised Learning with Graph Embeddings
Zhilin Yang, William W. Cohen, Ruslan SalakhutdinovICML 2016
Semi-Supervised Classification with Graph Convolutional Networks
Thomas N. Kipf, Max WellingICLR 2017
Neural Message Passing for Quantum Chemistry
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, George E. DahlICML 2017
Hoang Nguyen, Tsuyoshi MurataIJCAI 2017
Learning Graph Representations with Embedding Propagation
Alberto Garcia-Duran, Mathias NiepertNIPS 2017
Inductive Representation Learning on Large Graphs
William L. Hamilton, Rex Ying, Jure LeskovecNIPS 2017
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, Yoshua BengioICLR 2018
FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling
Jie Chen, Tengfei Ma, Cao XiaoICLR 2018
Representation Learning on Graphs with Jumping Knowledge Networks
Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, Stefanie JegelkaICML 2018
Stochastic Training of Graph Convolutional Networks with Variance Reduction
Jianfei Chen, Jun Zhu, Le SongICML 2018
Large-Scale Learnable Graph Convolutional Networks
Hongyang Gao, Zhengyang Wang, Shuiwang JiKDD 2018
Adaptive Sampling Towards Fast Graph Representation Learning
Wenbing Huang, Tong Zhang, Yu Rong, Junzhou HuangNeurIPS 2018
Hierarchical Graph Representation Learning with Differentiable Pooling
Rex Ying, Jiaxuan You, Christopher Morris, Xiang Ren, William L. Hamilton, Jure LeskovecNeurIPS 2018
Bayesian Semi-supervised Learning with Graph Gaussian Processes
Yin Cheng Ng, Nicolò Colombo, Ricardo SilvaNeurIPS 2018
Pitfalls of Graph Neural Network Evaluation
Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, Stephan GünnemannarXiv 2018.11
Heterogeneous Graph Attention Network
Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Peng Cui, P. Yu, Yanfang YeWWW 2019
Bayesian graph convolutional neural networks for semi-supervised classification
Yingxue Zhang, Soumyasundar Pal, Mark Coates, Deniz ÜstebayAAAI 2019
How Powerful are Graph Neural Networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie JegelkaICLR 2019
LanczosNet: Multi-Scale Deep Graph Convolutional Networks
Renjie Liao, Zhizhen Zhao, Raquel Urtasun, Richard S. ZemelICLR 2019
Bingbing Xu, Huawei Shen, Qi Cao, Yunqi Qiu, Xueqi ChengICLR 2019
Supervised Community Detection with Line Graph Neural Networks
Zhengdao Chen, Xiang Li, Joan BrunaICLR 2019
Predict then Propagate: Graph Neural Networks meet Personalized PageRank
Johannes Klicpera, Aleksandar Bojchevski, Stephan GünnemannICLR 2019
Invariant and Equivariant Graph Networks
Haggai Maron, Heli Ben-Hamu, Nadav Shamir, Yaron LipmanICLR 2019
Zhang Xinyi, Lihui ChenICLR 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 GalstyanICML 2019
Hongyang Gao, Shuiwang JiICML 2019
Disentangled Graph Convolutional Networks
Jianxin Ma, Peng Cui, Kun Kuang, Xin Wang, Wenwu ZhuICML 2019
GMNN: Graph Markov Neural Networks
Meng Qu, Yoshua Bengio, Jian TangICML 2019
Simplifying Graph Convolutional Networks
Felix Wu, Tianyi Zhang, Amauri Holanda de Souza Jr., Christopher Fifty, Tao Yu, Kilian Q. WeinbergerICML 2019
Position-aware Graph Neural Networks
Jiaxuan You, Rex Ying, Jure LeskovecICML 2019
Junhyun Lee, Inyeop Lee, Jaewoo KangICML 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 TitovEMNLP 2017
Graph Convolutional Encoders for Syntax-aware Neural Machine Translation
Joost Bastings, Ivan Titov, Wilker Aziz, Diego Marcheggiani, Khalil Sima’anEMNLP 2017
Graph-based Neural Multi-Document Summarization
Michihiro Yasunaga, Rui Zhang, Kshitijh Meelu, Ayush Pareek, Krishnan Srinivasan, Dragomir RadevCoNLL 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. LeICLR 2018
A Structured Self-attentive Sentence Embedding
Zhouhan Lin, Minwei Feng, Cicero Nogueira dos Santos, Mo Yu, Bing Xiang, Bowen Zhou, Yoshua BengioICLR 2018
Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering
Daniil Sorokin, Iryna GurevychCOLING 2018
Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks
Diego Marcheggiani, Joost Bastings, Ivan TitovNAACL 2018
Linguistically-Informed Self-Attention for Semantic Role Labeling
Emma Strubell, Patrick Verga, Daniel Andor, David Weiss, Andrew McCallumEMNLP 2018
Graph Convolution over Pruned Dependency Trees Improves Relation Extraction
Yuhao Zhang, Peng Qi, Christopher D. ManningEMNLP 2018
A Graph-to-Sequence Model for AMR-to-Text Generation
Linfeng Song, Yue Zhang, Zhiguo Wang, Daniel GildeaACL 2018
Graph-to-Sequence Learning using Gated Graph Neural Networks
Daniel Beck, Gholamreza Haffari, Trevor CohnACL 2018
Graph Convolutional Networks for Text Classification
Liang Yao, Chengsheng Mao, Yuan LuoAAAI 2019
Differentiable Perturb-and-Parse: Semi-Supervised Parsing with a Structured Variational Autoencoder
Caio Corro, Ivan TitovICLR 2019.
Structured Neural Summarization
Patrick Fernandes, Miltiadis Allamanis, Marc BrockschmidICLR 2019
Multi-task Learning over Graph Structures
Pengfei Liu, Jie Fu, Yue Dong, Xipeng Qiu, Jackie Chi Kit CheungAAAI 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 WangNAACL 2019
Single Document Summarization as Tree Induction
Yang Liu, Ivan Titov, Mirella LapataNAACL 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 ChenNAACL 2019
Graph Neural Networks with Generated Parameters for Relation Extraction
Hao Zhu, Yankai Lin, Zhiyuan Liu, Jie Fu, Tat-seng Chua, Maosong SunACL 2019
Dynamically Fused Graph Network for Multi-hop Reasoning
Yunxuan Xiao, Yanru Qu, Lin Qiu, Hao Zhou, Lei Li, Weinan Zhang, Yong YuACL 2019
Chang Li, Dan GoldwasserACL 2019
Attention Guided Graph Convolutional Networks for Relation Extraction
Zhijiang Guo, Yan Zhang, Wei LuACL 2019
Shikhar Vashishth, Manik Bhandari, Prateek Yadav, Piyush Rai, Chiranjib Bhattacharyya, Partha TalukdarACL 2019
GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction
Tsu-Jui Fu, Peng-Hsuan Li, Wei-Yun MaACL 2019
Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs
Ming Tu, Guangtao Wang, Jing Huang, Yun Tang, Xiaodong He, Bowen ZhouACL 2019
Cognitive Graph for Multi-Hop Reading Comprehension at Scale
Ming Ding, Chang Zhou, Qibin Chen, Hongxia Yang, Jie TangACL 2019
Coherent Comment Generation for Chinese Articles with a Graph-to-Sequence Model
Wei Li, Jingjing Xu, Yancheng He, Shengli Yan, Yunfang Wu, Xu SunACL 2019
Matching Article Pairs with Graphical Decomposition and Convolutions
Bang Liu, Di Niu, Haojie Wei, Jinghong Lin, Yancheng He, Kunfeng Lai, Yu XuACL 2019
Embedding Imputation with Grounded Language Information
Ziyi Yang, Chenguang Zhu, Vin Sachidananda, Eric DarveACL 2019
Learning Graph Pooling and Hybrid Convolutional Operations for Text Representations
Hongyang Gao, Yongjun Chen, Shuiwang JiWWW 2019
4.2 Computer Vision
3D Graph Neural Networks for RGBD Semantic Segmentation
Xiaojuan Qi, Renjie Liao, Jiaya Jia, Sanja Fidler, Raquel UrtasunICCV 2017
Situation Recognition With Graph Neural Networks
Ruiyu Li, Makarand Tapaswi, Renjie Liao, Jiaya Jia, Raquel Urtasun, Sanja FidlerICCV 2017
Graph-Based Classification of Omnidirectional Images
Renata Khasanova, Pascal FrossardICCV 2017
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
Sijie Yan, Yuanjun Xiong, Dahua LinAAAI 2018
Image Generation from Scene Graphs
Justin Johnson, Agrim Gupta, Li Fei-FeiCVPR 2018
FoldingNet: Point Cloud Auto-Encoder via Deep Grid Deformation
Yaoqing Yang, Chen Feng, Yiru Shen, Dong TianCVPR 2018
PPFNet: Global Context Aware Local Features for Robust 3D Point Matching
Haowen Deng, Tolga Birdal, Slobodan IlicCVPR 2018
Iterative Visual Reasoning Beyond Convolutions
Xinlei Chen, Li-Jia Li, Li Fei-Fei, Abhinav GuptaCVPR 2018
Ilya Kostrikov, Zhongshi Jiang, Daniele Panozzo, Denis Zorin, Joan BrunaCVPR 2018
FeaStNet: Feature-Steered Graph Convolutions for 3D Shape Analysis
Nitika Verma, Edmond Boyer, Jakob VerbeekCVPR 2018
Learning to Act Properly: Predicting and Explaining Affordances From Images
Ching-Yao Chuang, Jiaman Li, Antonio Torralba, Sanja FidlerCVPR 2018
Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling
Yiru Shen, Chen Feng, Yaoqing Yang, Dong TianCVPR 2018
Deformable Shape Completion With Graph Convolutional Autoencoders
Or Litany, Alex Bronstein, Michael Bronstein, Ameesh MakadiaCVPR 2018
Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images
Nanyang Wang, Yinda Zhang, Zhuwen Li, Yanwei Fu, Wei Liu, Yu-Gang JiangECCV 2018
Learning Human-Object Interactions by Graph Parsing Neural Networks
Siyuan Qi, Wenguan Wang, Baoxiong Jia, Jianbing Shen, Song-Chun ZhuECCV 2018
Generating 3D Faces using Convolutional Mesh Autoencoders
Anurag Ranjan, Timo Bolkart, Soubhik Sanyal, Michael J. BlackECCV 2018
Learning SO(3) Equivariant Representations with Spherical CNNs
Carlos Esteves, Christine Allen-Blanchette, Ameesh Makadia, Kostas DaniilidisECCV 2018
Neural Graph Matching Networks for Fewshot 3D Action Recognition
Michelle Guo, Edward Chou, De-An Huang, Shuran Song, Serena Yeung, Li Fei-FeiECCV 2018
Multi-Kernel Diffusion CNNs for Graph-Based Learning on Point Clouds
Lasse Hansen, Jasper Diesel, Mattias P. HeinrichECCV 2018
Hierarchical Video Frame Sequence Representation with Deep Convolutional Graph Network
Feng Mao, Xiang Wu, Hui Xue, Rong ZhangECCV 2018
Graph R-CNN for Scene Graph Generation
Jianwei Yang, Jiasen Lu, Stefan Lee, Dhruv Batra, Devi ParikhECCV 2018
Exploring Visual Relationship for Image Captioning
Ting Yao, Yingwei Pan, Yehao Li, Tao MeiECCV 2018
Beyond Grids: Learning Graph Representations for Visual Recognition
Yin Li, Abhinav GuptaNeurIPS 2018
Learning Conditioned Graph Structures for Interpretable Visual Question Answering
Will Norcliffe-Brown, Efstathios Vafeias, Sarah ParisotNeurIPS 2018
LinkNet: Relational Embedding for Scene Graph
Sanghyun Woo, Dahun Kim, Donghyeon Cho, In So KweonNeurIPS 2018
Flexible Neural Representation for Physics Prediction
Damian Mrowca, Chengxu Zhuang, Elias Wang, Nick Haber, Li Fei-Fei, Joshua B. Tenenbaum, Daniel L. K. YaminsNeurIPS 2018
Learning Localized Generative Models for 3D Point Clouds via Graph Convolution
Diego Valsesia, Giulia Fracastoro, Enrico MagliICLR 2019
Graph-Based Global Reasoning Networks
Yunpeng Chen, Marcus Rohrbach, Zhicheng Yan, Shuicheng Yan, Jiashi Feng, Yannis KalantidisCVPR 2019
Deep Graph Laplacian Regularization for Robust Denoising of Real Images
Jin Zeng, Jiahao Pang, Wenxiu Sun, Gene CheungCVPR 2019
Learning Context Graph for Person Search
Yichao Yan, Qiang Zhang, Bingbing Ni, Wendong Zhang, Minghao Xu, Xiaokang YangCVPR 2019
Graphonomy: Universal Human Parsing via Graph Transfer Learning
Ke Gong, Yiming Gao, Xiaodan Liang, Xiaohui Shen, Meng Wang, Liang LinCVPR 2019
Masked Graph Attention Network for Person Re-Identification
Liqiang Bao, Bingpeng Ma, Hong Chang, Xilin ChenCVPR 2019
Learning to Cluster Faces on an Affinity Graph
Lei Yang, Xiaohang Zhan, Dapeng Chen, Junjie Yan, Chen Change Loy, Dahua LinCVPR 2019
Actional-Structural Graph Convolutional Networks for Skeleton-Based Action Recognition
Maosen Li, Siheng Chen, Xu Chen, Ya Zhang, Yanfeng Wang, Qi TianCVPR 2019
Adaptively Connected Neural Networks
Guangrun Wang, Keze Wang, Liang LinCVPR 2019
MeshCNN: A Network with an Edge
Rana Hanocka, Amir Hertz, Noa Fish, Raja Giryes, Shachar Fleishman, Daniel Cohen-OrSIGGRAPH 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 LeskovecKDD 2018PinSage
SocialGCN: An Efficient Graph Convolutional Network based Model for Social Recommendation
Le Wu, Peijie Sun, Richang Hong, Yanjie Fu, Xiting Wang, Meng WangAAAI 2018GCN, Social recommendation
Session-based Social Recommendation via Dynamic Graph Attention Networks
Weiping Song, Zhiping Xiao, Yifan Wang, Laurent Charlin, Ming Zhang, Jian TangWSDM 2019Social recommendation, session-based, GAT
Qitian Wu, Hengrui Zhang, Xiaofeng Gao, Peng He, Paul Weng, Han Gao, Guihai ChenWWW 2019Social recommendation, GAT
Graph Neural Networks for Social Recommendation
Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, Dawei YinWWW 2019Social recommendation, GNN
Session-based Recommendation with Graph Neural Networks
Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, Tieniu TanAAAI 2019Session-based recommendation, GNN
A Neural Influence Diffusion Model for Social Recommendation
Le Wu, Peijie Sun, Yanjie Fu, Richang Hong, Xiting Wang, Meng WangSIGIR 2019Social Recommendation, diffusion
Neural Graph Collaborative Filtering
Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, Tat-Seng ChuaSIGIR 2019Collaborative Filtering, GNN
Binarized Collaborative Filtering with Distilling Graph Convolutional Networks
Haoyu Wang, Defu Lian, Yong GeIJCAI 2019
4.4 Link Prediction
Link Prediction Based on Graph Neural Networks
Muhan Zhang, Yixin ChenNeurIPS 2018
Link Prediction via Subgraph Embedding-Based Convex Matrix Completion
Zhu Cao, Linlin Wang, Gerard de MeloAAAI 2018
Graph Convolutional Matrix Completion
Rianne van den Berg, Thomas N. Kipf, Max WellingKDD 2018 Workshop
4.5 Influence Prediction
DeepInf: Social Influence Prediction with Deep Learning
Jiezhong Qiu, Jian Tang, Hao Ma, Yuxiao Dong, Kuansan Wang, Jie TangKDD 2018
Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks
Namyong Park, Andrey Kan, Xin Luna Dong, Tong Zhao, Christos FaloutsosKDD 2019
4.6 Neural Architecture Search
Graph HyperNetworks for Neural Architecture Search
Chris Zhang, Mengye Ren, Raquel UrtasunICLR 2019
4.7 Reinforcement Learning
Action Schema Networks: Generalised Policies with Deep Learning
Sam Toyer, Felipe Trevizan, Sylvie Thiebaux, Lexing XieAAAI 2018
NerveNet: Learning Structured Policy with Graph Neural Networks
Tingwu Wang, Renjie Liao, Jimmy Ba, Sanja FidlerICLR 2018
Graph Networks as Learnable Physics Engines for Inference and Control
Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg, Josh Merel, Martin RiedmillerICML 2018
Learning Policy Representations in Multiagent Systems
Aditya Grover, Maruan Al-Shedivat, Jayesh K. Gupta, Yura Burda, Harrison EdwardsICML 2018
Relational recurrent neural networks
Adam Santoro, Ryan Faulkner, David Raposo, Jack Rae, Mike Chrzanowski,Théophane Weber, Daan Wierstra, Oriol Vinyals, Razvan Pascanu, Timothy LillicrapNeurIPS 2018
Transfer of Deep Reactive Policies for MDP Planning
Aniket Bajpai, Sankalp Garg, MausamNeurIPS 2018
Neural Graph Evolution: Towards Efficient Automatic Robot Design
Tingwu Wang, Yuhao Zhou, Sanja Fidler, Jimmy BaICLR 2019
4.8 Combinatorial Optimization
Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search
Zhuwen Li, Qifeng Chen, Vladlen KoltunNeurIPS 2018
Reinforcement Learning for Solving the Vehicle Routing Problem
Mohammadreza Nazari, Afshin Oroojlooy, Lawrence V. Snyder, Martin TakáčNeurIPS 2018
4.9 Adversarial Attack
Adversarial Attack on Graph Structured Data
Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, Le SongICML 2018
Adversarial Attacks on Neural Networks for Graph Data
Daniel Zügner, Amir Akbarnejad, Stephan GünnemannKDD 2018
Adversarial Attacks on Graph Neural Networks via Meta Learning
Daniel Zügner, Stephan GünnemannICLR 2019
4.10 Meta Learning
Learning Steady-States of Iterative Algorithms over Graphs
Hanjun Dai, Zornitsa Kozareva, Bo Dai, Alex Smola, Le SongICML 2018
4.11 Structure Learning
Few-Shot Learning with Graph Neural Networks
Victor Garcia, Joan BrunaICLR 2018
Neural Relational Inference for Interacting Systems
Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, Richard ZemelICML 2018
Brain Signal Classification via Learning Connectivity Structure
Soobeom Jang, Seong-Eun Moon, Jong-Seok LeearXiv 1905
A Flexible Generative Framework for Graph-based Semi-supervised Learning
Jiaqi Ma, Weijing Tang, Ji Zhu, Qiaozhu MeiarXiv 1905
Joint embedding of structure and features via graph convolutional networks
Sébastien Lerique, Jacob Levy Abitbol, Márton KarsaiarXiv 1905
Variational Spectral Graph Convolutional Networks
Louis Tiao, Pantelis Elinas, Harrison Nguyen, Edwin V. BonillaarXiv 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 YangICLR 2019
Graph Learning Network: A Structure Learning Algorithm
Darwin Saire Pilco, Adín Ramírez RiveraICML 2019 Workshop
Learning Discrete Structures for Graph Neural Networks
Luca Franceschi, Mathias Niepert, Massimiliano Pontil, Xiao HeICML 2019
Graphite: Iterative Generative Modeling of Graphs
Aditya Grover, Aaron Zweig, Stefano ErmonICML 2019
4.12 Bioinformatics and Chemistry
Protein Interface Prediction using Graph Convolutional Networks
Alex Fout, Jonathon Byrd, Basir Shariat, Asa Ben-HurNeurIPS 2017
Modeling Polypharmacy Side Effects with Graph Convolutional Networks
Marinka Zitnik, Monica Agrawal, Jure LeskovecBioinformatics 2018
Fangping Wan, Lixiang Hong, An Xiao, Tao Jiang, Jianyang ZengBioinformatics 2018
Mario Krenn, Florian Häse, AkshatKumar Nigam, Pascal Friederich, Alán Aspuru-GuzikarXiv 1905
Drug-Drug Adverse Effect Prediction with Graph Co-Attention
Andreea Deac, Yu-Hsiang Huang, Petar Veličković, Pietro Liò, Jian TangarXiv 1905
4.13 Theorem Proving
Premise Selection for Theorem Proving by Deep Graph Embedding
Mingzhe Wang, Yihe Tang, Jian Wang, Jia DengNeurIPS 2017
5 Graph Generation
GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models
Jiaxuan You, Rex Ying, Xiang Ren, William L. Hamilton, Jure LeskovecICML 2018
NetGAN: Generating Graphs via Random Walks
Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, Stephan GünnemannICML 2018
Junction Tree Variational Autoencoder for Molecular Graph Generation
Wengong Jin, Regina Barzilay, Tommi JaakkolaICML 2018
MolGAN: An implicit generative model for small molecular graphs
Nicola De Cao, Thomas KipfarXiv 1805
Generative Modeling for Protein Structures
Namrata Anand, Po-Ssu HuangNeurIPS 2018
Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders
Tengfei Ma, Jie Chen, Cao XiaoNeurIPS 2018
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
Jiaxuan You, Bowen Liu, Rex Ying, Vijay Pande, Jure LeskovecNeurIPS 2018
Constrained Graph Variational Autoencoders for Molecule Design
Qi Liu, Miltiadis Allamanis, Marc Brockschmidt, Alexander L. GauntNeurIPS 2018
Learning Multimodal Graph-to-Graph Translation for Molecule Optimization
Wengong Jin, Kevin Yang, Regina Barzilay, Tommi JaakkolaICLR 2019
DAG-GNN: DAG Structure Learning with Graph Neural Networks
Yue Yu, Jie Chen, Tian Gao, Mo YuICML 2019
Graph to Graph: a Topology Aware Approach for Graph Structures Learning and Generation
Mingming Sun, Ping LiAISTATS 2019
6 Graph Layout and High-dimensional Data Visualization
Laurens van der Maaten, Geoffrey HintonJMLR 2008
Visualizing non-metric similarities in multiple maps
Laurens van der Maaten, Geoffrey HintonML 2012
Visualizing Large-scale and High-dimensional Data
Jian Tang, Jingzhou Liu, Ming Zhang, Qiaozhu MeiWWW 2016
GraphTSNE: A Visualization Technique for Graph-Structured Data
Yao Yang Leow, Thomas Laurent, Xavier BressonICLR 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 TangWWW 2019
PyTorch-BigGraph: A Large-scale Graph Embedding System
Adam Lerer, Ledell Wu, Jiajun Shen, Timothee Lacroix, Luca Wehrstedt, Abhijit Bose, Alex PeysakhovichSysML 2019
AliGraph: A Comprehensive Graph Neural Network Platform
Rong Zhu, Kun Zhao, Hongxia Yang, Wei Lin, Chang Zhou, Baole Ai, Yong Li, Jingren ZhouVLDB 2019
DGL Team
Luca Costabello, Sumit Pai, Chan Le Van, Rory McGrath, Nicholas McCarthy, Pedro Tabacof
Alimama Engineering Platform Team, Alimama Search Advertising Algorithm Team
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent