图神经网络（GNN)资源帖视频及必读论文

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篇首语：本文由小常识网(cha138.com)小编为大家整理，主要介绍了图神经网络（GNN)资源帖视频及必读论文相关的知识，希望对你有一定的参考价值。

最近在看图神经网络，发现了部分宝藏

图神经网络资源大集合

https://blog.csdn.net/weixin_45519842/article/details/109140724

视频部分

个人感觉容易懂的

科普各类讲座https://www.bilibili.com/video/BV1j54y1975p

https://www.bilibili.com/video/BV17y4y1E7Gr

入门到精通https://www.bilibili.com/video/BV1K5411H7EQ代码调试部分很棒

贪心学院公开课https://www.bilibili.com/video/BV19U4y1s7cv有两三个（中文）

论文部分

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
 3.17 Graph Matching 3.18 Computer Network

Survey papers

  1. Introduction to Graph Neural Networks. Synthesis Lectures on Artificial Intelligence and Machine Learning, Morgan & Claypool Publishers, 2020. book

    Zhiyuan Liu, Jie Zhou.

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

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

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

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

  4. Adversarial Attack and Defense on Graph Data: A Survey. arxiv 2018. paper

    Lichao Sun, Yingtong Dou, Carl Yang, Ji Wang, Philip S. Yu, Bo Li.

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

    Ziwei Zhang, Peng Cui, Wenwu Zhu.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

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

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

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

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

  12. Benchmarking Graph Neural Networks. arxiv 2020. paper

    Dwivedi, Vijay Prakash and Joshi, Chaitanya K. and Laurent, Thomas and Bengio, Yoshua and Bresson, Xavier.

  13. Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey. arxiv 2020. paper

    Skarding, Joakim and Gabrys, Bogdan and Musial, Katarzyna.

Models

Basic Models

  1. Supervised Neural Networks for the Classification of Structures. IEEE TNN 1997. paper

    Alessandro Sperduti and Antonina Starita.

  2. Graphical-Based Learning Environments for Pattern Recognition. SSPR/SPR 2004. paper

    Franco Scarselli, Ah Chung Tsoi, Marco Gori, Markus Hagenbuchner.

  3. A new model for learning in graph domains. IJCNN 2005. paper

    Marco Gori, Gabriele Monfardini, Franco Scarselli.

  4. Graph Neural Networks for Ranking Web Pages. WI 2005. paper

    Franco Scarselli, Sweah Liang Yong, Marco Gori, Markus Hagenbuchner, Ah Chung Tsoi, Marco Maggini.

  5. Neural Network for Graphs: A Contextual Constructive Approach. IEEE TNN 2009. paper

    Alessio Micheli.

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

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

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

    Mikael Henaff, Joan Bruna, Yann LeCun.

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

    Michaël Defferrard, Xavier Bresson, Pierre Vandergheynst.

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

    James Atwood, Don Towsley.

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

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

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

    Mathias Niepert, Mohamed Ahmed, Konstantin Kutzkov.

  12. Semantic Object Parsing with Graph LSTM. ECCV 2016. paper

    Xiaodan Liang, Xiaohui Shen, Jiashi Feng, Liang Lin, Shuicheng Yan.

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

    Thomas N. Kipf, Max Welling.

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

    William L. Hamilton, Rex Ying, Jure Leskovec.

  15. 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.

  16. Graph Attention Networks. ICLR 2018. paper

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

  17. Covariant Compositional Networks For Learning Graphs. ICLR 2018. paper

    Risi Kondor, Hy Truong Son, Horace Pan, Brandon Anderson, Shubhendu Trivedi.

  18. Graph Partition Neural Networks for Semi-Supervised Classification. ICLR 2018. paper

    Renjie Liao, Marc Brockschmidt, Daniel Tarlow, Alexander L. Gaunt, Raquel Urtasun, Richard Zemel.

  19. 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.

  20. Structure-Aware Convolutional Neural Networks. NeurIPS 2018. paper

    Jianlong Chang, Jie Gu, Lingfeng Wang, Gaofeng Meng, Shiming Xiang, Chunhong Pan.

more

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.

  10. HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs. NeurIPS 2019. paper

    Naganand Yadati, Madhav Nimishakavi, Prateek Yadav, Vikram Nitin, Anand Louis, Partha Talukdar.

  11. Graph Transformer Networks. NeurIPS 2019. paper

    Seongjun Yun, Minbyul Jeong, Raehyun Kim, Jaewoo Kang, Hyunwoo Kim.

  12. Recurrent Space-time Graph Neural Networks. NeurIPS 2019. paper

    Andrei Nicolicioiu, Iulia Duta, Marius Leordeanu.

  13. EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs. AAAI 2020. paper

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

  14. Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting. AAAI 2020. paper

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

  15. Type-aware Anchor Link Prediction across Heterogeneous Networks based on Graph Attention Network. AAAI 2020. paper

    Xiaoxue Li, Yanmin Shang, Yanan Cao, Yangxi Li, Jianlong Tan, Yanbing Liu.

  16. Composition-based Multi-Relational Graph Convolutional Networks. ICLR 2020. paper

    Shikhar Vashishth, Soumya Sanyal, Vikram Nitin, Partha Talukdar.

  17. Inductive representation learning on temporal graphs. ICLR 2020. paper

    da Xu, chuanwei ruan, evren korpeoglu, sushant kumar, kannan achan.

  18. Hyper-SAGNN: a self-attention based graph neural network for hypergraphs. ICLR 2020. paper

    Ruochi Zhang, Yuesong Zou, Jian Ma.

  19. Digraph Inception Convolutional Networks. NeurIPS 2020. paper

    Zekun Tong, Yuxuan Liang, Changsheng Sun, Xinke Li, David S. Rosenblum, Andrew Lim.

  20. Subgraph Neural Networks. NeurIPS 2020. paper

    Emily Alsentzer, Samuel Finlayson, Michelle Li, Marinka Zitnik.

Pooling Methods

  1. An End-to-End Deep Learning Architecture for Graph Classification. AAAI 2018. paper

    Muhan Zhang, Zhicheng Cui, Marion Neumann, Yixin Chen.

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

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

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

    Junhyun Lee, Inyeop Lee, Jaewoo Kang.

  4. Graph U-Nets. ICML 2019. paper

    Hongyang Gao, Shuiwang Ji.

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

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

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

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

  7. Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks. NeurIPS 2019. paper

    Sitao Luan, Mingde Zhao, Xiao-Wen Chang, Doina Precup.

  8. Diffusion Improves Graph Learning. NeurIPS 2019. paper

    Johannes Klicpera, Stefan Weißenberger, Stephan Günnemann.

  9. Hierarchical Graph Pooling with Structure Learning. AAAI 2020. paper

    Zhen Zhang, Jiajun Bu, Martin Ester, Jianfeng Zhang, Chengwei Yao, Zhi Yu, Can Wang.

  10. StructPool: Structured Graph Pooling via Conditional Random Fields. ICLR 2020. paper

    Hao Yuan, Shuiwang Ji.

  11. Spectral Clustering with Graph Neural Networks for Graph Pooling. ICML 2020. paper

    Filippo Maria Bianchi, Daniele Grattarola, Cesare Alippi.

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.

  12. Understanding Attention and Generalization in Graph Neural Networks. NeurIPS 2019. paper

    Boris Knyazev, Graham W. Taylor, Mohamed R. Amer.

  13. GNNExplainer: Generating Explanations for Graph Neural Networks. NeurIPS 2019. paper

    Zhitao Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, Jure Leskovec.

  14. Universal Invariant and Equivariant Graph Neural Networks. NeurIPS 2019. paper

    Nicolas Keriven, Gabriel Peyré.

  15. On the equivalence between graph isomorphism testing and function approximation with GNNs. NeurIPS 2019. paper

    Zhengdao Chen, Soledad Villar, Lei Chen, Joan Bruna.

  16. Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology. NeurIPS 2019. paper

    Nima Dehmamy, Albert-Laszlo Barabasi, Rose Yu.

  17. Graph Neural Networks Exponentially Lose Expressive Power for Node Classification. ICLR 2020. paper

    Kenta Oono, Taiji Suzuki.

  18. What graph neural networks cannot learn: depth vs width. ICLR 2020. paper

    Andreas Loukas.

  19. The Logical Expressiveness of Graph Neural Networks. ICLR 2020. paper

    Pablo Barceló, Egor V. Kostylev, Mikael Monet, Jorge Pérez, Juan Reutter, Juan Pablo Silva.

  20. On the Equivalence between Positional Node Embeddings and Structural Graph Representations. ICLR 2020. paper

    Balasubramaniam Srinivasan, Bruno Ribeiro.

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.

  7. Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks. NeurIPS 2019. paper

    Difan Zou, Ziniu Hu, Yewen Wang, Song Jiang, Yizhou Sun, Quanquan Gu.

  8. GraphSAINT: Graph Sampling Based Inductive Learning Method. ICLR 2020. paper code

    Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, Viktor Prasanna.

  9. Scalable Graph Convolutional Network Based Link Prediction on a Distributed Graph Database Server. IEEE CLOUD 2020. paper code

    Anuradha Karunarathna, Dinika Senarath, Shalika Madhushanki, Chinthaka Weerakkody, Miyuru Dayarathna, Sanath Jayasena, Toyotaro Suzumura.

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.

  10. Physics-aware Difference Graph Networks for Sparsely-Observed Dynamics. ICLR 2020. paper

    Sungyong Seo, Chuizheng Meng, Yan Liu.

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. Spectral Multigraph Networks for Discovering and Fusing Relationships in Molecules. NeurIPS Workshop 2018. paper

    Boris Knyazev, Xiao Lin, Mohamed R. Amer, Graham W. Taylor.

  7. 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.

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

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

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

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

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

    Tianle Ma, Aidong Zhang.

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

    Kien Do, Truyen Tran, Svetha Venkatesh.

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

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

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

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

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

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

  15. Retrosynthesis Prediction with Conditional Graph Logic Network. NeurIPS 2019. paper

    Hanjun Dai, Chengtao Li, Connor Coley, Bo Dai, Le Song.

  16. Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer. AAAI 2020. paper

    Edward Choi, Zhen Xu, Yujia Li, Michael W. Dusenberry, Gerardo Flores, Yuan Xue, Andrew M. Dai.

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.

  12. Multi-relational Poincaré Graph Embeddings. NeurIPS 2019. paper

    Ivana Balazevic, Carl Allen, Timothy Hospedales.

  13. Dynamically Pruned Message Passing Networks for Large-scale Knowledge Graph Reasoning. ICLR 2020. paper

    Xiaoran Xu, Wei Feng, Yunsheng Jiang, Xiaohui Xie, Zhiqing Sun, Zhi-Hong Deng.

  14. Efficient Probabilistic Logic Reasoning with Graph Neural Networks. ICLR 2020. paper

    Yuyu Zhang, Xinshi Chen, Yuan Yang, Arun Ramamurthy, Bo Li, Yuan Qi, Le Song.

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.

  15. Memory Augmented Graph Neural Networks for Sequential Recommendation. AAAI 2020. paper

    Chen Ma, Liheng Ma, Yingxue Zhang, Jianing Sun, Xue Liu, Mark Coates.

  16. Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach. AAAI 2020. paper

    Lei Chen, Le Wu, Richang Hong, Kun Zhang, Meng Wang.

  17. Inductive Matrix Completion Based on Graph Neural Networks. ICLR 2020. paper

    Muhan Zhang, Yixin Chen.

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

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

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

  18. Semi-supervised 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

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.

  9. Efficient Graph Generation with Graph Recurrent Attention Networks. NeurIPS 2019. paper

    Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, Will Hamilton, David Duvenaud, Raquel Urtasun, Richard Zemel.

  10. Graph Normalizing Flows. NeurIPS 2019. paper

    Jenny Liu, Aviral Kumar, Jimmy Ba, Jamie Kiros, Kevin Swersky.

  11. Conditional Structure Generation through Graph Variational Generative Adversarial Nets. NeurIPS 2019. paper

    Carl Yang, Peiye Zhuang, Wenhan Shi, Alan Luu, Pan Li.

  12. GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation. ICLR 2020. paper

    Chence Shi, Minkai Xu, Zhaocheng Zhu, Weinan Zhang, Ming Zhang, Jian Tang.

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. Exact Combinatorial Optimization with Graph Convolutional Neural Networks. NeurIPS 2019. paper

    Maxime Gasse, Didier Chetelat, Nicola Ferroni, Laurent Charlin, Andrea Lodi.

  8. Approximation Ratios of Graph Neural Networks for Combinatorial Problems. NeurIPS 2019. paper

    Ryoma Sato, Makoto Yamada, Hisashi Kashima.

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.

  10. Certifiable Robustness to Graph Perturbations. NeurIPS 2019. paper

    Aleksandar Bojchevski, Stephan Günnemann.

  11. A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning. NeurIPS 2019. paper

    Xuanqing Liu, Si Si, Jerry Zhu, Yang Li, Cho-Jui Hsieh.

  12. GNNGuard: Defending Graph Neural Networks against Adversarial Attacks. NeurIPS 2020. paper

    Xiang Zhang, Marinka Zitnik.

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.

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,

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