/Papers

Papers

Must-read papers on GNN

GNN: graph neural network

Contributed by Handason, Li Yiming, Xie Siyue. Acknowledgement: https://github.com/thunlp/GNNPapers

Content

1. Survey
2. Models
 2.1 Autoencoder  2.2 GAN
 2.3 Graph Kernels  2.4 Heterogeneous
 2.5 Matrix Factorization  2.6 OTHERS
 2.7 Pooling  2.8 Random Walk
 2.9 Riemannian  2.10 Scalability
 2.11 Spatial  2.12 Spectral
 2.13 Structural Role/ Motifs/ Graphlets  2.14 Temporal
 2.15 Theory/ Analysis  2.16 Transfer Learning
 2.17 Unsupervised
3. Applications
 3.1 Adversarial Attack  3.2 Cascade
 3.3 Chemistry and Biology  3.4 Combinatorial
 3.5 Community Detection/ Clustering  3.6 Computer Vision
 3.7 Graph Classification  3.8 Knowledge Graph
 3.9 Link Prediction  3.10 Meta Learning
 3.11 Natural Language Processing  3.12 Node Classification
 3.13 Path Classification  3.14 Physics
 3.15 Program Representation  3.16 Recommendation Systems
 3.17 Reinforcement Learning  3.18 Time Prediction
 3.19 Traffic Network  3.20 Generation

Survey papers

  1. Graph Neural Networks: A Review of Methods and Applications. arxiv 2018. paper

    Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Maosong Sun.

  2. A Comprehensive Survey on Graph Neural Networks. arxiv 2019. paper

    Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, Philip S. Yu.

  3. Deep Learning on Graphs: A Survey. arxiv 2018. paper

    Ziwei Zhang, Peng Cui, Wenwu Zhu.

  4. Relational Inductive Biases, Deep Learning, and Graph Networks. arxiv 2018. paper

    Battaglia, Peter W and Hamrick, Jessica B and Bapst, Victor and Sanchez-Gonzalez, Alvaro and Zambaldi, Vinicius and Malinowski, Mateusz and Tacchetti, Andrea and Raposo, David and Santoro, Adam and Faulkner, Ryan and others.

  5. Geometric Deep Learning: Going beyond Euclidean data. IEEE SPM 2017. paper

    Bronstein, Michael M and Bruna, Joan and LeCun, Yann and Szlam, Arthur and Vandergheynst, Pierre.

  6. Computational Capabilities of Graph Neural Networks. IEEE TNN 2009. paper

    Scarselli, Franco and Gori, Marco and Tsoi, Ah Chung and Hagenbuchner, Markus and Monfardini, Gabriele.

  7. Neural Message Passing for Quantum Chemistry. ICML 2017. paper

    Gilmer, Justin and Schoenholz, Samuel S and Riley, Patrick F and Vinyals, Oriol and Dahl, George E.

  8. Non-local Neural Networks. CVPR 2018. paper

    Wang, Xiaolong and Girshick, Ross and Gupta, Abhinav and He, Kaiming.

  9. The Graph Neural Network Model. IEEE TNN 2009. paper

    Scarselli, Franco and Gori, Marco and Tsoi, Ah Chung and Hagenbuchner, Markus and Monfardini, Gabriele.

Models

Autoencoder

  1. A Degeneracy Framework for Scalable Graph Autoencoders. IJCAI 2019. paper

    Guillaume Salha, Romain Hennequin, Viet Anh Tran, Michalis Vazirgiannis.

  2. Learning Deep Representations for Graph Clustering. AAAI 2014. paper

    Fei Tian, Bin Gao, Qing Cui, Enhong Chen, T. M. Liu.

  3. Structural Deep Network Embedding. KDD2018. paper code

    Wang, Daixin and Cui, Peng and Zhu, Wenwu.

GAN

  1. Semi-supervised Learning on Graphs with Generative Adversarial Nets CIKM 2018. paper

    Ming Ding, Jie Tang, Jie Zhang

Graph Kernels

  1. Learning Convolutional Neural Networks for Graphs. ICML 2016. paper

    Mathias Niepert, Mohamed Ahmed, Konstantin Kutzkov.

  2. Temporal-Relational Classifiers for Prediction in Evolving Domains. paper

    Umang Sharan, Jennifer Neville.

Heterogeneous

  1. Edge Attention-based Multi-Relational Graph Convolutional Networks. paper

    Chao Shang, Qinqing Liu, Ko-Shin Chen, Jiangwen Sun, Jin Lu, Jinfeng Yi, Jinbo Bi.

  2. Graph Convolutional Networks on User Mobility Heterogeneous Graphs for Social Relationship Inference IJCAI2019. paper

    Yongji Wu, Defu Lian, Shuowei Jin, Enhong Chen

  3. Heterogeneous Network Embedding via Deep Architectures KDD2015. paper

    Shiyu Chang, Wei Han, Jiliang Tang,. Guo-Jun Qi, Charu C. Aggarwal, Thomas S. Huang

  4. metapath2vec: Scalable Representation Learning for Heterogeneous Networks KDD2017. paper

    Dong, Y., Chawla, N. V., & Swami, A.

  5. Modeling polypharmacy side effects with graph convolutional networks. ISMB 2018. paper

    Marinka Zitnik, Monica Agrawal, Jure Leskovec.

  6. Modeling Relational Data with Graph Convolutional Networks. ESWC 2018. paper

    Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, Max Welling.

  7. Multi-task Network Embedding. IEEE-DSAA 2017. paper

    Linchuan Xu, Xiaokai Wei, Jiannong Cao, Philip S. Yu

Matrix Factorization

  1. Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec. WSDM2018. paper

    Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, Jie Tang.

  2. Temporally Factorized Network Modeling for Evolutionary Network Analysis WSDM2017. paper

    Wenchao Yu, Charu C. Aggarwal, Wei Wang.

OTHERS

  1. Deep Convolutional Networks on Graph-Structured Data. arxiv 2015. paper

    Mikael Henaff, Joan Bruna, Yann LeCun.

  2. Uncovering and Predicting the Dynamic Process of Collective Attention with Survival Theory. Scientific Reports 2017. paper

    Bao P, Zhang X.

  3. Learning Continuous-Time Information Diffusion Model for Social Behavioral Data Analysis. ACML2009. paper

    Saito K, Kimura M, Ohara K, et al.

  4. Temporally Factorized Network Modeling for Evolutionary Network Analysis. WSDM2017. paper

    Yu W, Aggarwal C C, Wang W.

  5. Deep Dynamic Relational Classifiers: Exploiting Dynamic Neighborhoods in Complex Networks. WSDM 2017 workshop. paper

    Park H, Moore J, Neville J.

  6. Temporal-Relational Classifiers for Prediction in Evolving Domains. ICDM2008. paper

    Sharan U, Neville J.

  7. Representation Learning on Graphs: Methods and Applications. IEEE Data Engineering Bulletin 2017. paper

    Hamilton W L, Ying R, Leskovec J.

  8. HONE: Higher-Order Network Embeddings. arXiv2018. paper

    Rossi R A, Ahmed N K, Koh E, et al.

  9. Combining Neural Networks with Personalized PageRank for Classification on Graphs. ICLR2019. paper

    Klicpera J, Bojchevski A, Günnemann S.

  10. Relational Representation Learning for Dynamic (Knowledge) Graphs: A Survey. arXiv2019. paper

    Kazemi S M, Goel R, Jain K, et al.

Pooling

  1. Hierarchical Graph Representation Learning with Differentiable Pooling. NeurIPS 2018. paper

    Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, Will Hamilton, Jure Leskovec.

  2. Self-Attention Graph Pooling. ICML 2019. paper

    Junhyun Lee, Inyeop Lee, Jaewoo Kang.

  3. Graph U-Nets. ICML 2019. paper

    Hongyang Gao, Shuiwang Ji.

  4. Graph Convolutional Networks with EigenPooling. KDD 2019. paper

    Yao Ma, Suhang Wang, Charu C. Aggarwal, Jiliang Tang.

  5. Relational Pooling for Graph Representations. ICML 2019. paper

    Ryan L. Murphy, Balasubramaniam Srinivasan, Vinayak Rao, Bruno Ribeiro.

Random Walk

  1. DeepWalk: Online Learning of Social Representations. KDD2014. paper

    Perozzi B, Al-Rfou R, Skiena S.

Riemannian

  1. Multi-relational Poincaré Graph Embeddings. arXiv2019. paper

    Balažević I, Allen C, Hospedales T.

  2. Stochastic gradient descent on Riemannian manifolds. IEEE Transactions Automatic Control 2013. paper

    Bonnabel S.

  3. Geometric deep learning on graphs and manifolds using mixture model cnns. CVPR 2017. paper

    Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodolà, Jan Svoboda, Michael M. Bronstein.

  4. **Geodesic Convolutional Neural Networks on Riemannian Manifolds.**ICCV workshop 2015. paper

    Masci J, Boscaini D, Bronstein M, et al.

Scalability

  1. Stochastic Training of Graph Convolutional Networks with Variance Reduction. ICML 2018. paper

    Jianfei Chen, Jun Zhu, Le Song.

  2. FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling. ICLR 2018. paper

    Jie Chen, Tengfei Ma, Cao Xiao.

  3. Adaptive Sampling Towards Fast Graph Representation Learning. NeurIPS 2018. paper

    Wenbing Huang, Tong Zhang, Yu Rong, Junzhou Huang.

  4. Large-Scale Learnable Graph Convolutional Networks. KDD 2018. paper

    Hongyang Gao, Zhengyang Wang, Shuiwang Ji.

  5. Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks. KDD 2019. paper

    Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, Cho-Jui Hsieh.

  6. A Degeneracy Framework for Scalable Graph Autoencoders. IJCAI 2019. paper

    Guillaume Salha, Romain Hennequin, Viet Anh Tran, Michalis Vazirgiannis.

  7. DEEP GRAPH LIBRARY: TOWARDS EFFICIENT AND SCALABLE DEEP LEARNING ON GRAPHS. ICLR2019. paper

    Minjie Wang, Lingfan Yu, Da Zheng, et al.

  8. Motifs in Temporal Networks. WSDM 2017. paper

    Paranjape A, Benson A R, Leskovec J.

Spatial

  1. Dyn2Vec: Exploiting dynamic behaviour using difference networks-based node embeddings for classification. ICDATA 2018. paper

    Mitrovic S, De Weerdt J.

  2. Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs. ICML 2017. paper code

    Trivedi, Rakshit; Dai, Hanjun; Wang, Yichen; Song, Le.

  3. Heterogeneous Network Embedding via Deep Architectures. KDD 2015. paper

    Shiyu Chang, Wei Han, Jiliang Tang, Guo-Jun Qi, Charu C. Aggarwal, Thomas S. Huang.

  4. How Powerful are Graph Neural Networks? ICLR 2019. paper

    Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka.

  5. Deep Inductive Network Representation Learning. WWW 2018. paper

    Ryan A. Rossi, Rong Zhou, Nesreen K. Ahmed.

  6. GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs. UAI 2018. paper

    Jiani Zhang, Xingjian Shi, Junyuan Xie, Hao Ma, Irwin King, Dit-Yan Yeung.

  7. Streaming Graph Neural Networks. 2018. paper

    Yao Ma, Ziyi Guo, Zhaochun Ren, Eric Zhao, Jiliang Tang, Dawei Yin.

  8. Representation Learning on Graphs: Methods and Applications. IEEE Data Engineering Bulletin 2017. paper

    Hamilton W L, Ying R, Leskovec J.

  9. Modeling polypharmacy side effects with graph convolutional networks. ISMB 2018. paper

    Marinka Zitnik, Monica Agrawal, Jure Leskovec.

  10. Capturing Edge Attributes via Network Embedding. IEEE Transactions on Computational Social Systems 2018. paper

    Goyal P, Hosseinmardi H, Ferrara E, et al.

  11. Edge Attention-based Multi-Relational Graph Convolutional Networks. ICLR 2019. paper

    Chao Shang, Qinqing Liu, Ko-Shin Chen, Jiangwen Sun, Jin Lu, Jinfeng Yi, Jinbo Bi.

Spectral

  1. Semi-Supervised Classification with Graph Convolutional Networks. ICLR 2017. paper

    Thomas N. Kipf, Max Welling.

  2. Spectral Networks and Locally Connected Networks on Graphs. ICLR 2014. paper

    Joan Bruna, Wojciech Zaremba, Arthur Szlam, Yann LeCun.

  3. The Emerging Field of Signal Processing on Graphs: Extending High-Dimensional Data Analysis to Networks and Other Irregular Domains. IEEE Signal Processing Magazine 2013. paper

    Shuman D I, Narang S K, Frossard P, et al.

  4. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. NIPS 2016. paper

    Michaël Defferrard, Xavier Bresson, Pierre Vandergheynst.

  5. Structured Sequence Modeling with Graph Convolutional Recurrent Networks 2017. paper

    Youngjoo Seo, Michaël Defferrard, Pierre Vandergheynst, Xavier Bresson

  6. Learning Structural Node Embeddings Via Diffusion Wavelets. SIGKDD 2018. paper

    Claire Donnat, Marinka Zitnik, David Hallac, Jure Leskovec.

  7. Higher-order Spectral Clustering for Heterogeneous Graphs. paper

    Aldo G. Carranza, Ryan A. Rossi, Anup Rao, Eunyee Koh.

  8. Spectral Inference Networks: Unifying Deep and Spectral Learning. ICLR2019 paper

    Pfau D, Petersen S, Agarwal A, et al.

  9. LanczosNet: Multi-Scale Deep Graph Convolutional Networks. ICLR 2019. paper

    Renjie Liao, Zhizhen Zhao, Raquel Urtasun, Richard Zemel.

  10. Simplifying Graph Convolutional Networks. ICML 2019. paper

    Felix Wu, Tianyi Zhang, Amauri Holanda de Souza Jr., Christopher Fifty, Tao Yu, Kilian Q. Weinberger.

  11. Spectral–Spatial Graph Convolutional Networks for Semisupervised Hyperspectral Image Classification. IEEE Geoscience and Remote Sensing Letters 2019. paper

    Qin A, Shang Z, Tian J, et al.

  12. Lecture Notes on Expansion, Sparsest Cut, and Spectral Graph Theory. 2013. paper

    Trevisan L.

  13. Lecture Notes on Expansion, Sparsest Cut, and Spectral Graph Theory. 1996. paper

    Chung F R K.

  14. Lecture Notes on Expansion, Sparsest Cut, and Spectral Graph Theory. arXiv 2015. paper

    Rippel O, Snoek J, Adams R P.

  15. An O(log n/ log log n)-approximation Algorithm for the Asymmetric Traveling Salesman Problem. SODA 2010. paper

    Asadpour A, Goemans M X, Mądry A, et al.

  16. Graph Convolutional Networks using Heat Kernel for Semi-supervised Learning. IJCAI2019. paper

    Bingbing Xu , Huawei Shen , Qi Cao, et al.

Role

  1. Learning Convolutional Neural Networks for Graphs. ICML 2016. paper

    Mathias Niepert, Mohamed Ahmed, Konstantin Kutzkov.

  2. Higher-order Graph Convolutional Networks. arXiv 2018. paper

    John Boaz Lee, Ryan A. Rossi, Xiangnan Kong, et al.

  3. Deep Inductive Network Representation Learning. WWW 2018. paper

    Ryan A. Rossi, Rong Zhou, Nesreen K. Ahmed.

  4. Estimation of local subgraph counts. Big Data 2016. paper

    Ahmed N K, Willke T L, Rossi R A.

  5. struc2vec: Learning Node Representations from Structural Identity. KDD 2017. paper

    Leonardo F. R. Ribeiro, Pedro H. P. Savarese, Daniel R. Figueiredo.

  6. Learning Structural Node Embeddings Via Diffusion Wavelets. SIGKDD 2018. paper

    Claire Donnat, Marinka Zitnik, David Hallac, Jure Leskovec.

  7. Deep Inductive Graph Representation Learning. paper

    Ryan Anthony Rossi, Rong Zhou, Nesreen Ahmed.

  8. Higher-order Spectral Clustering for Heterogeneous Graphs. paper

    Aldo G. Carranza, Ryan A. Rossi, Anup Rao, Eunyee Koh.

  9. HONE: Higher-Order Network Embeddings. arXiv2018. paper

    Rossi R A, Ahmed N K, Koh E, et al.

  10. Role action embeddings: scalable representation of network positions. paper

    George Berry.

  11. Motifs in Temporal Networks. WSDM 2017. paper

    Paranjape A, Benson A R, Leskovec J.

  12. Graphlet Count Estimation via Convolutional Neural Networks. arXiv 2018. paper

    Liu X, Chen Y Z J, Lui J, et al.

Temporal

  1. Temporal Modeling of Information Diffusion Using Multi-actor Self-exciting Processes. WWW 2018. paper

    Bowen Zhang, Wing Cheong Lau.

  2. SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity. SIGKDD 2015. paper

    Zhao, Qingyuan, Erdogdu Murat A, He Hera Y, Rajaraman Anand, Leskovec Jure

  3. Modeling Information Propagation with Survival Theory. ICML 2013. paper

    *Manuel Gomez Rodriguez, Jure Leskovec, Bernhard Schoelkopf *

  4. Uncovering and Predicting the Dynamic Process of Collective Attention with Survival Theory. Scientific Reports 2017. paper

    Bao P, Zhang X.

  5. Topological Recurrent Neural Network for Diffusion Prediction. ICDM 2017. paper

    Jia Wang, Vincent W. Zheng, Zemin Liu, Kevin Chen-Chuan Chang

  6. Learning Continuous-Time Information Diffusion Model for Social Behavioral Data Analysis. ACML2009. paper

    Saito K, Kimura M, Ohara K, et al.

  7. Representation Learning over Dynamic Graphs. ICLR 2019. paper

    Rakshit Trivedi, Mehrdad Farajtabar, Prasenjeet Biswal, Hongyuan Zha.

  8. Temporally Factorized Network Modeling for Evolutionary Network Analysis. WSDM2017. paper

    Yu W, Aggarwal C C, Wang W.

  9. Link Prediction with Spatial and Temporal Consistency in Dynamic Networks. ICLR 2017. paper

    Wenchao Yu, Wei Cheng, Charu C Aggarwal, Haifeng Chen, Wei Wang.

  10. Recovering time-varying networks of dependencies in social and biological studies. PNAS 2009. paper

    Amr Ahme, Eric P. Xing.

  11. The Emerging Field of Signal Processing on Graphs: Extending High-Dimensional Data Analysis to Networks and Other Irregular Domains. IEEE Signal Processing Magazine 2013. paper

    Shuman D I, Narang S K, Frossard P, et al.

  12. Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. ICLR 2018. paper

    Yaguang Li, Rose Yu, Cyrus Shahabi, Yan Liu.

  13. Structured Sequence Modeling with Graph Convolutional Recurrent Networks 2017. paper

    Youngjoo Seo, Michaël Defferrard, Pierre Vandergheynst, Xavier Bresson

  14. Graph Clustering with Dynamic Embedding. KDD 2017. paper

    Carl Yang, Mengxiong Liu, Zongyi Wang, Liyuan Liu, Jiawei Han.

  15. Dyn2Vec: Exploiting dynamic behaviour using difference networks-based node embeddings for classification. ICDATA 2018. paper

    Mitrovic S, De Weerdt J.

  16. Deep Dynamic Relational Classifiers: Exploiting Dynamic Neighborhoods in Complex Networks. WSDM 2017 workshop. paper

    Park H, Moore J, Neville J.

  17. Temporal-Relational Classifiers for Prediction in Evolving Domains. ICDM2008. paper

    Sharan U, Neville J.

  18. Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs. ICML 2017. paper code

    Trivedi, Rakshit; Dai, Hanjun; Wang, Yichen; Song, Le.

  19. STWalk: Learning Trajectory Representations in Temporal Graphs CoDS-COMAD 2018. paper

    Supriya Pandhre, Himangi Mittal, Manish Gupta, Vineeth N Balasubramanian.

  20. Streaming Graph Neural Networks. 2018. paper

    Yao Ma, Ziyi Guo, Zhaochun Ren, Eric Zhao, Jiliang Tang, Dawei Yin.

  21. EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs. paper

    Aldo Pareja, Giacomo Domeniconi, Jie Chen, Tengfei Ma, Toyotaro Suzumura, Hiroki Kanezashi, Tim Kaler, Charles E. Leisersen.

  22. Scalable Graph Learning for Anti-Money Laundering: A First Look. NIPS 2018. paper

    Mark Weber, Jie Chen, Toyotaro Suzumura, Aldo Pareja, Tengfei Ma, Hiroki Kanezashi, Tim Kaler, Charles E. Leiserson, Tao B. Schardl.

  23. Graph WaveNet for Deep Spatial-Temporal Graph Modeling. IJCAI 2019. paper

    Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Chengqi Zhang.

  24. Relational Representation Learning for Dynamic (Knowledge) Graphs: A Survey. arXiv2019. paper

    Kazemi S M, Goel R, Jain K, et al.

  25. Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics. KDD2019. paper

    Weber M, Domeniconi G, Chen J, et al.

  26. Motifs in Temporal Networks. WSDM 2017. paper

    Paranjape A, Benson A R, Leskovec J.

  27. Predicting Path Failure In Time-Evolving Graphs. KDD 2019. paper

    Jia Li, Zhichao Han, Hong Cheng, Jiao Su, Pengyun Wang, Jianfeng Zhang, Lujia Pan.

  28. Node Embedding over Temporal Graphs. arXiv 2019. paper

    Uriel Singer, Ido Guy, Kira Radinsky.

Analysis

  1. SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity. SIGKDD 2015. paper

    Zhao, Qingyuan, Erdogdu Murat A, He Hera Y, Rajaraman Anand, Leskovec Jure

  2. Tracing Fake-News Footprints: Characterizing Social Media Messages by How They Propagate. WSDM 2018. paper

    Liang Wu, Huan Liu

  3. Semi-Supervised Classification with Graph Convolutional Networks. ICLR 2017. paper

    Thomas N. Kipf, Max Welling.

  4. Inductive Representation Learning on Large Graphs. NIPS 2017. paper

    William L. Hamilton, Rex Ying, Jure Leskovec.

  5. Spectral Networks and Locally Connected Networks on Graphs. ICLR 2014. paper

    Joan Bruna, Wojciech Zaremba, Arthur Szlam, Yann LeCun.

  6. The Emerging Field of Signal Processing on Graphs: Extending High-Dimensional Data Analysis to Networks and Other Irregular Domains. IEEE Signal Processing Magazine 2013. paper

    Shuman D I, Narang S K, Frossard P, et al.

  7. Semi-supervised Learning on Graphs with Generative Adversarial Nets CIKM 2018. paper

    Ming Ding, Jie Tang, Jie Zhang

  8. Machine learning techniques for anti-money laundering (AML) solutions in suspicious transaction detection: a review Knowledge and Information Systems 2018. paper

    Chen Z, Teoh E N, Nazir A, et al.

  9. Stochastic Training of Graph Convolutional Networks with Variance Reduction. ICML 2018. paper

    Jianfei Chen, Jun Zhu, Le Song.

  10. Scalable Graph Learning for Anti-Money Laundering: A First Look. NIPS 2018. paper

    Mark Weber, Jie Chen, Toyotaro Suzumura, Aldo Pareja, Tengfei Ma, Hiroki Kanezashi, Tim Kaler, Charles E. Leiserson, Tao B. Schardl.

  11. Pre-training Graph Neural Networks. paper

    Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, Jure Leskovec.

  12. Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec. WSDM 2018. paper

    Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, Jie Tang.

  13. A Comparison between Recursive Neural Networks and Graph Neural Networks. IJCNN 2006. paper

    Vincenzo Di Massa, Gabriele Monfardini, Lorenzo Sarti, Franco Scarselli, Marco Maggini, Marco Gori.

  14. Neural networks for relational learning: an experimental comparison. Machine Learning 2011. paper

    Werner Uwents, Gabriele Monfardini, Hendrik Blockeel, Marco Gori, Franco Scarselli.

  15. Mean-field theory of graph neural networks in graph partitioning. NeurIPS 2018. paper

    Tatsuro Kawamoto, Masashi Tsubaki, Tomoyuki Obuchi.

  16. Representation Learning on Graphs with Jumping Knowledge Networks. ICML 2018. paper

    Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, Stefanie Jegelka.

  17. Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning. AAAI 2018. paper

    Qimai Li, Zhichao Han, Xiao-Ming Wu.

  18. How Powerful are Graph Neural Networks? ICLR 2019. paper

    Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka.

  19. Stability and Generalization of Graph Convolutional Neural Networks. KDD 2019. paper

    Saurabh Verma, Zhi-Li Zhang.

  20. Simplifying Graph Convolutional Networks. ICML 2019. paper

    Felix Wu, Tianyi Zhang, Amauri Holanda de Souza Jr., Christopher Fifty, Tao Yu, Kilian Q. Weinberger.

  21. Explainability Methods for Graph Convolutional Neural Networks. CVPR 2019. paper

    Phillip E. Pope, Soheil Kolouri, Mohammad Rostami, Charles E. Martin, Heiko Hoffmann.

  22. Can GCNs Go as Deep as CNNs? ICCV 2019. paper

    Guohao Li, Matthias Müller, Ali Thabet, Bernard Ghanem.

  23. Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks. AAAI 2019. paper

    Christopher Morris, Martin Ritzert, Matthias Fey, William L. Hamilton, Jan Eric Lenssen, Gaurav Rattan, Martin Grohe.

Transfer Learning

  1. Cross-City Transfer Learning for Deep Spatio-Temporal Prediction. arXiv 2018. paper

    Wang L, Geng X, Ma X, et al.

  2. GLoMo: Unsupervisedly Learned Relational Graphs as Transferable Representations. arXiv 2018. paper

    Yang Z, Zhao J, Dhingra B, et al.

Unsupervised

  1. Learning Deep Representations for Graph Clustering. AAAI 2014. paper

    Fei Tian, Bin Gao, Qing Cui, Enhong Chen, Tie-Yan Liu.

  2. node2vec: Scalable Feature Learning for Networks. SIGKDD 2016. paper

    Aditya Grover, Jure Leskovec

  3. LINE: Large-scale Information Network Embedding. WWW 2015. paper

    *Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan,

  4. Inductive Representation Learning on Large Graphs. NIPS 2017. paper

    *William L. Hamilton, Rex Ying, Jure Leskovec.Qiaozhu Mei.

  5. Unsupervised Deep Embedding for Clustering Analysis. ICML 2016. paper

    Junyuan Xie, Ross Girshick, Ali Farhadi.

  6. Improved Deep Embedded Clustering with Local Structure Preservation. IJCAI 2017. paper

    Xifeng Guo, Long Gao, Xinwang Liu, Jianping Yin.

  7. STWalk: Learning Trajectory Representations in Temporal Graphs CoDS-COMAD 2018. paper

    Supriya Pandhre, Himangi Mittal, Manish Gupta, Vineeth N Balasubramanian.

  8. Heterogeneous Network Embedding via Deep Architectures. KDD 2015. paper

    Shiyu Chang, Wei Han, Jiliang Tang

  9. Deep Graph Infomax. ICLR 2019. paper

    Petar Veličković, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, R Devon Hjelm.

  10. Learning Structural Node Embeddings Via Diffusion Wavelets. SIGKDD 2018. paper

    Claire Donnat, Marinka Zitnik, David Hallac, Jure Leskovec.

  11. Role action embeddings: scalable representation of network positions. paper

    George Berry.

  12. Pre-training Graph Neural Networks. paper

    Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, Jure Leskovec.

  13. Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking. ICLR2018 paper

    Bojchevski A, Günnemann S.

  14. Variational Graph Auto-Encoders. NIPS 2016 paper

    Kipf T N, Welling M.

  15. Spatio-Temporal Deep Graph Infomax. ICLR 2019 paper

    Opolka F L, Solomon A, Cangea C, et al.

Knowledge Graph

  1. Modeling Relational Data with Graph Convolutional Networks. ESWC 2018. paper

    Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, Max Welling.

  2. How Powerful are Graph Neural Networks? ICLR 2019. paper

    Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka.

  3. Graph Convolutional Neural Networks for Web-Scale Recommender Systems. KDD 2018. paper

    Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, Jure Leskovec.

  4. Role action embeddings: scalable representation of network positions. paper

    George Berry.

  5. Stochastic Training of Graph Convolutional Networks with Variance Reduction. ICML 2018. paper

    Jianfei Chen, Jun Zhu, Le Song.

  6. Neural Message Passing for Quantum Chemistry. ICML 2017. paper

    Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, George E. Dahl.

  7. Scalable Graph Learning for Anti-Money Laundering: A First Look. NIPS 2018. paper

    Mark Weber, Jie Chen, Toyotaro Suzumura, Aldo Pareja, Tengfei Ma, Hiroki Kanezashi, Tim Kaler, Charles E. Leiserson, Tao B. Schardl.

  8. Pre-training Graph Neural Networks. paper

    Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, Jure Leskovec.

  9. Relational Representation Learning for Dynamic (Knowledge) Graphs: A Survey. arXiv2019. paper

    Kazemi S M, Goel R, Jain K, et al.

Applications

Cascade

  1. Temporal Modeling of Information Diffusion Using Multi-actor Self-exciting Processes. WWW 2018. paper

    Bowen Zhang, Wing Cheong Lau.

  2. SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity. SIGKDD 2015. paper

    Zhao, Qingyuan, Erdogdu Murat A, He Hera Y, Rajaraman Anand, Leskovec Jure

  3. DeepCas: An End-to-end Predictor of Information Cascades. WWW 2017. paper

    Cheng Li, Jiaqi Ma, Xiaoxiao Guo, Qiaozhu Mei

  4. Modeling Information Propagation with Survival Theory. ICML 2013. paper

    *Manuel Gomez Rodriguez, Jure Leskovec, Bernhard Schoelkopf *

  5. DeepHawkes: Bridging the Gap between Prediction and Understanding of Information Cascades. CIKM 2017. paper

    *Qi Cao, Huawei Shen, Keting Cen, Wentao Quyang, Xueqi Cheng"

  6. Tracing Fake-News Footprints: Characterizing Social Media Messages by How They Propagate. WSDM 2018. paper

    Liang Wu, Huan Liu

  7. Topological Recurrent Neural Network for Diffusion Prediction. ICDM 2017. paper

    Jia Wang, Vincent W. Zheng, Zemin Liu, Kevin Chen-Chuan Chang

Physics

  1. Discovering objects and their relations from entangled scene representations. ICLR Workshop 2017. paper

    David Raposo, Adam Santoro, David Barrett, Razvan Pascanu, Timothy Lillicrap, Peter Battaglia.

  2. A simple neural network module for relational reasoning. NIPS 2017. paper

    Adam Santoro, David Raposo, David G.T. Barrett, Mateusz Malinowski, Razvan Pascanu, Peter Battaglia, Timothy Lillicrap.

  3. Interaction Networks for Learning about Objects, Relations and Physics. NIPS 2016. paper

    Peter Battaglia, Razvan Pascanu, Matthew Lai, Danilo Rezende, Koray Kavukcuoglu.

  4. Visual Interaction Networks: Learning a Physics Simulator from Video. NIPS 2017. paper

    Nicholas Watters, Andrea Tacchetti, Théophane Weber, Razvan Pascanu, Peter Battaglia, Daniel Zoran.

  5. Graph networks as learnable physics engines for inference and control. ICML 2018. paper

    Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg, Josh Merel, Martin Riedmiller, Raia Hadsell, Peter Battaglia.

  6. Learning Multiagent Communication with Backpropagation. NIPS 2016. paper

    Sainbayar Sukhbaatar, Arthur Szlam, Rob Fergus.

  7. VAIN: Attentional Multi-agent Predictive Modeling. NIPS 2017 paper

    Yedid Hoshen.

  8. Neural Relational Inference for Interacting Systems. ICML 2018. paper

    Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, Richard Zemel.

  9. Graph Element Networks: adaptive, structured computation and memory. ICML 2019. paper

    Ferran Alet, Adarsh K. Jeewajee, Maria Bauza, Alberto Rodriguez, Tomas Lozano-Perez, Leslie Pack Kaelbling.

Chemistry and Biology

  1. Convolutional networks on graphs for learning molecular fingerprints. NIPS 2015. paper

    David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael Gómez-Bombarelli, Timothy Hirzel, Alán Aspuru-Guzik, Ryan P. Adams.

  2. Molecular Graph Convolutions: Moving Beyond Fingerprints. Journal of computer-aided molecular design 2016. paper

    Steven Kearnes, Kevin McCloskey, Marc Berndl, Vijay Pande, Patrick Riley.

  3. Protein Interface Prediction using Graph Convolutional Networks. NIPS 2017. paper

    Alex Fout, Jonathon Byrd, Basir Shariat, Asa Ben-Hur.

  4. Hybrid Approach of Relation Network and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classification. IJCAI 2018. paper

    Sungmin Rhee, Seokjun Seo, Sun Kim.

  5. Modeling polypharmacy side effects with graph convolutional networks. ISMB 2018. paper

    Marinka Zitnik, Monica Agrawal, Jure Leskovec.

  6. MR-GNN: Multi-Resolution and Dual Graph Neural Network for Predicting Structured Entity Interactions. IJCAI 2019. paper

    Nuo Xu, Pinghui Wang, Long Chen, Jing Tao, Junzhou Zhao.

  7. Pre-training of Graph Augmented Transformers for Medication Recommendation. IJCAI 2019. paper

    Junyuan Shang, Tengfei Ma, Cao Xiao, Jimeng Sun.

  8. GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination. AAAI 2019. paper

    Junyuan Shang, Cao Xiao, Tengfei Ma, Hongyan Li, Jimeng Sun.

  9. AffinityNet: semi-supervised few-shot learning for disease type prediction. AAAI 2019. paper

    Tianle Ma, Aidong Zhang.

  10. Graph Transformation Policy Network for Chemical Reaction Prediction. KDD 2019. paper

    Kien Do, Truyen Tran, Svetha Venkatesh.

  11. Functional Transparency for Structured Data: a Game-Theoretic Approach. ICML 2019. paper

    Guang-He Lee, Wengong Jin, David Alvarez-Melis, Tommi S. Jaakkola.

  12. Learning Multimodal Graph-to-Graph Translation for Molecular Optimization. ICLR 2019. paper

    Wengong Jin, Kevin Yang, Regina Barzilay, Tommi Jaakkola.

  13. A Generative Model For Electron Paths. ICLR 2019. paper

    John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato.

  14. Recovering time-varying networks of dependencies in social and biological studies. PNAS 2009. paper

    Amr Ahme, Eric P. Xing.

  15. Pairwise alignment of protein interaction networks. paper

    Koyutürk M1, Kim Y, Topkara U, Subramaniam S, Szpankowski W, Grama A.

  16. Neural Message Passing for Quantum Chemistry. ICML 2017. paper

    Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, George E. Dahl.

  17. Pre-training Graph Neural Networks. paper

    Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, Jure Leskovec.

Knowledge Graph

  1. Modeling Relational Data with Graph Convolutional Networks. ESWC 2018. paper

    Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, Max Welling.

  2. Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks. EMNLP 2018. paper

    Zhichun Wang, Qingsong Lv, Xiaohan Lan, Yu Zhang.

  3. Representation learning for visual-relational knowledge graphs. arxiv 2017. paper

    Daniel Oñoro-Rubio, Mathias Niepert, Alberto García-Durán, Roberto González, Roberto J. López-Sastre.

  4. End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completion. AAAI 2019. paper

    Chao Shang, Yun Tang, Jing Huang, Jinbo Bi, Xiaodong He, Bowen Zhou.

  5. Knowledge Transfer for Out-of-Knowledge-Base Entities : A Graph Neural Network Approach. IJCAI 2017. paper

    Takuo Hamaguchi, Hidekazu Oiwa, Masashi Shimbo, Yuji Matsumoto.

  6. Logic Attention Based Neighborhood Aggregation for Inductive Knowledge Graph Embedding. AAAI 2019. paper

    Peifeng Wang, Jialong Han, Chenliang Li, Rong Pan.

  7. Dynamic Graph Generation Network: Generating Relational Knowledge from Diagrams. CVPR 2018. paper

    Haoyu Wang, Defu Lian, Yong Ge.

  8. Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks. KDD 2019. paper

    Namyong Park, Andrey Kan, Xin Luna Dong, Tong Zhao, Christos Faloutsos.

  9. OAG: Toward Linking Large-scale Heterogeneous Entity Graphs. KDD 2019. paper

    Fanjin Zhang, Xiao Liu, Jie Tang, Yuxiao Dong, Peiran Yao, Jie Zhang, Xiaotao Gu, Yan Wang, Bin Shao, Rui Li, Kuansan Wang.

  10. Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs. ACL 2019. paper

    Deepak Nathani, Jatin Chauhan, Charu Sharma, Manohar Kaul.

  11. Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Network. ACL 2019. paper

    Kun Xu, Mo Yu, Yansong Feng, Yan Song, Zhiguo Wang, Dong Yu.

Recommendation Systems

  1. Graph Convolutional Neural Networks for Web-Scale Recommender Systems. KDD 2018. paper

    Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, Jure Leskovec.

  2. Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks. NIPS 2017. paper

    Federico Monti, Michael M. Bronstein, Xavier Bresson.

  3. Graph Convolutional Matrix Completion. 2017. paper

    Rianne van den Berg, Thomas N. Kipf, Max Welling.

  4. STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems. IJCAI 2019. paper

    Jiani Zhang, Xingjian Shi, Shenglin Zhao, Irwin King.

  5. Binarized Collaborative Filtering with Distilling Graph Convolutional Networks. IJCAI 2019. paper

    Haoyu Wang, Defu Lian, Yong Ge.

  6. Graph Contextualized Self-Attention Network for Session-based Recommendation. IJCAI 2019. paper

    Chengfeng Xu, Pengpeng Zhao, Yanchi Liu, Victor S. Sheng, Jiajie Xu, Fuzhen Zhuang, Junhua Fang, Xiaofang Zhou.

  7. Session-based Recommendation with Graph Neural Networks. AAAI 2019. paper

    Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, Tieniu Tan.

  8. Geometric Hawkes Processes with Graph Convolutional Recurrent Neural Networks. AAAI 2019. paper

    Jin Shang, Mingxuan Sun.

  9. Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems. KDD 2019. paper

    Hongwei Wang, Fuzheng Zhang, Mengdi Zhang, Jure Leskovec, Miao Zhao, Wenjie Li, Zhongyuan Wang.

  10. Exact-K Recommendation via Maximal Clique Optimization. KDD 2019. paper

    Yu Gong, Yu Zhu, Lu Duan, Qingwen Liu, Ziyu Guan, Fei Sun, Wenwu Ou, Kenny Q. Zhu.

  11. KGAT: Knowledge Graph Attention Network for Recommendation. KDD 2019. paper

    Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, Tat-Seng Chua.

  12. Knowledge Graph Convolutional Networks for Recommender Systems. WWW 2019. paper

    Hongwei Wang, Miao Zhao, Xing Xie, Wenjie Li, Minyi Guo.

  13. Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems. WWW 2019. paper

    Qitian Wu, Hengrui Zhang, Xiaofeng Gao, Peng He, Paul Weng, Han Gao, Guihai Chen.

  14. Graph Neural Networks for Social Recommendation. WWW 2019. paper

    Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, Dawei Yin.

Computer Vision

  1. Structured Sequence Modeling with Graph Convolutional Recurrent Networks 2017. paper

    Youngjoo Seo, Michaël Defferrard, Pierre Vandergheynst, Xavier Bresson

  2. Learning Convolutional Neural Networks for Graphs. ICML 2016. paper

    Mathias Niepert, Mohamed Ahmed, Konstantin Kutzkov.

  3. Spectral Networks and Locally Connected Networks on Graphs. ICLR 2014. paper

    Joan Bruna, Wojciech Zaremba, Arthur Szlam, Yann LeCun.

  4. Graph Neural Networks for Object Localization. ECAI 2006. paper

    Gabriele Monfardini, Vincenzo Di Massa, Franco Scarselli, Marco Gori.

  5. Learning Human-Object Interactions by Graph Parsing Neural Networks. ECCV 2018. paper

    Siyuan Qi, Wenguan Wang, Baoxiong Jia, Jianbing Shen, Song-Chun Zhu.

  6. Learning Conditioned Graph Structures for Interpretable Visual Question Answering. NeurIPS 2018. paper

    Will Norcliffe-Brown, Efstathios Vafeias, Sarah Parisot.

  7. Symbolic Graph Reasoning Meets Convolutions. NeurIPS 2018. paper

    Xiaodan Liang, Zhiting Hu, Hao Zhang, Liang Lin, Eric P. Xing.

  8. Out of the Box: Reasoning with Graph Convolution Nets for Factual Visual Question Answering. NeurIPS 2018. paper

    Medhini Narasimhan, Svetlana Lazebnik, Alexander Schwing.

  9. Structural-RNN: Deep Learning on Spatio-Temporal Graphs. CVPR 2016. paper

    Ashesh Jain, Amir R. Zamir, Silvio Savarese, Ashutosh Saxena.

  10. Relation Networks for Object Detection. CVPR 2018. paper

    Han Hu, Jiayuan Gu, Zheng Zhang, Jifeng Dai, Yichen Wei.

  11. Learning Region features for Object Detection. ECCV 2018. paper

    Jiayuan Gu, Han Hu, Liwei Wang, Yichen Wei, Jifeng Dai.

  12. The More You Know: Using Knowledge Graphs for Image Classification. CVPR 2017. paper

    Kenneth Marino, Ruslan Salakhutdinov, Abhinav Gupta.

  13. Understanding Kin Relationships in a Photo. TMM 2012. paper

    Siyu Xia, Ming Shao, Jiebo Luo, Yun Fu.

  14. Graph-Structured Representations for Visual Question Answering. CVPR 2017. paper

    Damien Teney, Lingqiao Liu, Anton van den Hengel.

  15. Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition. AAAI 2018. paper

    Sijie Yan, Yuanjun Xiong, Dahua Lin.

  16. Dynamic Graph CNN for Learning on Point Clouds. CVPR 2018. paper

    Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E. Sarma, Michael M. Bronstein, Justin M. Solomon.

  17. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. CVPR 2018. paper

    Charles R. Qi, Hao Su, Kaichun Mo, Leonidas J. Guibas.

  18. 3D Graph Neural Networks for RGBD Semantic Segmentation. CVPR 2017. paper

    Xiaojuan Qi, Renjie Liao, Jiaya Jia, Sanja Fidler, Raquel Urtasun.

  19. Iterative Visual Reasoning Beyond Convolutions. CVPR 2018. paper

    Xinlei Chen, Li-Jia Li, Li Fei-Fei, Abhinav Gupta.

  20. Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs. CVPR 2017. paper

    Martin Simonovsky, Nikos Komodakis.

  21. Situation Recognition with Graph Neural Networks. ICCV 2017. paper

    Ruiyu Li, Makarand Tapaswi, Renjie Liao, Jiaya Jia, Raquel Urtasun, Sanja Fidler.

  22. Deep Reasoning with Knowledge Graph for Social Relationship Understanding. IJCAI 2018. paper

    Zhouxia Wang, Tianshui Chen, Jimmy Ren, Weihao Yu, Hui Cheng, Liang Lin.

  23. I Know the Relationships: Zero-Shot Action Recognition via Two-Stream Graph Convolutional Networks and Knowledge Graphs. AAAI 2019. paper

    Junyu Gao, Tianzhu Zhang, Changsheng Xu.

more

  1. Graph CNNs with Motif and Variable Temporal Block for Skeleton-based Action Recognition. AAAI 2019. paper

    Yu-Hui Wen, Lin Gao, Hongbo Fu, Fang-Lue Zhang, Shihong Xia.

  2. Multi-Label Image Recognition with Graph Convolutional Networks. CVPR 2019. paper

    Zhao-Min Chen, Xiu-Shen Wei, Peng Wang, Yanwen Guo.

  3. Spatial-Aware Graph Relation Network for Large-Scale Object Detection. CVPR 2019. paper

    Hang Xu, Chenhan Jiang, Xiaodan Liang, Zhenguo Li.

  4. GCAN: Graph Convolutional Adversarial Network for Unsupervised Domain Adaptation. CVPR 2019. paper

    Xinhong Ma, Tianzhu Zhang, Changsheng Xu.

  5. Mind Your Neighbours: Image Annotation With Metadata Neighbourhood Graph Co-Attention Networks. CVPR 2019. paper

    Junjie Zhang, Qi Wu, Jian Zhang, Chunhua Shen, Jianfeng Lu.

  6. Attentive Relational Networks for Mapping Images to Scene Graphs. CVPR 2019. paper

    Mengshi Qi, Weijian Li, Zhengyuan Yang, Yunhong Wang, Jiebo Luo.

  7. Knowledge-Embedded Routing Network for Scene Graph Generation. CVPR 2019. paper

    Tianshui Chen, Weihao Yu, Riquan Chen, Liang Lin.

  8. Auto-Encoding Scene Graphs for Image Captioning. CVPR 2019. paper

    Xu Yang, Kaihua Tang, Hanwang Zhang, Jianfei Cai.

  9. Learning to Cluster Faces on an Affinity Graph. CVPR 2019. paper

    Lei Yang, Xiaohang Zhan, Dapeng Chen, Junjie Yan, Chen Change Loy, Dahua Lin.

  10. Learning a Deep ConvNet for Multi-label Classification with Partial Labels. CVPR 2019. paper

    Thibaut Durand, Nazanin Mehrasa, Greg Mori.

  11. Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detection. CVPR 2019. paper

    Jia-Xing Zhong, Nannan Li, Weijie Kong, Shan Liu, Thomas H. Li, Ge Li.

  12. Learning Actor Relation Graphs for Group Activity Recognition. CVPR 2019. paper

    Jianchao Wu, Limin Wang, Li Wang, Jie Guo, Gangshan Wu.

  13. ABC: A Big CAD Model Dataset For Geometric Deep Learning. CVPR 2019. paper

    Sebastian Koch, Albert Matveev, Zhongshi Jiang, Francis Williams, Alexey Artemov, Evgeny Burnaev, Marc Alexa, Denis Zorin, Daniele Panozzo.

  14. Neighbourhood Watch: Referring Expression Comprehension via Language-guided Graph Attention Networks. CVPR 2019. paper

    Peng Wang, Qi Wu, Jiewei Cao, Chunhua Shen, Lianli Gao, Anton van den Hengel.

  15. Graph-Based Global Reasoning Networks. CVPR 2019. paper

    Yunpeng Chen, Marcus Rohrbach, Zhicheng Yan, Shuicheng Yan, Jiashi Feng, Yannis Kalantidis.

  16. Linkage Based Face Clustering via Graph Convolution Network. CVPR 2019. paper

    Zhongdao Wang, Liang Zheng, Yali Li, Shengjin Wang.

  17. Fast Interactive Object Annotation with Curve-GCN. CVPR 2019. paper

    Huan Ling, Jun Gao, Amlan Kar, Wenzheng Chen, Sanja Fidler.

  18. Semantic Graph Convolutional Networks for 3D Human Pose Regression. CVPR 2019. paper

    Long Zhao, Xi Peng, Yu Tian, Mubbasir Kapadia, Dimitris N. Metaxas.

  19. Neural Task Graphs: Generalizing to Unseen Tasks from a Single Video Demonstration. CVPR 2019. paper

    De-An Huang, Suraj Nair, Danfei Xu, Yuke Zhu, Animesh Garg, Li Fei-Fei, Silvio Savarese, Juan Carlos Niebles.

  20. Graphonomy: Universal Human Parsing via Graph Transfer Learning. CVPR 2019. paper

    Ke Gong, Yiming Gao, Xiaodan Liang, Xiaohui Shen, Meng Wang, Liang Lin.

  21. Learning Context Graph for Person Search. CVPR 2019. paper

    Yichao Yan, Qiang Zhang, Bingbing Ni, Wendong Zhang, Minghao Xu, Xiaokang Yang.

  22. Occlusion-Net: 2D/3D Occluded Keypoint Localization Using Graph Networks. CVPR 2019. paper

    N. Dinesh Reddy, Minh Vo, Srinivasa G. Narasimhan.

  23. MAN: Moment Alignment Network for Natural Language Moment Retrieval via Iterative Graph Adjustment. CVPR 2019. paper

    Da Zhang, Xiyang Dai, Xin Wang, Yuan-Fang Wang, Larry S. Davis.

  24. Context-Aware Visual Compatibility Prediction. CVPR 2019. paper

    Guillem Cucurull, Perouz Taslakian, David Vazquez.

  25. Graph Attention Convolution for Point Cloud Semantic Segmentation. CVPR 2019. paper

    Lei Wang, Yuchun Huang, Yaolin Hou, Shenman Zhang, Jie Shan.

  26. An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition. CVPR 2019. paper

    Chenyang Si, Wentao Chen, Wei Wang, Liang Wang, Tieniu Tan.

  27. Actional-Structural Graph Convolutional Networks for Skeleton-based Action Recognition. CVPR 2019. paper

    Maosen Li, Siheng Chen, Xu Chen, Ya Zhang, Yanfeng Wang, Qi Tian.

  28. Graph Convolutional Tracking. CVPR 2019. paper

    Junyu Gao, Tianzhu Zhang, Changsheng Xu.

  29. Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition. CVPR 2019. paper

    Lei Shi, Yifan Zhang, Jian Cheng, Hanqing Lu.

  30. Skeleton-Based Action Recognition With Directed Graph Neural Networks. CVPR 2019. paper

    Lei Shi, Yifan Zhang, Jian Cheng, Hanqing Lu.

  31. Neural Module Networks. CVPR 2016. paper

    Jacob Andreas, Marcus Rohrbach, Trevor Darrell, Dan Klein.

  32. LatentGNN: Learning Efficient Non-local Relations for Visual Recognition. ICML 2019. paper

    Songyang Zhang, Shipeng Yan, Xuming He.

  33. Graph Convolutional Gaussian Processes. ICML 2019. paper

    Ian Walker, Ben Glocker.

  34. GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects. ICML 2019. paper

    Edward J. Smith, Scott Fujimoto, Adriana Romero, David Meger.

Natural Language Processing

  1. Structured Sequence Modeling with Graph Convolutional Recurrent Networks 2017. paper

    Youngjoo Seo, Michaël Defferrard, Pierre Vandergheynst, Xavier Bresson

  2. Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks. 2018. paper

    Xu, Kun; Wu, Lingfei; Wang, Zhiguo; Feng, Yansong; Witbrock, Michael; Sheinin, Vadim.

  3. Conversation Modeling on Reddit using a Graph-Structured LSTM. TACL 2018. paper

    Vicky Zayats, Mari Ostendorf.

  4. Learning Graphical State Transitions. ICLR 2017. paper

    Daniel D. Johnson.

  5. Multiple Events Extraction via Attention-based Graph Information Aggregation. EMNLP 2018. paper

    Xiao Liu, Zhunchen Luo, Heyan Huang.

  6. Recurrent Relational Networks. NeurIPS 2018. paper

    Rasmus Palm, Ulrich Paquet, Ole Winther.

  7. Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks. ACL 2015. paper

    Kai Sheng Tai, Richard Socher, Christopher D. Manning.

  8. Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling. EMNLP 2017. paper

    Diego Marcheggiani, Ivan Titov.

  9. Graph Convolutional Networks with Argument-Aware Pooling for Event Detection. AAAI 2018. paper

    Thien Huu Nguyen, Ralph Grishman.

  10. Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks. NAACL 2018. paper

    Diego Marcheggiani, Joost Bastings, Ivan Titov.

  11. Exploring Graph-structured Passage Representation for Multi-hop Reading Comprehension with Graph Neural Networks. 2018. paper

    Linfeng Song, Zhiguo Wang, Mo Yu, Yue Zhang, Radu Florian, Daniel Gildea.

  12. Graph Convolution over Pruned Dependency Trees Improves Relation Extraction. EMNLP 2018. paper

    Yuhao Zhang, Peng Qi, Christopher D. Manning.

  13. N-ary relation extraction using graph state LSTM. EMNLP 18. paper

    Linfeng Song, Yue Zhang, Zhiguo Wang, Daniel Gildea.

  14. A Graph-to-Sequence Model for AMR-to-Text Generation. ACL 2018. paper

    Linfeng Song, Yue Zhang, Zhiguo Wang, Daniel Gildea.

  15. Graph-to-Sequence Learning using Gated Graph Neural Networks. ACL 2018. paper

    Daniel Beck, Gholamreza Haffari, Trevor Cohn.

  16. Cross-Sentence N-ary Relation Extraction with Graph LSTMs. TACL. paper

    Nanyun Peng, Hoifung Poon, Chris Quirk, Kristina Toutanova, Wen-tau Yih.

  17. Sentence-State LSTM for Text Representation. ACL 2018. paper

    Yue Zhang, Qi Liu, Linfeng Song.

  18. End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures. ACL 2016. paper

    Makoto Miwa, Mohit Bansal.

  19. Graph Convolutional Encoders for Syntax-aware Neural Machine Translation. EMNLP 2017. paper

    Joost Bastings, Ivan Titov, Wilker Aziz, Diego Marcheggiani, Khalil Sima'an.

  20. Semi-supervised User Geolocation via Graph Convolutional Networks. ACL 2018. paper

    Afshin Rahimi, Trevor Cohn, Timothy Baldwin.

  21. Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering. COLING 2018. paper

    Daniil Sorokin, Iryna Gurevych.

  22. Graph Convolutional Networks for Text Classification. AAAI 2019. paper

    Liang Yao, Chengsheng Mao, Yuan Luo.

more

  1. Constructing Narrative Event Evolutionary Graph for Script Event Prediction. IJCAI 2018. paper

    Zhongyang Li, Xiao Ding, Ting Liu.

  2. Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks. ACL 2019. paper

    Shikhar Vashishth, Manik Bhandari, Prateek Yadav, Piyush Rai, Chiranjib Bhattacharyya, Partha Talukdar

  3. PaperRobot: Incremental Draft Generation of Scientific Ideas. ACL 2019. paper

    Qingyun Wang, Lifu Huang, Zhiying Jiang, Kevin Knight, Heng Ji, Mohit Bansal, Yi Luan.

  4. Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network. ACL 2019. paper

    Sunil Kumar Sahu, Fenia Christopoulou, Makoto Miwa, Sophia Ananiadou.

  5. Textbook Question Answering with Multi-modal Context Graph Understanding and Self-supervised Open-set Comprehension. ACL 2019. paper

    Daesik Kim, Seonhoon Kim, Nojun Kwak.

  6. Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs. ACL 2019. paper

    Ming Tu, Guangtao Wang, Jing Huang, Yun Tang, Xiaodong He, Bowen Zhou.

  7. Dynamically Fused Graph Network for Multi-hop Reasoning. ACL 2019. paper

    Yunxuan Xiao, Yanru Qu, Lin Qiu, Hao Zhou, Lei Li, Weinan Zhang, Yong Yu.

  8. Cognitive Graph for Multi-Hop Reading Comprehension at Scale. ACL 2019. paper

    Ming Ding, Chang Zhou, Qibin Chen, Hongxia Yang, Jie Tang.

  9. Joint Type Inference on Entities and Relations via Graph Convolutional Networks. ACL 2019. paper

    Changzhi Sun, Yeyun Gong, Yuanbin Wu, Ming Gong, Daxing Jiang, Man Lan, Shiliang Sun1, Nan Duan.

  10. Attention Guided Graph Convolutional Networks for Relation Extraction. ACL 2019. paper

    Zhijiang Guo, Yan Zhang, Wei Lu.

  11. GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction. ACL 2019. paper

    Tsu-Jui Fu, Peng-Hsuan Li, Wei-Yun Ma.

  12. Graph Neural Networks with Generated Parameters for Relation Extraction. ACL 2019. paper

    Hao Zhu, Yankai Lin, Zhiyuan Liu, Jie Fu, Tat-seng Chua, Maosong Sun.

  13. Generating Logical Forms from Graph Representations of Text and Entities. ACL 2019. paper

    Peter Shaw, Philip Massey, Angelica Chen, Francesco Piccinno, Yasemin Altun.

  14. Matching Article Pairs with Graphical Decomposition and Convolutions. ACL 2019. paper

    Bang Liu, Di Niu, Haojie Wei, Jinghong Lin, Yancheng He, Kunfeng Lai, Yu Xu.

  15. Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing. ACL 2019. paper

    Ben Bogin, Matt Gardner, Jonathan Berant.

  16. Coherent Comment Generation for Chinese Articles with a Graph-to-Sequence Model. ACL 2019. paper

    Wei Li, Jingjing Xu, Yancheng He, Shengli Yan, Yunfang Wu, Xu sun.

  17. GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verification. ACL 2019. paper

    Jie Zhou, Xu Han, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, Maosong Sun.

  18. Look Again at the Syntax: Relational Graph Convolutional Network for Gendered Ambiguous Pronoun Resolution. ACL 2019. paper

    Yinchuan Xu, Junlin Yang.

  19. Structured Neural Summarization. ICLR 2019. paper

    Patrick Fernandes, Miltiadis Allamanis, Marc Brockschmidt.

  20. Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks. NAACL 2019. paper

    Ningyu Zhang, Shumin Deng, Zhanlin Sun, Guanying Wang, Xi Chen, Wei Zhang, Huajun Chen.

  21. Text Generation from Knowledge Graphs with Graph Transformers. NAACL 2019. paper

    Rik Koncel-Kedziorski, Dhanush Bekal, Yi Luan, Mirella Lapata, Hannaneh Hajishirzi.

  22. Question Answering by Reasoning Across Documents with Graph Convolutional Networks. NAACL 2019. paper

    Nicola De Cao, Wilker Aziz, Ivan Titov.

  23. BAG: Bi-directional Attention Entity Graph Convolutional Network for Multi-hop Reasoning Question Answering. NAACL 2019. paper

    Yu Cao, Meng Fang, Dacheng Tao.

  24. GraphIE: A Graph-Based Framework for Information Extraction. NAACL 2019. paper

    Yujie Qian, Enrico Santus, Zhijing Jin, Jiang Guo, Regina Barzilay.

  25. Graph Convolution for Multimodal Information Extraction from Visually Rich Documents. NAACL 2019. paper

    Xiaojing Liu, Feiyu Gao, Qiong Zhang, Huasha Zhao.

  26. Structural Neural Encoders for AMR-to-text Generation. NAACL 2019. paper

    Marco Damonte, Shay B. Cohen.

  27. Abusive Language Detection with Graph Convolutional Networks. NAACL 2019. paper

    Pushkar Mishra, Marco Del Tredici, Helen Yannakoudakis, Ekaterina Shutova.

  28. Learning Graph Pooling and Hybrid Convolutional Operations for Text Representations. WWW 2019. paper

    Hongyang Gao, Yongjun Chen, Shuiwang Ji.

Path Classification

  1. STWalk: Learning Trajectory Representations in Temporal Graphs CoDS-COMAD 2018. paper

    Supriya Pandhre, Himangi Mittal, Manish Gupta, Vineeth N Balasubramanian.

Generation

  1. Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation. NeurIPS 2018. paper

    Jiaxuan You, Bowen Liu, Rex Ying, Vijay Pande, Jure Leskovec.

  2. Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders. NeurIPS 2018. paper

    Tengfei Ma, Jie Chen, Cao Xiao.

  3. Learning deep generative models of graphs. ICLR Workshop 2018. paper

    Yujia Li, Oriol Vinyals, Chris Dyer, Razvan Pascanu, Peter Battaglia.

  4. MolGAN: An implicit generative model for small molecular graphs. 2018. paper

    Nicola De Cao, Thomas Kipf.

  5. GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models. ICML 2018. paper

    Jiaxuan You, Rex Ying, Xiang Ren, William L. Hamilton, Jure Leskovec.

  6. NetGAN: Generating Graphs via Random Walks. ICML 2018. paper

    Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, Stephan Günnemann.

  7. Graphite: Iterative Generative Modeling of Graphs. ICML 2019. paper

    Aditya Grover, Aaron Zweig, Stefano Ermon.

  8. Generative Code Modeling with Graphs. ICLR 2019. paper

    Marc Brockschmidt, Miltiadis Allamanis, Alexander L. Gaunt, Oleksandr Polozov.

Combinatorial Optimization

  1. Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search. NeurIPS 2018. paper

    Zhuwen Li, Qifeng Chen, Vladlen Koltun.

  2. Learning a SAT Solver from Single-Bit Supervision. ICLR 2019. paper

    Daniel Selsam, Matthew Lamm, Benedikt Bünz, Percy Liang, Leonardo de Moura, David L. Dill.

  3. A Note on Learning Algorithms for Quadratic Assignment with Graph Neural Networks. PADL 2017. paper

    Alex Nowak, Soledad Villar, Afonso S. Bandeira, Joan Bruna.

  4. Attention Solves Your TSP, Approximately. 2018. paper

    Wouter Kool, Herke van Hoof, Max Welling.

  5. Learning to Solve NP-Complete Problems - A Graph Neural Network for Decision TSP. AAAI 2019. paper

    Marcelo O. R. Prates, Pedro H. C. Avelar, Henrique Lemos, Luis Lamb, Moshe Vardi.

  6. DAG-GNN: DAG Structure Learning with Graph Neural Networks. ICML 2019. paper

    Yue Yu, Jie Chen, Tian Gao, Mo Yu.

  7. Scalable Large Near-Clique Detection in Large-Scale Networks via Sampling. KDD 2015. paper

    Michael Mitzenmacher, Jakub Pachocki, Richard Peng, Charalampos Tsourakakis, Shen Chen Xu

  8. Gated Graph Sequence Neural Networks. ICLR 2016. paper

    Yujia Li, Daniel Tarlow, Marc Brockschmidt, Richard Zemel

  9. Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon. paper

    Yoshua Bengio, Andrea Lodi, Antoine Prouvost

  10. Learning Combinatorial Optimization Algorithms over Graphs. NIPS 2017 paper

    Hanjun Dai, Elias B. Khalil, Yuyu Zhang, Bistra Dilkina, Le Song

  11. Learning Combinatorial Optimization Algorithms over Graphs. NIPS 2017 paper

    Hanjun Dai, Elias B. Khalil, Yuyu Zhang, Bistra Dilkina, Le Song

Adversarial Attack

  1. Adversarial Attacks on Neural Networks for Graph Data. KDD 2018. paper

    Daniel Zügner, Amir Akbarnejad, Stephan Günnemann.

  2. Adversarial Attack on Graph Structured Data. ICML 2018. paper

    Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, Le Song.

  3. Adversarial Examples on Graph Data: Deep Insights into Attack and Defense. IJCAI 2019. paper

    Huijun Wu, Chen Wang, Yuriy Tyshetskiy, Andrew Docherty, Kai Lu, Liming Zhu.

  4. Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective. IJCAI 2019. paper

    Kaidi Xu, Hongge Chen, Sijia Liu, Pin-Yu Chen, Tsui-Wei Weng, Mingyi Hong, Xue Lin.

  5. Robust Graph Convolutional Networks Against Adversarial Attacks. KDD 2019. paper

    Dingyuan Zhu, Ziwei Zhang, Peng Cui, Wenwu Zhu.

  6. Certifiable Robustness and Robust Training for Graph Convolutional Networks. KDD 2019. paper

    Daniel Zügner, Stephan Günnemann.

  7. Adversarial Attacks on Node Embeddings via Graph Poisoning. ICML 2019. paper

    Aleksandar Bojchevski, Stephan Günnemann.

  8. Adversarial Attacks on Graph Neural Networks via Meta Learning. ICLR 2019. paper

    Daniel Zügner, Stephan Günnemann.

  9. PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks. ICLR 2019. paper

    Jan Svoboda, Jonathan Masci, Federico Monti, Michael Bronstein, Leonidas Guibas.

Graph Clustering

  1. Attributed Graph Clustering: A Deep Attentional Embedding Approach. IJCAI 2019. paper

    Chun Wang, Shirui Pan, Ruiqi Hu, Guodong Long, Jing Jiang, Chengqi Zhang.

  2. Attributed Graph Clustering via Adaptive Graph Convolution. IJCAI 2019. paper

    Xiaotong Zhang, Han Liu, Qimai Li, Xiao-Ming Wu.

  3. Learning Deep Representations for Graph Clustering. AAAI 2014. paper

    Fei Tian, Bin Gao, Qing Cui, Enhong Chen, Tie-Yan Liu.

  4. metapath2vec: Scalable Representation Learning for Heterogeneous Networks. KDD 2017. paper

    Yuxiao Dong, Nitesh V. Chawla, Ananthram Swami.

  5. Graph Clustering with Dynamic Embedding. KDD 2017. paper

    Carl Yang, Mengxiong Liu, Zongyi Wang, Liyuan Liu, Jiawei Han.

  6. Unsupervised Deep Embedding for Clustering Analysis. ICML 2016. paper

    Junyuan Xie, Ross Girshick, Ali Farhadi.

  7. Improved Deep Embedded Clustering with Local Structure Preservation. IJCAI 2017. paper

    Xifeng Guo, Long Gao, Xinwang Liu, Jianping Yin.

  8. Learning Structural Node Embeddings Via Diffusion Wavelets. SIGKDD 2018. paper

    Claire Donnat, Marinka Zitnik, David Hallac, Jure Leskovec.

  9. Heterogeneous Network Embedding via Deep Architectures. KDD 2015. paper

    Shiyu Chang, Wei Han, Jiliang Tang, Guo-Jun Qi, Charu C. Aggarwal, Thomas S. Huang.

  10. Higher-order Spectral Clustering for Heterogeneous Graphs. paper

    Aldo G. Carranza, Ryan A. Rossi, Anup Rao, Eunyee Koh.

  11. Community Detection and Link Prediction via Cluster-driven Low-rank Matrix Completion. IJCAI 2019. paper

    Junming Shao, Zhong Zhang, Zhongjing Yu, Jun Wang, Yi Zhao, Qinli Yang.

  12. Scaling Fine-grained Modularity Clustering for Massive Graphs. IJCAI 2019. paper

    Hiroaki Shiokawa, Toshiyuki Amagasa, Hiroyuki Kitagawa.

  13. EdMot: An Edge Enhancement Approach for Motif-aware Community Detection. KDD 2019. paper

    Pei-Zhen Li, Ling Huang, Chang-Dong Wang, Jian-Huang Lai.

Graph Classification

  1. Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing. ICML 2018. paper

    Davide Bacciu, Federico Errica, Alessio Micheli.

  2. Semi-Supervised Graph Classification: A Hierarchical Graph Perspective. WWW 2019. paper

    Jia Li, Yu Rong, Hong Cheng, Helen Meng, Wenbing Huang, Junzhou Huang.

  3. DDGK: Learning Graph Representations for Deep Divergence Graph Kernels. WWW 2019. paper

    Rami Al-Rfou, Dustin Zelle, Bryan Perozzi.

  4. Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity. IJCAI 2019. paper

    Yunsheng Bai, Hao Ding, Yang Qiao, Agustin Marinovic, Ken Gu, Ting Chen, Yizhou Sun, Wei Wang.

  5. Diffusion-Convolutional Neural Networks. NIPS 2016. paper

    James Atwood, Don Towsley.

  6. Learning Convolutional Neural Networks for Graphs. ICML 2016. paper

    Mathias Niepert, Mohamed Ahmed, Konstantin Kutzkov.

  7. How Powerful are Graph Neural Networks? ICLR 2019. paper

    Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka.

  8. Edge Attention-based Multi-Relational Graph Convolutional Networks. ICLR 2019. paper

    Chao Shang, Qinqing Liu, Ko-Shin Chen, Jiangwen Sun, Jin Lu, Jinfeng Yi, Jinbo Bi.

  9. Predict then Propagate: Graph Neural Networks meet Personalized PageRank. ICLR 2019. paper

    Johannes Klicpera, Aleksandar Bojchevski, Stephan Günnemann.

  10. Pre-training Graph Neural Networks. paper

    Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, Jure Leskovec.

Reinforcement Learning

  1. NerveNet: Learning Structured Policy with Graph Neural Networks. ICLR 2018. paper

    Tingwu Wang, Renjie Liao, Jimmy Ba, Sanja Fidler.

  2. Structured Dialogue Policy with Graph Neural Networks. ICCL 2018. paper

    Lu Chen, Bowen Tan, Sishan Long, Kai Yu.

  3. Relational inductive bias for physical construction in humans and machines. CogSci 2018. paper

    Jessica B. Hamrick, Kelsey R. Allen, Victor Bapst, Tina Zhu, Kevin R. McKee, Joshua B. Tenenbaum, Peter W. Battaglia.

  4. Relational Deep Reinforcement Learning. arxiv 2018. paper

    Vinicius Zambaldi, David Raposo, Adam Santoro, Victor Bapst, Yujia Li, Igor Babuschkin, Karl Tuyls, David Reichert, Timothy Lillicrap, Edward Lockhart, Murray Shanahan, Victoria Langston, Razvan Pascanu, Matthew Botvinick, Oriol Vinyals, Peter Battaglia.

  5. Playing Text-Adventure Games with Graph-Based Deep Reinforcement Learning. NAACL 2019. paper

    Prithviraj Ammanabrolu, Mark O. Riedl.

Traffic Network

  1. Spatiotemporal Multi‐Graph Convolution Network for Ride-hailing Demand Forecasting. AAAI 2019. paper

    Xu Geng, Yaguang Li, Leye Wang, Lingyu Zhang, Qiang Yang, Jieping Ye, Yan Liu.

  2. Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting. AAAI 2019. paper

    Shengnan Guo, Youfang Lin, Ning Feng, Chao Song, Huaiyu Wan.

  3. Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting. arxiv 2018. paper

    Zhiyong Cui, Kristian Henrickson, Ruimin Ke, Yinhai Wang.

  4. Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting. IJCAI 2018. paper

    Bing Yu, Haoteng Yin, Zhanxing Zhu.

  5. Origin-Destination Matrix Prediction via Graph Convolution: a New Perspective of Passenger Demand Modeling. KDD 2019. paper

    Yuandong Wang, Hongzhi Yin, Hongxu Chen, Tianyu Wo, Jie Xu, Kai Zheng.

  6. Predicting Path Failure In Time-Evolving Graphs. KDD 2019. paper

    Jia Li, Zhichao Han, Hong Cheng, Jiao Su, Pengyun Wang, Jianfeng Zhang, Lujia Pan.

  7. Stochastic Weight Completion for Road Networks using Graph Convolutional Networks. ICDE 2019. paper

    Jilin Hu, Chenjuan Guo, Bin Yang, Christian S. Jensen.

  8. STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting. IJCAI 2019. paper

    Lei Bai, Lina Yao, Salil.S Kanhere, Xianzhi Wang, Quan.Z Sheng.

  9. Graph WaveNet for Deep Spatial-Temporal Graph Modeling. IJCAI 2019. paper

    Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Chengqi Zhang.

  10. Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. ICLR 2018. paper

    Yaguang Li, Rose Yu, Cyrus Shahabi, Yan Liu.

  11. GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs. UAI 2018. paper

    Jiani Zhang, Xingjian Shi, Junyuan Xie, Hao Ma, Irwin King, Dit-Yan Yeung.

Few-shot and Zero-shot Learning

  1. Few-Shot Learning with Graph Neural Networks. ICLR 2018. paper

    Victor Garcia, Joan Bruna.

  2. Prototype Propagation Networks (PPN) for Weakly-supervised Few-shot Learning on Category Graph. IJCAI 2019. paper

    Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, Lina Yao, Chengqi Zhang.

  3. Edge-labeling Graph Neural Network for Few-shot Learning. CVPR 2019. paper

    Jongmin Kim, Taesup Kim, Sungwoong Kim, Chang D. Yoo.

  4. Generating Classification Weights with GNN Denoising Autoencoders for Few-Shot Learning. CVPR 2019. paper

    Spyros Gidaris, Nikos Komodakis.

  5. Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs. CVPR 2018. paper

    Xiaolong Wang, Yufei Ye, Abhinav Gupta.

  6. Rethinking Knowledge Graph Propagation for Zero-Shot Learning. CVPR 2019. paper

    Michael Kampffmeyer, Yinbo Chen, Xiaodan Liang, Hao Wang, Yujia Zhang, Eric P. Xing.

  7. Multi-Label Zero-Shot Learning with Structured Knowledge Graphs. CVPR 2018. paper

    Chung-Wei Lee, Wei Fang, Chih-Kuan Yeh, Yu-Chiang Frank Wang.

Reinforcement Learning

  1. Relational inductive bias for physical construction in humans and machines. CogSci 2018. paper

    Jessica B. Hamrick, Kelsey R. Allen, Victor Bapst, Tina Zhu, Kevin R. McKee, Joshua B. Tenenbaum, Peter W. Battaglia.

  2. Relational Deep Reinforcement Learning. arxiv 2018. paper

    Vinicius Zambaldi, David Raposo, Adam Santoro, Victor Bapst, Yujia Li, Igor Babuschkin, Karl Tuyls, David Reichert, Timothy Lillicrap, Edward Lockhart, Murray Shanahan, Victoria Langston, Razvan Pascanu, Matthew Botvinick, Oriol Vinyals, Peter Battaglia.

  3. Action Schema Networks: Generalised Policies with Deep Learning. AAAI 2018. paper

    Sam Toyer, Felipe Trevizan, Sylvie Thiébaux, Lexing Xie.

Program Representation

  1. Learning to Represent Programs with Graphs. ICLR 2018. paper

    Miltiadis Allamanis, Marc Brockschmidt, Mahmoud Khademi.

  2. Open Vocabulary Learning on Source Code with a Graph-Structured Cache. ICML 2019. paper

    Milan Cvitkovic, Badal Singh, Anima Anandkumar.

  3. Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks. 2018. paper

    Xu, Kun; Wu, Lingfei; Wang, Zhiguo; Feng, Yansong; Witbrock, Michael; Sheinin, Vadim.

Time Prediction

  1. Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs. ICML 2017. paper code

    Trivedi, Rakshit; Dai, Hanjun; Wang, Yichen; Song, Le.

  2. SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity. KDD 2015. paper

    Zhao, Qingyuan; Erdogdu, Murat A.; He, Hera Y.; Rajaraman, Anand; Leskovec, Jure.

Visualization

  1. Scalable graph exploration and visualization: Sensemaking challenges and opportunities BigComp 2015. paper

    Robert Pienta, James Abello, Minsuk Kahng, Duen Horng Chau.

  2. Semi-Supervised Classification with Graph Convolutional Networks. ICLR 2017. paper

    Thomas N. Kipf, Max Welling.

  3. Semi-supervised Learning on Graphs with Generative Adversarial Nets CIKM 2018. paper

    Ming Ding, Jie Tang, Jie Zhang

  4. Spectral Networks and Locally Connected Networks on Graphs. ICLR 2014. paper

    Joan Bruna, Wojciech Zaremba, Arthur Szlam, Yann LeCun.

  5. Structural Deep Network Embedding. KDD2018. paper code

    Wang, Daixin and Cui, Peng and Zhu, Wenwu.

  6. Structured Sequence Modeling with Graph Convolutional Recurrent Networks 2017. paper

    Youngjoo Seo, Michaël Defferrard, Pierre Vandergheynst, Xavier Bresson

Link Prediction

  1. node2vec: Scalable Feature Learning for Networks. SIGKDD 2016. paper

    Aditya Grover, Jure Leskovec

  2. Structural Deep Network Embedding. KDD2018. paper code

    Wang, Daixin and Cui, Peng and Zhu, Wenwu.

  3. Inductive Representation Learning on Large Graphs. NIPS 2017. paper

    William L. Hamilton, Rex Ying, Jure Leskovec.

  4. Representation Learning over Dynamic Graphs. ICLR 2019. paper

    Rakshit Trivedi, Mehrdad Farajtabar, Prasenjeet Biswal, Hongyuan Zha.

  5. Link Prediction with Spatial and Temporal Consistency in Dynamic Networks. ICLR 2017. paper

    Wenchao Yu, Wei Cheng, Charu C Aggarwal, Haifeng Chen, Wei Wang.

  6. Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs. ICML 2017. paper code

    Trivedi, Rakshit; Dai, Hanjun; Wang, Yichen; Song, Le.

  7. Deep Inductive Network Representation Learning. WWW 2018. paper

    Ryan A. Rossi, Rong Zhou, Nesreen K. Ahmed.

  8. Streaming Graph Neural Networks. 2018. paper

    Yao Ma, Ziyi Guo, Zhaochun Ren, Eric Zhao, Jiliang Tang, Dawei Yin.

  9. Modeling polypharmacy side effects with graph convolutional networks. ISMB 2018. paper

    Marinka Zitnik, Monica Agrawal, Jure Leskovec.

  10. Deep Inductive Graph Representation Learning. paper

    Ryan Anthony Rossi, Rong Zhou, Nesreen Ahmed.

  11. Multi-task Network Embedding. DSAA 2017 paper

    RLinchuan Xu, Xiaokai Wei, Jiannong Cao, Philip S. Yu.

  12. Modeling Relational Data with Graph Convolutional Networks. ESWC 2018. paper

    Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, Max Welling.

  13. EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs. paper

    Aldo Pareja, Giacomo Domeniconi, Jie Chen, Tengfei Ma, Toyotaro Suzumura, Hiroki Kanezashi, Tim Kaler, Charles E. Leisersen.

  14. Community Detection and Link Prediction via Cluster-driven Low-rank Matrix Completion. IJCAI 2019. paper

    Junming Shao, Zhong Zhang, Zhongjing Yu, Jun Wang, Yi Zhao, Qinli Yang.

Node Classification

  1. Diffusion-Convolutional Neural Networks. NIPS 2016. paper

    James Atwood, Don Towsley.

  2. node2vec: Scalable Feature Learning for Networks. SIGKDD 2016. paper

    Aditya Grover, Jure Leskovec

  3. LINE: Large-scale Information Network Embedding. WWW 2015. paper

    Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, Qiaozhu Mei.

  4. Structural Deep Network Embedding. KDD2018. paper code

    Wang, Daixin and Cui, Peng and Zhu, Wenwu.

  5. Semi-Supervised Classification with Graph Convolutional Networks. ICLR 2017. paper

    Thomas N. Kipf, Max Welling.

  6. Inductive Representation Learning on Large Graphs. NIPS 2017. paper

    William L. Hamilton, Rex Ying, Jure Leskovec.

  7. Gated Graph Sequence Neural Networks. ICLR 2016. paper

    Yujia Li, Daniel Tarlow, Marc Brockschmidt, Richard Zemel.

  8. Semi-supervised Learning on Graphs with Generative Adversarial Nets CIKM 2018. paper

    Ming Ding, Jie Tang, Jie Zhang

  9. metapath2vec: Scalable Representation Learning for Heterogeneous Networks. KDD 2017. paper

    Yuxiao Dong, Nitesh V. Chawla, Ananthram Swami.

  10. Graph Attention Networks. ICLR 2018. paper

    Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, Yoshua Bengio.

  11. Revisiting Semi-Supervised Learning with Graph Embeddings. ICML 2016. paper

    Zhilin Yang, William W. Cohen, Ruslan Salakhutdinov.

  12. Dyn2Vec: Exploiting dynamic behaviour using difference networks-based node embeddings for classification. ICDATA 2018. paper

    Sandra Mitrovic, ochen De Weerdt.

  13. Heterogeneous Network Embedding via Deep Architectures. KDD 2015. paper

    Shiyu Chang, Wei Han, Jiliang Tang, Guo-Jun Qi, Charu C. Aggarwal, Thomas S. Huang.

  14. How Powerful are Graph Neural Networks? ICLR 2019. paper

    Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka.

  15. Deep Inductive Network Representation Learning. WWW 2018. paper

    Ryan A. Rossi, Rong Zhou, Nesreen K. Ahmed.

  16. GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs. UAI 2018. paper

    Jiani Zhang, Xingjian Shi, Junyuan Xie, Hao Ma, Irwin King, Dit-Yan Yeung.

  17. Streaming Graph Neural Networks. 2018. paper

    Yao Ma, Ziyi Guo, Zhaochun Ren, Eric Zhao, Jiliang Tang, Dawei Yin.

  18. struc2vec: Learning Node Representations from Structural Identity. KDD 2017. paper

    Leonardo F. R. Ribeiro, Pedro H. P. Savarese, Daniel R. Figueiredo..

  19. Learning Structural Node Embeddings Via Diffusion Wavelets. SIGKDD 2018. paper

    Claire Donnat, Marinka Zitnik, David Hallac, Jure Leskovec.

  20. Deep Inductive Graph Representation Learning. paper

    Ryan Anthony Rossi, Rong Zhou, Nesreen Ahmed.

  21. Multi-task Network Embedding. DSAA 2017 paper

    RLinchuan Xu, Xiaokai Wei, Jiannong Cao, Philip S. Yu.

  22. Modeling Relational Data with Graph Convolutional Networks. ESWC 2018. paper

    Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, Max Welling.

  23. Role action embeddings: scalable representation of network positions. paper

    George Berry.

  24. Stochastic Training of Graph Convolutional Networks with Variance Reduction. ICML 2018. paper

    Jianfei Chen, Jun Zhu, Le Song.

  25. Scalable Graph Learning for Anti-Money Laundering: A First Look. NIPS 2018. paper

    Mark Weber, Jie Chen, Toyotaro Suzumura, Aldo Pareja, Tengfei Ma, Hiroki Kanezashi, Tim Kaler, Charles E. Leiserson, Tao B. Schardl.

  26. Pre-training Graph Neural Networks. paper

    Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, Jure Leskovec.

  27. Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec. WSDM 2018. paper

    Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, Jie Tang.