清華NLP圖神經網路GNN論文分門別類，16大應用200+篇論文

THU資料派發表於2019-08-12
神經網路GNN
[ 導讀 ]圖神經網路研究成為當前深度學習領域的熱點。最近，清華大學NLP課題組Jie Zhou, Ganqu Cui, Zhengyan Zhang and Yushi Bai同學對 GNN 相關的綜述論文、模型與應用進行了綜述，併發布在 GitHub 上。16大應用包含物理、知識圖譜等最新論文整理推薦。

GitHub 連結：

https://github.com/thunlp/GNNPapers
清華NLP圖神經網路GNN論文分門別類，16大應用200+篇論文

目錄

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

綜述論文

  • 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.

圖型別

  • 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.

應用

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.

化學生物

  • 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.

知識圖譜

  • 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.

推薦系統

  • 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

計算機視覺

  • 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.

自然語言處理

  • 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.

組合最佳化

  • 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.

對抗攻擊

  • 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 圖聚類

  • 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 圖分類

  • 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 強化學習

  • 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 交通網路

  • 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 小樣本學習

  • 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 強化學習

  • 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 程式設計表示

  • 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 社交網路

  • 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.

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