This is a paper list about deep learning for graphs.
- 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
- 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
- Zhicheng He, Jie Liu, Na Li, Yalou Huang
KDD 2019
- 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
- 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
- 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
Applications of Graph Deep Learning =================================
- 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
- 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
- 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>_ | :authors:`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
- 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
- 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
- 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
- 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
- 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
- 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
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
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
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
- 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
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
Graph Algorithms ---------------
- Neural Execution of Graph Algorithms
- Petar Veličković, Rex Ying, Matilde Padovano, Raia Hadsell, Charles Blundell
ICLR 2020
- Premise Selection for Theorem Proving by Deep Graph Embedding
- Mingzhe Wang, Yihe Tang, Jian Wang, Jia Deng
NeurIPS 2017
- 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
- 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
- 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
- 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