【清华NLP】图神经网络GNN论文分门别类，16大应用200+篇论文最新推荐

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

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

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

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

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

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

   Ziwei Zhang, Peng Cui, Wenwu Zhu.

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

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

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

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

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

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

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

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

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

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

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

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

模型

基本模型

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

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

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

   Marco Gori, Gabriele Monfardini, Franco Scarselli.

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

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

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

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

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

   Mikael Henaff, Joan Bruna, Yann LeCun.

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

   Michaël Defferrard, Xavier Bresson, Pierre Vandergheynst.

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

   James Atwood, Don Towsley.

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

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

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

   Mathias Niepert, Mohamed Ahmed, Konstantin Kutzkov.

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

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

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

    Thomas N. Kipf, Max Welling.

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

    William L. Hamilton, Rex Ying, Jure Leskovec.

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

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

14. Graph Attention Networks. ICLR 2018. paper

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

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

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

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

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

17. Inference in Probabilistic Graphical Models by Graph Neural Networks. ICLR Workshop 2018. paper

    KiJung Yoon, Renjie Liao, Yuwen Xiong, Lisa Zhang, Ethan Fetaya, Raquel Urtasun, Richard Zemel, Xaq Pitkow.

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

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

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

    Yin Cheng Ng, Nicolò Colombo, Ricardo Silva.

20. Adaptive Graph Convolutional Neural Networks. AAAI 2018. paper

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

图类型

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

   Liyu Gong, Qiang Cheng.

池化方法

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

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

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

   Junhyun Lee, Inyeop Lee, Jaewoo Kang.

3. Graph U-Nets. ICML 2019. paper

   Hongyang Gao, Shuiwang Ji.

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

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

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

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

分析

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.

效率

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.

应用

物理

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.

化学生物

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

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

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

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

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

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

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

   Sungmin Rhee, Seokjun Seo, Sun Kim.

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

   Marinka Zitnik, Monica Agrawal, Jure Leskovec.

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

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

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

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

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

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

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

   Tianle Ma, Aidong Zhang.

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

    Kien Do, Truyen Tran, Svetha Venkatesh.

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

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

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

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

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

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

知识图谱

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.

推荐系统

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

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

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

   Jin Shang, Mingxuan Sun.

8. 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.

9. 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.

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

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

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

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

12. 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.

13. 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

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.

自然语言处理

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.

生成

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.

组合优化

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.

对抗攻击

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

   Daniel Zügner, Amir Akbarnejad, Stephan Günnemann.

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

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

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

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

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

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

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

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

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

   Daniel Zügner, Stephan Günnemann.

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

   Aleksandar Bojchevski, Stephan Günnemann.

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

   Daniel Zügner, Stephan Günnemann.

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

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

Graph Clustering 图聚类

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 图分类

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

   Davide Bacciu, Federico Errica, Alessio Micheli.

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

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

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

   Rami Al-Rfou, Dustin Zelle, Bryan Perozzi.

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

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

Reinforcement Learning 强化学习

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

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

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

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

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

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

4. Relational Deep Reinforcement Learning. arxiv 2018. paper

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

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

   Prithviraj Ammanabrolu, Mark O. Riedl.

Traffic Network 交通网络

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

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

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

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

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

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

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

   Bing Yu, Haoteng Yin, Zhanxing Zhu.

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

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

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

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

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

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

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

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

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

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

Few-shot and Zero-shot Learning 小样本学习

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

   Victor Garcia, Joan Bruna.

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

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

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

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

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

   Spyros Gidaris, Nikos Komodakis.

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

   Xiaolong Wang, Yufei Ye, Abhinav Gupta.

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

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

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

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

Reinforcement Learning 强化学习

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

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

2. Relational Deep Reinforcement Learning. arxiv 2018. paper

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

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

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

Program Representation 编程表示

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

   Miltiadis Allamanis, Marc Brockschmidt, Mahmoud Khademi.

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

   Milan Cvitkovic, Badal Singh, Anima Anandkumar.

Social Network 社交网络

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

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

2. 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.

3. 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.

4. 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.

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

   Chang Li, Dan Goldwasser.

6. 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.

-END-

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