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清华NLP图神经网络GNN论文分门别类，16大应用200+篇论文

本文总结了清华大学NLP课题组Jie Zhou, Ganqu Cui, Zhengyan Zhang and Yushi Bai同学对 GNN 相关的综述论文、模型与应用。

[ 导读 ]图神经网络研究成为当前深度学习领域的热点。最近，清华大学NLP课题组Jie Zhou, Ganqu Cui, Zhengyan Zhang and Yushi Bai同学对 GNN 相关的综述论文、模型与应用进行了综述，并发布在 GitHub 上。16大应用包含物理、知识图谱等最新论文整理推荐。

GitHub 链接：

https://github.com/thunlp/GNNPapers

目录

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

综述论文

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

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

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

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

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

Ziwei Zhang, Peng Cui, Wenwu Zhu.

  • 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.

  • 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.

  • 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.

  • 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.

  • Non-local Neural Networks. CVPR 2018. paper

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

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

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

模型

基本模型

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

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

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

Marco Gori, Gabriele Monfardini, Franco Scarselli.

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

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

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

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

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

Mikael Henaff, Joan Bruna, Yann LeCun.

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

Michaël Defferrard, Xavier Bresson, Pierre Vandergheynst.

  • Diffusion-Convolutional Neural Networks. NIPS 2016. paper

James Atwood, Don Towsley.

  • Gated Graph Sequence Neural Networks. ICLR 2016. paper

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

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

Mathias Niepert, Mohamed Ahmed, Konstantin Kutzkov.

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

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

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

Thomas N. Kipf, Max Welling.

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

William L. Hamilton, Rex Ying, Jure Leskovec.

  • 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.

  • Graph Attention Networks. ICLR 2018. paper

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

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

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

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

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

  • 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.

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

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

  • Bayesian Semi-supervised Learning with Graph Gaussian Processes. NeurIPS 2018. paper

Yin Cheng Ng, Nicolò Colombo, Ricardo Silva.

  • Adaptive Graph Convolutional Neural Networks. AAAI 2018. paper

Ruoyu Li, Sheng Wang, Feiyun Zhu, Junzhou Huang.

图类型

  • DyRep: Learning Representations over Dynamic Graphs. ICLR 2019. paper

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

  • Hypergraph Neural Networks. AAAI 2019. paper

Yifan Feng, Haoxuan You, Zizhao Zhang, Rongrong Ji, Yue Gao.

  • Heterogeneous Graph Attention Network. WWW 2019. paper

Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Peng Cui, P. Yu, Yanfang Ye.

  • Representation Learning for Attributed Multiplex Heterogeneous Network. KDD 2019. paper

Yukuo Cen, Xu Zou, Jianwei Zhang, Hongxia Yang, Jingren Zhou, Jie Tang.

  • ActiveHNE: Active Heterogeneous Network Embedding. IJCAI 2019. paper

Xia Chen, Guoxian Yu, Jun Wang, Carlotta Domeniconi, Zhao Li, Xiangliang Zhang.

  • GCN-LASE: Towards Adequately Incorporating Link Attributes in Graph Convolutional Networks. IJCAI 2019. paper

Ziyao Li, Liang Zhang, Guojie Song.

  • Exploiting Edge Features in Graph Neural Networks. CVPR 2019. paper

Liyu Gong, Qiang Cheng.

池化方法

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

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

  • Self-Attention Graph Pooling. ICML 2019. paper

Junhyun Lee, Inyeop Lee, Jaewoo Kang.

  • Graph U-Nets. ICML 2019. paper

Hongyang Gao, Shuiwang Ji.

  • Graph Convolutional Networks with EigenPooling. KDD 2019. paper

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

  • Relational Pooling for Graph Representations. ICML 2019. paper

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

分析

  • 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.

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

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

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

Tatsuro Kawamoto, Masashi Tsubaki, Tomoyuki Obuchi.

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

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

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

Qimai Li, Zhichao Han, Xiao-Ming Wu.

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

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

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

Saurabh Verma, Zhi-Li Zhang.

  • Simplifying Graph Convolutional Networks. ICML 2019. paper

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

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

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

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

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

  • 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.

效率

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

Jianfei Chen, Jun Zhu, Le Song.

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

Jie Chen, Tengfei Ma, Cao Xiao.

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

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

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

Hongyang Gao, Zhengyang Wang, Shuiwang Ji.

  • 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.

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

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

应用

物理

  • 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.

  • 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.

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

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

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

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

  • 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.

  • Learning Multiagent Communication with Backpropagation. NIPS 2016. paper

Sainbayar Sukhbaatar, Arthur Szlam, Rob Fergus.

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

Yedid Hoshen.

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

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

  • 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.

化学生物

  • 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.

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

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

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

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

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

Sungmin Rhee, Seokjun Seo, Sun Kim.

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

Marinka Zitnik, Monica Agrawal, Jure Leskovec.

  • 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.

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

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

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

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

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

Tianle Ma, Aidong Zhang.

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

Kien Do, Truyen Tran, Svetha Venkatesh.

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

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

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

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

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

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

知识图谱

  • 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.

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

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

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

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

  • 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.

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

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

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

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

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

Haoyu Wang, Defu Lian, Yong Ge.

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

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

  • 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.

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

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

  • 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.

推荐系统

  • 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.

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

Federico Monti, Michael M. Bronstein, Xavier Bresson.

  • Graph Convolutional Matrix Completion. 2017. paper

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

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

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

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

Haoyu Wang, Defu Lian, Yong Ge.

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

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

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

Jin Shang, Mingxuan Sun.

  • 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.

  • 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.

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

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

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

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

  • 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.

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

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

计算机视觉

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

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

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

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

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

Will Norcliffe-Brown, Efstathios Vafeias, Sarah Parisot.

  • Symbolic Graph Reasoning Meets Convolutions. NeurIPS 2018. paper

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

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

Medhini Narasimhan, Svetlana Lazebnik, Alexander Schwing.

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

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

  • Relation Networks for Object Detection. CVPR 2018. paper

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

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

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

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

Kenneth Marino, Ruslan Salakhutdinov, Abhinav Gupta.

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

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

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

Damien Teney, Lingqiao Liu, Anton van den Hengel.

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

Sijie Yan, Yuanjun Xiong, Dahua Lin.

  • 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.

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

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

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

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

  • Iterative Visual Reasoning Beyond Convolutions. CVPR 2018. paper

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

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

Martin Simonovsky, Nikos Komodakis.

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

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

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

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

  • 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.

自然语言处理

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

Vicky Zayats, Mari Ostendorf.

  • Learning Graphical State Transitions. ICLR 2017. paper

Daniel D. Johnson.

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

Xiao Liu, Zhunchen Luo, Heyan Huang.

  • Recurrent Relational Networks. NeurIPS 2018. paper

Rasmus Palm, Ulrich Paquet, Ole Winther.

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

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

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

Diego Marcheggiani, Ivan Titov.

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

Thien Huu Nguyen, Ralph Grishman.

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

Diego Marcheggiani, Joost Bastings, Ivan Titov.

  • 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.

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

Yuhao Zhang, Peng Qi, Christopher D. Manning.

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

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

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

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

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

Daniel Beck, Gholamreza Haffari, Trevor Cohn.

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

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

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

Yue Zhang, Qi Liu, Linfeng Song.

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

Makoto Miwa, Mohit Bansal.

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

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

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

Afshin Rahimi, Trevor Cohn, Timothy Baldwin.

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

Daniil Sorokin, Iryna Gurevych.

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

Liang Yao, Chengsheng Mao, Yuan Luo.

生成

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

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

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

Tengfei Ma, Jie Chen, Cao Xiao.

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

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

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

Nicola De Cao, Thomas Kipf.

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

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

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

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

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

  • Aditya Grover, Aaron Zweig, Stefano Ermon.Generative Code Modeling with Graphs. ICLR 2019. paper

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

组合优化

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

Zhuwen Li, Qifeng Chen, Vladlen Koltun.

  • 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.

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

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

  • Attention Solves Your TSP, Approximately. 2018. paper

Wouter Kool, Herke van Hoof, Max Welling.

  • 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.

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

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

对抗攻击

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

Daniel Zügner, Amir Akbarnejad, Stephan Günnemann.

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

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

  • 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.

  • 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.

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

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

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

Daniel Zügner, Stephan Günnemann.

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

Aleksandar Bojchevski, Stephan Günnemann.

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

Daniel Zügner, Stephan Günnemann.

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

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

Graph Clustering 图聚类

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

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

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

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

Graph Classification 图分类

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

Davide Bacciu, Federico Errica, Alessio Micheli.

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

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

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

Rami Al-Rfou, Dustin Zelle, Bryan Perozzi.

  • 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.

Reinforcement Learning 强化学习

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

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

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

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

  • 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.

  • 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.

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

Prithviraj Ammanabrolu, Mark O. Riedl.

Traffic Network 交通网络

  • 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.

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

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

  • 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.

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

Bing Yu, Haoteng Yin, Zhanxing Zhu.

  • 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.

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

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

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

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

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.

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

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

Few-shot and Zero-shot Learning 小样本学习

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

Victor Garcia, Joan Bruna.

  • 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.

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

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

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

Spyros Gidaris, Nikos Komodakis.

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

Xiaolong Wang, Yufei Ye, Abhinav Gupta.

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

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

  • 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 强化学习

  • 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.

  • 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.

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

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

Program Representation 编程表示

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

Miltiadis Allamanis, Marc Brockschmidt, Mahmoud Khademi.

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

Milan Cvitkovic, Badal Singh, Anima Anandkumar

Social Network 社交网络

  • DeepInf: Social Influence Prediction with Deep Learning. KDD 2018. paper

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

  • Characterizing and Forecasting User Engagement with In-app Action Graph: A Case Study of Snapchat. KDD 2019. paper

Yozen Liu, Xiaolin Shi, Lucas Pierce, Xiang Ren.

  • MCNE: An End-to-End Framework for Learning Multiple Conditional Network Representations of Social Network.KDD 2019. paper

Hao Wang, Tong Xu, Qi Liu, Defu Lian, Enhong Chen, Dongfang Du, Han Wu, Wen Su.

  • Is a Single Vector Enough? Exploring Node Polysemy for Network Embedding. KDD 2019. paper

Ninghao Liu, Qiaoyu Tan, Yuening Li, Hongxia Yang, Jingren Zhou, Xia Hu.

  • Encoding Social Information with Graph Convolutional Networks for Political Perspective Detection in News Media.ACL 2019. paper

Chang Li, Dan Goldwasser.

  • Fine-grained Event Categorization with Heterogeneous Graph Convolutional Networks. IJCAI 2019. paper

Hao Peng, Jianxin Li, Qiran Gong, Yangqiu Song, Yuanxing Ning, Kunfeng Lai, Philip S. Yu.

THU数据派
THU数据派

THU数据派"基于清华，放眼世界"，以扎实的理工功底闯荡“数据江湖”。发布全球大数据资讯，定期组织线下活动，分享前沿产业动态。了解清华大数据，敬请关注姐妹号“数据派THU”。

理论清华大学NLP图神经网络GNN论文
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Yan Wang人物

纽约州立大学宾汉姆顿分校计算机科学系副教授，研究兴趣：移动计算、智能医疗、嵌入式系统和无线网络。曾获得CNS 2018最佳论文奖。

来源：个人主页Yan Wang
深度学习技术

深度学习（deep learning）是机器学习的分支，是一种试图使用包含复杂结构或由多重非线性变换构成的多个处理层对数据进行高层抽象的算法。 深度学习是机器学习中一种基于对数据进行表征学习的算法，至今已有数种深度学习框架，如卷积神经网络和深度置信网络和递归神经网络等已被应用在计算机视觉、语音识别、自然语言处理、音频识别与生物信息学等领域并获取了极好的效果。

来源：LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436.
池化技术

池化（Pooling）是卷积神经网络中的一个重要的概念，它实际上是一种形式的降采样。有多种不同形式的非线性池化函数，而其中“最大池化（Max pooling）”是最为常见的。它是将输入的图像划分为若干个矩形区域，对每个子区域输出最大值。直觉上，这种机制能够有效的原因在于，在发现一个特征之后，它的精确位置远不及它和其他特征的相对位置的关系重要。池化层会不断地减小数据的空间大小，因此参数的数量和计算量也会下降，这在一定程度上也控制了过拟合。通常来说，CNN的卷积层之间都会周期性地插入池化层。

来源：cs231n
计算机视觉技术

计算机视觉（CV）是指机器感知环境的能力。这一技术类别中的经典任务有图像形成、图像处理、图像提取和图像的三维推理。目标识别和面部识别也是很重要的研究领域。

来源：机器之心
知识图谱技术

知识图谱本质上是语义网络，是一种基于图的数据结构，由节点(Point)和边(Edge)组成。在知识图谱里，每个节点表示现实世界中存在的“实体”，每条边为实体与实体之间的“关系”。知识图谱是关系的最有效的表示方式。通俗地讲，知识图谱就是把所有不同种类的信息（Heterogeneous Information）连接在一起而得到的一个关系网络。知识图谱提供了从“关系”的角度去分析问题的能力。 知识图谱这个概念最早由Google提出，主要是用来优化现有的搜索引擎。不同于基于关键词搜索的传统搜索引擎，知识图谱可用来更好地查询复杂的关联信息，从语义层面理解用户意图，改进搜索质量。比如在Google的搜索框里输入Bill Gates的时候，搜索结果页面的右侧还会出现Bill Gates相关的信息比如出生年月，家庭情况等等。

来源：机器之心
推荐系统技术

推荐系统（RS）主要是指应用协同智能（collaborative intelligence）做推荐的技术。推荐系统的两大主流类型是基于内容的推荐系统和协同过滤（Collaborative Filtering）。另外还有基于知识的推荐系统（包括基于本体和基于案例的推荐系统）是一类特殊的推荐系统，这类系统更加注重知识表征和推理。

来源：机器之心
图神经网络技术

图网络即可以在社交网络或其它基于图形数据上运行的一般深度学习架构，它是一种基于图结构的广义神经网络。图网络一般是将底层图形作为计算图，并通过在整张图上传递、转换和聚合节点特征信息，从而学习神经网络基元以生成单节点嵌入向量。生成的节点嵌入向量可作为任何可微预测层的输入，并用于节点分类或预测节点之间的连接，完整的模型可以通过端到端的方式训练。

来源：机器之心
自然语言处理技术

自然语言处理（英语：natural language processing，缩写作 NLP）是人工智能和语言学领域的分支学科。此领域探讨如何处理及运用自然语言；自然语言认知则是指让电脑“懂”人类的语言。自然语言生成系统把计算机数据转化为自然语言。自然语言理解系统把自然语言转化为计算机程序更易于处理的形式。

来源：维基百科
强化学习技术

强化学习是一种试错方法，其目标是让软件智能体在特定环境中能够采取回报最大化的行为。强化学习在马尔可夫决策过程环境中主要使用的技术是动态规划（Dynamic Programming）。流行的强化学习方法包括自适应动态规划（ADP）、时间差分（TD）学习、状态-动作-回报-状态-动作（SARSA）算法、Q 学习、深度强化学习（DQN）；其应用包括下棋类游戏、机器人控制和工作调度等。

来源：机器之心
聚类技术

将物理或抽象对象的集合分成由类似的对象组成的多个类的过程被称为聚类。由聚类所生成的簇是一组数据对象的集合，这些对象与同一个簇中的对象彼此相似，与其他簇中的对象相异。“物以类聚，人以群分”，在自然科学和社会科学中，存在着大量的分类问题。聚类分析又称群分析，它是研究（样品或指标）分类问题的一种统计分析方法。聚类分析起源于分类学，但是聚类不等于分类。聚类与分类的不同在于，聚类所要求划分的类是未知的。聚类分析内容非常丰富，有系统聚类法、有序样品聚类法、动态聚类法、模糊聚类法、图论聚类法、聚类预报法等。

来源：百度百科
Tieniu Tan人物

谭铁牛， 英国帝国理工学院电子电气工程系图像处理专业博士研究生，中国科学院院士、英国皇家工程院外籍院士、发展中国家科学院院士和巴西科学院通讯院士。现任中央政府驻港联络办副主任、中国科学院自动化研究所研究员、博士生导师、智能感知与计算研究中心主任。

来源：Tieniu Tan
Xiaodong He人物

现任京东AI研究院常务副院长，深度学习和语音及语言实验室主任。何晓冬博士本科毕业于清华大学，并先后在中国科学院及美国密苏里大学-哥伦比亚分校获得硕士学位及博士学位。加入京东之前，何晓冬博士曾任职于美国微软雷德蒙德研究院，任主任研究员（PrincipalResearcher）及深度学习技术中心负责人(ResearchManager)，其工作包括深度结构化语义模型（DSSM），层次化注意力模型（HAN），看图说话机器人CaptionBot，智能绘画机器人DrawingBot等，研究成果对微软产品如Office、SeeingAI、搜索及广告、智能云服务、微软小冰等有着重要价值。同时，何博士还在华盛顿大学电子与计算机工程系兼任教授、博士生导师。

来源：Xiaodong He
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