【论文整理】图网络必读论文 Must-read papers on GNN GNN: graph neural network Contributed by Jie Zhou, Ganqu Cui, Zhengyan Zhang and Yushi Bai. Content 1. Survey 2. Models
2.1 Basic Models
2.2 Graph Types
2.3 Pooling Methods
2.4 Analysis
2.5 Efficiency 3. Applications
3.1 Physics
3.2 Chemistry and Biology
3.3 Knowledge Graph
3.4 Recommender Systems
3.5 Computer Vision
3.6 Natural Language Processing
3.7 Generation
3.8 Combinatorial Optimization
3.9 Adversarial Attack
3.10 Graph Clustering
3.11 Graph Classification
3.12 Reinforcement Learning
3.13 Traffic Network
3.14 Few-shot and Zero-shot Learning
3.15 Program Representation
3.16 Social Network 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 Basic Models 1. Graphical-Based Learning Environments for Pattern Recognition. SSPR/SPR 2004. paper
Franco Scarselli, Ah Chung Tsoi, Marco Gori, Markus Hagenbuchner. 2. A new model for learning in graph domains. IJCNN 2005. paper
Marco Gori, Gabriele Monfardini, Franco Scarselli. 3. Graph Neural Networks for Ranking Web Pages. WI 2005. paper
Franco Scarselli, Sweah Liang Yong, Marco Gori, Markus Hagenbuchner, Ah Chung Tsoi, Marco Maggini. 4. Spectral Networks and Locally Connected Networks on Graphs. ICLR 2014. paper
Joan Bruna, Wojciech Zaremba, Arthur Szlam, Yann LeCun. 5. Deep Convolutional Networks on Graph-Structured Data. arxiv 2015. paper
Mikael Henaff, Joan Bruna, Yann LeCun. 6. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. NIPS 2016. paper
Michaël Defferrard, Xavier Bresson, Pierre Vandergheynst. 7. Diffusion-Convolutional Neural Networks. NIPS 2016. paper
James Atwood, Don Towsley. 8. Gated Graph Sequence Neural Networks. ICLR 2016. paper
Yujia Li, Daniel Tarlow, Marc Brockschmidt, Richard Zemel. 9. Learning Convolutional Neural Networks for Graphs. ICML 2016. paper
Mathias Niepert, Mohamed Ahmed, Konstantin Kutzkov. 10. Semantic Object Parsing with Graph LSTM. ECCV 2016. paper
Xiaodan Liang, Xiaohui Shen, Jiashi Feng, Liang Lin, Shuicheng Yan.
11. Semi-Suervised Classification with Graph Convolutional Networks. ICLR 2017. paper
Thomas N. Kipf, Max Welling. 12. Inductive Representation Learning on Large Graphs. NIPS 2017. paper
William L. Hamilton, Rex Ying, Jure Leskovec. 13. 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. 14. Graph Attention Networks. ICLR 2018. paper
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, Yoshua Bengio. 15. Covariant Compositional Networks For Learning Graphs. ICLR 2018. paper
Risi Kondor, Hy Truong Son, Horace Pan, Brandon Anderson, Shubhendu Trivedi. 16. Graph Partition Neural Networks for Semi-Supervised Classification. ICLR 2018. paper
Renjie Liao, Marc Brockschmidt, Daniel Tarlow, Alexander L. Gaunt, Raquel Urtasun, Richard Zemel. 17. Inference in Probabilistic Graphical Models by Graph Neural Networks. ICLR Workshop 2018. paper
KiJung Yoon, Renjie Liao, Yuwen Xiong, Lisa Zhang, Ethan Fetaya, Raquel Urtasun, Richard Zemel, Xaq Pitkow. 18. Structure-Aware Convolutional Neural Networks. NeurIPS 2018. paper
Jianlong Chang, Jie Gu, Lingfeng Wang, Gaofeng Meng, Shiming Xiang, Chunhong Pan. 19. Bayesian Semi-supervised Learning with Graph Gaussian Processes. NeurIPS 2018. paper
Yin Cheng Ng, Nicolò Colombo, Ricardo Silva. 20. Adaptive Graph Convolutional Neural Networks. AAAI 2018. paper
Ruoyu Li, Sheng Wang, Feiyun Zhu, Junzhou Huang. more
21. Dual Graph Convolutional Networks for Graph-Based Semi-Supervised Classification. WWW 2018. paper
Chenyi Zhuang, Qiang Ma. 22. Learning Steady-States of Iterative Algorithms over Graphs. ICML 2018. paper
Hanjun Dai, Zornitsa Kozareva, Bo Dai, Alex Smola, Le Song. 23. Graph Capsule Convolutional Neural Networks. ICML 2018 Workshop. paper
Saurabh Verma, Zhi-Li Zhang. 24. Capsule Graph Neural Network. ICLR 2019. paper
Zhang Xinyi, Lihui Chen. 25. Graph Wavelet Neural Network. ICLR 2019. paper
Bingbing Xu, Huawei Shen, Qi Cao, Yunqi Qiu, Xueqi Cheng. 26. Deep Graph Infomax. ICLR 2019. paper
Petar Veličković, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, R Devon Hjelm. 27. Predict then Propagate: Graph Neural Networks meet Personalized PageRank. ICLR 2019. paper
Johannes Klicpera, Aleksandar Bojchevski, Stephan Günnemann. 28. LanczosNet: Multi-Scale Deep Graph Convolutional Networks. ICLR 2019. paper
Renjie Liao, Zhizhen Zhao, Raquel Urtasun, Richard Zemel. 29. Invariant and Equivariant Graph Networks. ICLR 2019. paper
Haggai Maron, Heli Ben-Hamu, Nadav Shamir, Yaron Lipman. 30. GMNN: Graph Markov Neural Networks. ICML 2019. paper
Meng Qu, Yoshua Bengio, Jian Tang. 31. Position-aware Graph Neural Networks. ICML 2019. paper
Jiaxuan You, Rex Ying, Jure Leskovec. 32. Disentangled Graph Convolutional Networks. ICML 2019. paper
Jianxin Ma, Peng Cui, Kun Kuang, Xin Wang, Wenwu Zhu. 33. Stochastic Blockmodels meet Graph Neural Networks. ICML 2019. paper
Nikhil Mehta, Lawrence Carin, Piyush Rai. 34. Learning Discrete Structures for Graph Neural Networks. ICML 2019. paper
Luca Franceschi, Mathias Niepert, Massimiliano Pontil, Xiao He. 35. MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing. ICML 2019. paper
Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, Aram Galstyan. 36. DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification. KDD 2019. paper
Jun Wu, Jingrui He, Jiejun Xu. 37. Graph Representation Learning via Hard and Channel-Wise Attention Networks. KDD 2019. paper
Hongyang Gao, Shuiwang Ji. 38. Graph Learning-Convolutional Networks. CVPR 2019. paper
Bo Jiang, Ziyan Zhang, Doudou Lin, Jin Tang. 39. Data Representation and Learning with Graph Diffusion-Embedding Networks. CVPR 2019. paper
Bo Jiang, Doudou Lin, Jin Tang, Bin Luo. 40. Label Efficient Semi-Supervised Learning via Graph Filtering. CVPR 2019. paper
Qimai Li, Xiao-Ming Wu, Han Liu, Xiaotong Zhang, Zhichao Guan. 41. SPAGAN: Shortest Path Graph Attention Network. IJCAI 2019. paper
Yiding Yang, Xinchao Wang, Mingli Song, Junsong Yuan, Dacheng Tao. 42. Topology Optimization based Graph Convolutional Network. IJCAI 2019. paper
Liang Yang, Zesheng Kang, Xiaochun Cao, Di Jin, Bo Yang, Yuanfang Guo. 43. Hierarchical Graph Convolutional Networks for Semi-supervised Node Classification. IJCAI 2019. paper
Fenyu Hu, Yanqiao Zhu, Shu Wu, Liang Wang, Tieniu Tan. 44. Masked Graph Convolutional Network. IJCAI 2019. paper
Liang Yang, Fan Wu, Yingkui Wang, Junhua Gu, Yuanfang Guo. 45. Dual Self-Paced Graph Convolutional Network: Towards Reducing Attribute Distortions Induced by Topology. IJCAI 2019. paper
Liang Yang, Zhiyang Chen, Junhua Gu, Yuanfang Guo. 46. Bayesian graph convolutional neural networks for semi-supervised classification. AAAI 2019. paper
Yingxue Zhang, Soumyasundar Pal, Mark Coates, Deniz Üstebay. 47. GeniePath: Graph Neural Networks with Adaptive Receptive Paths. AAAI 2019. paper
Ziqi Liu, Chaochao Chen, Longfei Li, Jun Zhou, Xiaolong Li, Le Song, Yuan Qi. 48. Gaussian-Induced Convolution for Graphs. AAAI 2019. paper
Jiatao Jiang, Zhen Cui, Chunyan Xu, Jian Yang. 49. Fisher-Bures Adversary Graph Convolutional Networks. UAI 2019. paper
Ke Sun, Piotr Koniusz, Zhen Wang. 50. N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification. UAI 2019. paper
Sami Abu-El-Haija, Amol Kapoor, Bryan Perozzi, Joonseok Lee. 51. Confidence-based Graph Convolutional Networks for Semi-Supervised Learning. AISTATS 2019. paper
Shikhar Vashishth, Prateek Yadav, Manik Bhandari, Partha Talukdar. 52. Lovasz Convolutional Networks. AISTATS 2019. paper
Prateek Yadav, Madhav Nimishakavi, Naganand Yadati, Shikhar Vashishth, Arun Rajkumar, Partha Talukdar. Graph Types 1. DyRep: Learning Representations over Dynamic Graphs. ICLR 2019. paper
Rakshit Trivedi, Mehrdad Farajtabar, Prasenjeet Biswal, Hongyuan Zha. 2. Hypergraph Neural Networks. AAAI 2019. paper
Yifan Feng, Haoxuan You, Zizhao Zhang, Rongrong Ji, Yue Gao.
3. Heterogeneous Graph Attention Network. WWW 2019. paper
Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Peng Cui, P. Yu, Yanfang Ye. 4. Representation Learning for Attributed Multiplex Heterogeneous Network. KDD 2019. paper
Yukuo Cen, Xu Zou, Jianwei Zhang, Hongxia Yang, Jingren Zhou, Jie Tang. 5. ActiveHNE: Active Heterogeneous Network Embedding. IJCAI 2019. paper
Xia Chen, Guoxian Yu, Jun Wang, Carlotta Domeniconi, Zhao Li, Xiangliang Zhang. 6. GCN-LASE: Towards Adequately Incorporating Link Attributes in Graph Convolutional Networks. IJCAI 2019. paper
Ziyao Li, Liang Zhang, Guojie Song. 7. Dynamic Hypergraph Neural Networks. IJCAI 2019. paper
Jianwen Jiang, Yuxuan Wei, Yifan Feng, Jingxuan Cao, Yue Gao. 8. Exploiting Interaction Links for Node Classification with Deep Graph Neural Networks. IJCAI 2019. paper
Hogun Park, Jennifer Neville. 9. Exploiting Edge Features in Graph Neural Networks. CVPR 2019. paper
Liyu Gong, Qiang Cheng. Pooling Methods 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. Analysis 1. 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. 2. Neural networks for relational learning: an experimental comparison. Machine Learning 2011. paper
Werner Uwents, Gabriele Monfardini, Hendrik Blockeel, Marco Gori, Franco Scarselli. 3. Mean-field theory of graph neural networks in graph partitioning. NeurIPS 2018. paper
Tatsuro Kawamoto, Masashi Tsubaki, Tomoyuki Obuchi. 4. Representation Learning on Graphs with Jumping Knowledge Networks. ICML 2018. paper
Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, Stefanie Jegelka. 5. Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning. AAAI 2018. paper
Qimai Li, Zhichao Han, Xiao-Ming Wu. 6. How Powerful are Graph Neural Networks? ICLR 2019. paper
Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka. 7. Stability and Generalization of Graph Convolutional Neural Networks. KDD 2019. paper
Saurabh Verma, Zhi-Li Zhang. 8. Simplifying Graph Convolutional Networks. ICML 2019. paper
Felix Wu, Tianyi Zhang, Amauri Holanda de Souza Jr., Christopher Fifty, Tao Yu, Kilian Q. Weinberger. 9. Explainability Methods for Graph Convolutional Neural Networks. CVPR 2019. paper
Phillip E. Pope, Soheil Kolouri, Mohammad Rostami, Charles E. Martin, Heiko Hoffmann.
10. Can GCNs Go as Deep as CNNs? ICCV 2019. paper
Guohao Li, Matthias Müller, Ali Thabet, Bernard Ghanem. 11. 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. Efficiency 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. Applications 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. 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. Recommender 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. Graph Neural Networks for Object Localization. ECAI 2006. paper
Gabriele Monfardini, Vincenzo Di Massa, Franco Scarselli, Marco Gori. 2. Learning Human-Object Interactions by Graph Parsing Neural Networks. ECCV 2018. paper
Siyuan Qi, Wenguan Wang, Baoxiong Jia, Jianbing Shen, Song-Chun Zhu.
3. Learning Conditioned Graph Structures for Interpretable Visual Question Answering. NeurIPS 2018. paper
Will Norcliffe-Brown, Efstathios Vafeias, Sarah Parisot. 4. Symbolic Graph Reasoning Meets Convolutions. NeurIPS 2018. paper
Xiaodan Liang, Zhiting Hu, Hao Zhang, Liang Lin, Eric P. Xing. 5. Out of the Box: Reasoning with Graph Convolution Nets for Factual Visual Question Answering. NeurIPS 2018. paper
Medhini Narasimhan, Svetlana Lazebnik, Alexander Schwing. 6. Structural-RNN: Deep Learning on Spatio-Temporal Graphs. CVPR 2016. paper
Ashesh Jain, Amir R. Zamir, Silvio Savarese, Ashutosh Saxena. 7. Relation Networks for Object Detection. CVPR 2018. paper
Han Hu, Jiayuan Gu, Zheng Zhang, Jifeng Dai, Yichen Wei. 8. Learning Region features for Object Detection. ECCV 2018. paper
Jiayuan Gu, Han Hu, Liwei Wang, Yichen Wei, Jifeng Dai. 9. The More You Know: Using Knowledge Graphs for Image Classification. CVPR 2017. paper
Kenneth Marino, Ruslan Salakhutdinov, Abhinav Gupta. 10. Understanding Kin Relationships in a Photo. TMM 2012. paper
Siyu Xia, Ming Shao, Jiebo Luo, Yun Fu. 11. Graph-Structured Representations for Visual Question Answering. CVPR 2017. paper
Damien Teney, Lingqiao Liu, Anton van den Hengel. 12. Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition. AAAI 2018. paper
Sijie Yan, Yuanjun Xiong, Dahua Lin. 13. 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.
14. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. CVPR 2018. paper
Charles R. Qi, Hao Su, Kaichun Mo, Leonidas J. Guibas. 15. 3D Graph Neural Networks for RGBD Semantic Segmentation. CVPR 2017. paper
Xiaojuan Qi, Renjie Liao, Jiaya Jia, Sanja Fidler, Raquel Urtasun. 16. Iterative Visual Reasoning Beyond Convolutions. CVPR 2018. paper
Xinlei Chen, Li-Jia Li, Li Fei-Fei, Abhinav Gupta. 17. Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs. CVPR 2017. paper
Martin Simonovsky, Nikos Komodakis. 18. Situation Recognition with Graph Neural Networks. ICCV 2017. paper
Ruiyu Li, Makarand Tapaswi, Renjie Liao, Jiaya Jia, Raquel Urtasun, Sanja Fidler. 19. Deep Reasoning with Knowledge Graph for Social Relationship Understanding. IJCAI 2018. paper
Zhouxia Wang, Tianshui Chen, Jimmy Ren, Weihao Yu, Hui Cheng, Liang Lin. 20. 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 21. 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. 22. Multi-Label Image Recognition with Graph Convolutional Networks. CVPR 2019. paper
Zhao-Min Chen, Xiu-Shen Wei, Peng Wang, Yanwen Guo. 23. Spatial-Aware Graph Relation Network for Large-Scale Object Detection. CVPR 2019. paper
Hang Xu, Chenhan Jiang, Xiaodan Liang, Zhenguo Li. 24. GCAN: Graph Convolutional Adversarial Network for Unsupervised Domain Adaptation. CVPR 2019. paper
Xinhong Ma, Tianzhu Zhang, Changsheng Xu. 25. 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. 26. Attentive Relational Networks for Mapping Images to Scene Graphs. CVPR 2019. paper
Mengshi Qi, Weijian Li, Zhengyuan Yang, Yunhong Wang, Jiebo Luo. 27. Knowledge-Embedded Routing Network for Scene Graph Generation. CVPR 2019. paper
Tianshui Chen, Weihao Yu, Riquan Chen, Liang Lin. 28. Auto-Encoding Scene Graphs for Image Captioning. CVPR 2019. paper
Xu Yang, Kaihua Tang, Hanwang Zhang, Jianfei Cai. 29. Learning to Cluster Faces on an Affinity Graph. CVPR 2019. paper
Lei Yang, Xiaohang Zhan, Dapeng Chen, Junjie Yan, Chen Change Loy, Dahua Lin. 30. Learning a Deep ConvNet for Multi-label Classification with Partial Labels. CVPR 2019. paper
Thibaut Durand, Nazanin Mehrasa, Greg Mori. 31. 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. 32. Learning Actor Relation Graphs for Group Activity Recognition. CVPR 2019. paper
Jianchao Wu, Limin Wang, Li Wang, Jie Guo, Gangshan Wu. 33. 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. 34. 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. 35. Graph-Based Global Reasoning Networks. CVPR 2019. paper
Yunpeng Chen, Marcus Rohrbach, Zhicheng Yan, Shuicheng Yan, Jiashi Feng, Yannis Kalantidis. 36. Linkage Based Face Clustering via Graph Convolution Network. CVPR 2019. paper
Zhongdao Wang, Liang Zheng, Yali Li, Shengjin Wang. 37. Fast Interactive Object Annotation with Curve-GCN. CVPR 2019. paper
Huan Ling, Jun Gao, Amlan Kar, Wenzheng Chen, Sanja Fidler. 38. Semantic Graph Convolutional Networks for 3D Human Pose Regression. CVPR 2019. paper
Long Zhao, Xi Peng, Yu Tian, Mubbasir Kapadia, Dimitris N. Metaxas. 39. 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. 40. Graphonomy: Universal Human Parsing via Graph Transfer Learning. CVPR 2019. paper
Ke Gong, Yiming Gao, Xiaodan Liang, Xiaohui Shen, Meng Wang, Liang Lin. 41. Learning Context Graph for Person Search. CVPR 2019. paper
Yichao Yan, Qiang Zhang, Bingbing Ni, Wendong Zhang, Minghao Xu, Xiaokang Yang. 42. Occlusion-Net: 2D/3D Occluded Keypoint Localization Using Graph Networks. CVPR 2019. paper
N. Dinesh Reddy, Minh Vo, Srinivasa G. Narasimhan. 43. 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. 44. Context-Aware Visual Compatibility Prediction. CVPR 2019. paper
Guillem Cucurull, Perouz Taslakian, David Vazquez. 45. Graph Attention Convolution for Point Cloud Semantic Segmentation. CVPR 2019. paper
Lei Wang, Yuchun Huang, Yaolin Hou, Shenman Zhang, Jie Shan.
46. 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. 47. al-Structural Graph Convolutional Networks for Skeleton-based Action Recognition. CVPR 2019. paper
Maosen Li, Siheng Chen, Xu Chen, Ya Zhang, Yanfeng Wang, Qi Tian. 48. Graph Convolutional Tracking. CVPR 2019. paper
Junyu Gao, Tianzhu Zhang, Changsheng Xu. 49. Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition. CVPR 2019. paper
Lei Shi, Yifan Zhang, Jian Cheng, Hanqing Lu. 50. Skeleton-Based Action Recognition With Directed Graph Neural Networks. CVPR 2019. paper
Lei Shi, Yifan Zhang, Jian Cheng, Hanqing Lu. 51. Neural Module Networks. CVPR 2016. paper
Jacob Andreas, Marcus Rohrbach, Trevor Darrell, Dan Klein. 52. LatentGNN: Learning Efficient Non-local Relations for Visual Recognition. ICML 2019. paper
Songyang Zhang, Shipeng Yan, Xuming He. 53. Graph Convolutional Gaussian Processes. ICML 2019. paper
Ian Walker, Ben Glocker. 54. GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects. ICML 2019. paper
Edward J. Smith, Scott Fujimoto, Adriana Romero, David Meger. Natural Language Processing 1. Conversation Modeling on Reddit using a Graph-Structured LSTM. TACL 2018. paper
Vicky Zayats, Mari Ostendorf. 2. Learning Graphical State Transitions. ICLR 2017. paper
Daniel D. Johnson.
3. Multiple Events Extraction via Attention-based Graph Information Aggregation. EMNLP 2018. paper
Xiao Liu, Zhunchen Luo, Heyan Huang. 4. Recurrent Relational Networks. NeurIPS 2018. paper
Rasmus Palm, Ulrich Paquet, Ole Winther. 5. Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks. ACL 2015. paper
Kai Sheng Tai, Richard Socher, Christopher D. Manning. 6. Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling. EMNLP 2017. paper
Diego Marcheggiani, Ivan Titov. 7. Graph Convolutional Networks with Argument-Aware Pooling for Event Detection. AAAI 2018. paper
Thien Huu Nguyen, Ralph Grishman. 8. Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks. NAACL 2018. paper
Diego Marcheggiani, Joost Bastings, Ivan Titov. 9. 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. 10. Graph Cvolution over Pruned Dependency Trees Improves Relation Extraction. EMNLP 2018. paper
Yuhao Zhang, Peng Qi, Christopher D. Manning. 11. N-ary relation extraction using graph state LSTM. EMNLP 18. paper
Linfeng Song, Yue Zhang, Zhiguo Wang, Daniel Gildea. 12. A Graph-to-Sequence Model for AMR-to-Text Generation. ACL 2018. paper
Linfeng Song, Yue Zhang, Zhiguo Wang, Daniel Gildea. 13. Graph-to-Sequence Learning using Gated Graph Neural Networks. ACL 2018. paper
Daniel Beck, Gholamreza Haffari, Trevor Cohn.
14. Cross-Sentence N-ary Relation Extraction with Graph LSTMs. TACL. paper
Nanyun Peng, Hoifung Poon, Chris Quirk, Kristina Toutanova, Wen-tau Yih. 15. Sentence-State LSTM for Text Representation. ACL 2018. paper
Yue Zhang, Qi Liu, Linfeng Song. 16. End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures. ACL 2016. paper
Makoto Miwa, Mohit Bansal. 17. Gph Convolutional Encoders for Syntax-aware Neural Machine Translation. EMNLP 2017. paper
Joost Bastings, Ivan Titov, Wilker Aziz, Diego Marcheggiani, Khalil Sima’an. 18. Semi-survised User Geolocation via Graph Convolutional Networks. ACL 2018. paper
Afshin Rahimi, Trevor Cohn, Timothy Baldwin. 19. Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering. COLING 2018. paper
Daniil Sorokin, Iryna Gurevych. 20. Graph Convolutional Networks for Text Classification. AAAI 2019. paper
Liang Yao, Chengsheng Mao, Yuan Luo. more 21. Constructing Narrative Event Evol...
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