paper graph gnn

Top paper based on graph neural network

Posted by Hao Do on September 23, 2023

Top paper based on graph neural network

Survey papers

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

    Zhiyuan Liu, Jie Zhou.

  2. Graph Neural Networks: A Review of Methods and Applications. AI Open 2020. paper

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

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

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

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

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

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

    Ziwei Zhang, Peng Cui, Wenwu Zhu.

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

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

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

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

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

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

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

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

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

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

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

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

  12. Benchmarking Graph Neural Networks. arxiv 2020. paper

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

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

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

  14. Bridging the Gap between Spatial and Spectral Domains: A Survey on Graph Neural Networks. arxiv 2020. paper

    Zhiqian Chen, Fanglan Chen, Lei Zhang, Taoran Ji, Kaiqun Fu, Liang Zhao, Feng Chen, Chang-Tien Lu.

  15. Explainability in Graph Neural Networks: A Taxonomic Survey. arxiv 2020. paper

    Hao Yuan, Haiyang Yu, Shurui Gui, Shuiwang Ji.

  16. Self-Supervised Learning of Graph Neural Networks: A unified view. TPAMI 2022. paper

    Yaochen Xie, Zhao Xu, Jingtun Zhang, Zhangyang Wang, Shuiwang Ji.

Models

Basic Models

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

    Alessandro Sperduti and Antonina Starita.

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

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

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

    Marco Gori, Gabriele Monfardini, Franco Scarselli.

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

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

  5. Automatic Generation of Complementary Descriptors with Molecular Graph Networks. J.Chem.Inf.Model. 2005. paper

    Christian Merkwirth and Thomas Lengauer.

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

    Alessio Micheli.

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

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

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

    Mikael Henaff, Joan Bruna, Yann LeCun.

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

    Michaël Defferrard, Xavier Bresson, Pierre Vandergheynst.

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

    James Atwood, Don Towsley.

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

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

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

    Mathias Niepert, Mohamed Ahmed, Konstantin Kutzkov.

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

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

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

    Thomas N. Kipf, Max Welling.

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

    William L. Hamilton, Rex Ying, Jure Leskovec.

  16. 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.

  17. Graph Attention Networks. ICLR 2018. paper

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

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

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

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

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

  20. 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.

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

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

more 21. **Bayesian Semi-supervised Learning with Graph Gaussian Processes.** NeurIPS 2018. [paper](https://arxiv.org/pdf/1809.04379) *Yin Cheng Ng, Nicolò Colombo, Ricardo Silva.* 22. **Adaptive Graph Convolutional Neural Networks.** AAAI 2018. [paper](https://arxiv.org/pdf/1801.03226.pdf) *Ruoyu Li, Sheng Wang, Feiyun Zhu, Junzhou Huang.* 1. **Dual Graph Convolutional Networks for Graph-Based Semi-Supervised Classification.** WWW 2018. [paper](http://delivery.acm.org/10.1145/3190000/3186116/p499-zhuang.pdf?ip=123.134.247.159&id=3186116&acc=OPEN&key=4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E6D218144511F3437&__acm__=1564108433_6b20d1fe2ab710632bc8434ad3a00bc8) *Chenyi Zhuang, Qiang Ma.* 1. **Learning Steady-States of Iterative Algorithms over Graphs.** ICML 2018. [paper](http://proceedings.mlr.press/v80/dai18a/dai18a.pdf) *Hanjun Dai, Zornitsa Kozareva, Bo Dai, Alex Smola, Le Song.* 1. **Graph Capsule Convolutional Neural Networks.** ICML 2018 Workshop. [paper](https://arxiv.org/pdf/1805.08090.pdf) *Saurabh Verma, Zhi-Li Zhang.* 1. **Capsule Graph Neural Network.** ICLR 2019. [paper](https://openreview.net/pdf?id=Byl8BnRcYm) *Zhang Xinyi, Lihui Chen.* 1. **Graph Wavelet Neural Network.** ICLR 2019. [paper](https://openreview.net/pdf?id=H1ewdiR5tQ) *Bingbing Xu, Huawei Shen, Qi Cao, Yunqi Qiu, Xueqi Cheng.* 1. **Deep Graph Infomax.** ICLR 2019. [paper](https://openreview.net/pdf?id=rklz9iAcKQ) *Petar Veličković, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, R Devon Hjelm.* 1. **Predict then Propagate: Graph Neural Networks meet Personalized PageRank.** ICLR 2019. [paper](https://openreview.net/pdf?id=H1gL-2A9Ym) *Johannes Klicpera, Aleksandar Bojchevski, Stephan Günnemann.* 1. **LanczosNet: Multi-Scale Deep Graph Convolutional Networks.** ICLR 2019. [paper](https://openreview.net/pdf?id=BkedznAqKQ) *Renjie Liao, Zhizhen Zhao, Raquel Urtasun, Richard Zemel.* 1. **Invariant and Equivariant Graph Networks.** ICLR 2019. [paper](https://openreview.net/pdf?id=Syx72jC9tm) *Haggai Maron, Heli Ben-Hamu, Nadav Shamir, Yaron Lipman.* 1. **GMNN: Graph Markov Neural Networks.** ICML 2019. [paper](https://arxiv.org/pdf/1905.06214) *Meng Qu, Yoshua Bengio, Jian Tang.* 1. **Position-aware Graph Neural Networks.** ICML 2019. [paper](https://arxiv.org/pdf/1906.04817) *Jiaxuan You, Rex Ying, Jure Leskovec.* 1. **Disentangled Graph Convolutional Networks.** ICML 2019. [paper](http://proceedings.mlr.press/v97/ma19a/ma19a.pdf) *Jianxin Ma, Peng Cui, Kun Kuang, Xin Wang, Wenwu Zhu.* 1. **Stochastic Blockmodels meet Graph Neural Networks.** ICML 2019. [paper](https://arxiv.org/pdf/1905.05738) *Nikhil Mehta, Lawrence Carin, Piyush Rai.* 1. **Learning Discrete Structures for Graph Neural Networks.** ICML 2019. [paper](https://arxiv.org/pdf/1903.11960) *Luca Franceschi, Mathias Niepert, Massimiliano Pontil, Xiao He.* 1. **MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing.** ICML 2019. [paper](https://arxiv.org/pdf/1905.00067) *Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, Aram Galstyan.* 1. **DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification.** KDD 2019. [paper](https://arxiv.org/pdf/1906.02319.pdf) *Jun Wu, Jingrui He, Jiejun Xu.* 1. **Graph Representation Learning via Hard and Channel-Wise Attention Networks.** KDD 2019. [paper](https://arxiv.org/pdf/1907.04652) *Hongyang Gao, Shuiwang Ji.* 1. **Graph Learning-Convolutional Networks.** CVPR 2019. [paper](https://arxiv.org/pdf/1811.09971.pdf) *Bo Jiang, Ziyan Zhang, Doudou Lin, Jin Tang.* 1. **Data Representation and Learning with Graph Diffusion-Embedding Networks.** CVPR 2019. [paper](http://openaccess.thecvf.com/content_CVPR_2019/papers/Jiang_Data_Representation_and_Learning_With_Graph_Diffusion-Embedding_Networks_CVPR_2019_paper.pdf) *Bo Jiang, Doudou Lin, Jin Tang, Bin Luo.* 1. **Label Efficient Semi-Supervised Learning via Graph Filtering.** CVPR 2019. [paper](https://arxiv.org/pdf/1901.09993.pdf) *Qimai Li, Xiao-Ming Wu, Han Liu, Xiaotong Zhang, Zhichao Guan.* 1. **SPAGAN: Shortest Path Graph Attention Network.** IJCAI 2019. [paper](https://cse.buffalo.edu/~jsyuan/papers/2019/SPAGAN_Shortest_Path_Graph_Attention_Network.pdf) *Yiding Yang, Xinchao Wang, Mingli Song, Junsong Yuan, Dacheng Tao.* 1. **Topology Optimization based Graph Convolutional Network.** IJCAI 2019. [paper](https://yangliang.github.io/pdf/ijcai19_to.pdf) *Liang Yang, Zesheng Kang, Xiaochun Cao, Di Jin, Bo Yang, Yuanfang Guo.* 1. **Hierarchical Graph Convolutional Networks for Semi-supervised Node Classification.** IJCAI 2019. [paper](https://arxiv.org/pdf/1902.06667.pdf) *Fenyu Hu, Yanqiao Zhu, Shu Wu, Liang Wang, Tieniu Tan.* 1. **Masked Graph Convolutional Network.** IJCAI 2019. [paper](https://yangliang.github.io/pdf/ijcai19_mask.pdf) *Liang Yang, Fan Wu, Yingkui Wang, Junhua Gu, Yuanfang Guo.* 1. **Dual Self-Paced Graph Convolutional Network: Towards Reducing Attribute Distortions Induced by Topology.** IJCAI 2019. [paper](https://yangliang.github.io/pdf/ijcai19_paced.pdf) *Liang Yang, Zhiyang Chen, Junhua Gu, Yuanfang Guo.* 1. **Bayesian graph convolutional neural networks for semi-supervised classification.** AAAI 2019. [paper](https://arxiv.org/pdf/1811.11103.pdf) *Yingxue Zhang, Soumyasundar Pal, Mark Coates, Deniz Üstebay.* 1. **GeniePath: Graph Neural Networks with Adaptive Receptive Paths.** AAAI 2019. [paper](https://arxiv.org/pdf/1802.00910.pdf) *Ziqi Liu, Chaochao Chen, Longfei Li, Jun Zhou, Xiaolong Li, Le Song, Yuan Qi.* 1. **Gaussian-Induced Convolution for Graphs.** AAAI 2019. [paper](https://arxiv.org/pdf/1811.04393.pdf) *Jiatao Jiang, Zhen Cui, Chunyan Xu, Jian Yang.* 1. **Fisher-Bures Adversary Graph Convolutional Networks.** UAI 2019. [paper](https://arxiv.org/pdf/1903.04154.pdf) *Ke Sun, Piotr Koniusz, Zhen Wang.* 1. **N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification.** UAI 2019. [paper](https://arxiv.org/pdf/1802.08888.pdf) *Sami Abu-El-Haija, Amol Kapoor, Bryan Perozzi, Joonseok Lee.* 1. **Confidence-based Graph Convolutional Networks for Semi-Supervised Learning.** AISTATS 2019. [paper](https://arxiv.org/pdf/1901.08255.pdf) *Shikhar Vashishth, Prateek Yadav, Manik Bhandari, Partha Talukdar.* 1. **Lovasz Convolutional Networks.** AISTATS 2019. [paper](https://arxiv.org/pdf/1805.11365.pdf) *Prateek Yadav, Madhav Nimishakavi, Naganand Yadati, Shikhar Vashishth, Arun Rajkumar, Partha Talukdar.* 1. **Provably Powerful Graph Networks.** NeurIPS 2019. [paper](http://papers.nips.cc/paper/by-source-2019-1275) *Haggai Maron, Heli Ben-Hamu, Hadar Serviansky, Yaron Lipman.* 1. **Graph Agreement Models for Semi-Supervised Learning.** NeurIPS 2019. [paper](http://papers.nips.cc/paper/by-source-2019-4699) *Otilia Stretcu, Krishnamurthy Viswanathan, Dana Movshovitz-Attias, Emmanouil Platanios. Sujith Ravi, Andrew Tomkins.* 1. **Graph-Based Semi-Supervised Learning with Non-ignorable Non-response.** NeurIPS 2019. [paper](http://papers.nips.cc/paper/by-source-2019-3795) *Fan Zhou, Tengfei Li, Haibo Zhou, Hongtu Zhu, Ye Jieping.* 1. **A Flexible Generative Framework for Graph-based Semi-supervised Learning.** NeurIPS 2019. [paper](http://papers.nips.cc/paper/by-source-2019-1831) *Jiaqi Ma, Weijing Tang, Ji Zhu, Qiaozhu Mei.* 1. **Semi-Implicit Graph Variational Auto-Encoders.** NeurIPS 2019. [paper](http://papers.nips.cc/paper/by-source-2019-5716) *Arman Hasanzadeh, Ehsan Hajiramezanali, Krishna Narayanan, Nick Duffield, Mingyuan Zhou, Xiaoning Qian.* 1. **Hyperbolic Graph Neural Networks.** NeurIPS 2019. [paper](http://papers.nips.cc/paper/by-source-2019-4472) *Qi Liu, Maximilian Nickel, Douwe Kiela.* 1. **Hyperbolic Graph Convolutional Neural Networks.** NeurIPS 2019. [paper](http://papers.nips.cc/paper/by-source-2019-2699) *Ines Chami, Zhitao Ying, Christopher Ré, Jure Leskovec.* 1. **Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels.** NeurIPS 2019. [paper](http://papers.nips.cc/paper/by-source-2019-3077) *Simon Du, Kangcheng Hou, Russ Salakhutdinov, Barnabas Poczos, Ruosong Wang, Keyulu Xu.* 1. **SNEQ: Semi-supervised Attributed Network Embedding with Attention-based Quantisation.** AAAI 2020. [paper](http://users.monash.edu/~yli/assets/pdf/sneq-aaai-20.pdf) *Tao He, Lianli Gao, Jingkuan Song, Xin Wang, Kejie Huang, Yuan-­‐Fang Li.* 1. **Going Deep: Graph Convolutional Ladder-Shape Networks.** AAAI 2020. [paper](https://shiruipan.github.io/publication/aaai-2020-hu/) *Ruiqi Hu, Shirui Pan, Guodong Long, Qinghua Lu, Liming Zhu, Jing Jiang.* 1. **Co-GCN for Multi-View Semi-Supervised Learning.** AAAI 2020. [paper]() *Shu Li, Wen-­‐Tao Li, Wei Wang.* 1. **Graph Representation Learning via Ladder Gamma Variational Autoencoders.** AAAI 2020. [paper]() *Arindam Sarkar, Nikhil Mehta, Piyush Rai.* 1. **GSSNN: Graph Smoothing Splines Neural Networks.** AAAI 2020. [paper](https://shiruipan.github.io/publication/aaai-2020-zhu/) *Shichao Zhu, Lewei Zhou, Shirui Pan, Chuan Zhou, Guiying Yan, Bin Wang.* 1. **Effective Decoding in Graph Auto-Encoder using Triadic Closure.** AAAI 2020. [paper](https://arxiv.org/abs/1911.11322) *Han Shi, Haozheng Fan, James T. Kwok.* 1. **An Attention-based Graph Neural Network for Heterogeneous Structural Learning.** AAAI 2020. [paper](https://arxiv.org/abs/1912.10832) *Huiting Hong, Hantao Guo, Yucheng Lin, Xiaoqing Yang, Zang Li, Jieping Ye.* 1. **Fast and Deep Graph Neural Networks.** AAAI 2020. [paper](https://arxiv.org/abs/1911.08941) *Claudio Gallicchio, Alessio Micheli.* 1. **Hypergraph Label Propagation Network.** AAAI 2020. [paper]() *Yubo Zhang, Nan Wang, Yufeng Chen, Changqing Zou, Hai Wan, Xibin Zhao, Yue Gao.* 1. **Learning Signed Network Embedding via Graph Attention.** AAAI 2020. [paper]() *Yu Li, Yuan Tian, Jiawei Zhang, Yi Chang.* 1. **GraLSP: Graph Neural Networks with Local Structural Patterns.** AAAI 2020. [paper](https://arxiv.org/abs/1911.07675) *Yilun Jin, Guojie Song, Chuan Shi.* 1. **ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations.** AAAI 2020. [paper](https://arxiv.org/abs/1911.07979) *Ekagra Ranjan, Soumya Sanyal, Partha Pratim Talukdar.* 1. **Multi‐Stage Self­‐Supervised Learning for Graph Convolutional Networks on Graphs with Few Labeled Nodes.** AAAI 2020. [paper](https://zhouchenlin.github.io/Publications/2020-AAAI-M3S.pdf) *Ke Sun, Zhouchen Lin, Zhanxing Zhu.* 1. **Collaborative Graph Convolutional Networks: Unsupervised Learning Meets Semi-­‐Supervised Learning.** AAAI 2020. [paper]() *Binyuan Hui, Pengfei Zhu, Qinghua, Hu.* 1. **A Multi­‐Scale Approach for Graph Link Prediction.** AAAI 2020. [paper]() *Lei Cai, Shuiwang Ji.* 1. **Adaptive Structural Fingerprints for Graph Attention Networks.** ICLR 2020. [paper](https://openreview.net/pdf?id=BJxWx0NYPr) *Kai Zhang, Yaokang Zhu, Jun Wang, Jie Zhang.* 1. **Strategies for Pre-training Graph Neural Networks.** ICLR 2020. [paper](https://openreview.net/pdf?id=HJlWWJSFDH) *Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, Jure Leskovec.* 1. **DropEdge: Towards Deep Graph Convolutional Networks on Node Classification.** ICLR 2020. [paper](https://openreview.net/pdf?id=Hkx1qkrKPr) *Yu Rong, Wenbing Huang, Tingyang Xu, Junzhou Huang.* 1. **Directional Message Passing for Molecular Graphs.** ICLR 2020. [paper](https://openreview.net/pdf?id=B1eWbxStPH) *Johannes Klicpera, Janek Groß, Stephan Günnemann.* 1. **DeepSphere: a graph-based spherical CNN.** ICLR 2020. [paper](https://openreview.net/pdf?id=B1e3OlStPB) *Michaël Defferrard, Martino Milani, Frédérick Gusset, Nathanaël Perraudin.* 1. **Geom-GCN: Geometric Graph Convolutional Networks.** ICLR 2020. [paper](https://openreview.net/pdf?id=S1e2agrFvS) *Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, Bo Yang.* 1. **Curvature Graph Network.** ICLR 2020. [paper](https://openreview.net/pdf?id=BylEqnVFDB) *Ze Ye, Kin Sum Liu, Tengfei Ma, Jie Gao, Chao Chen.* 1. **Measuring and Improving the Use of Graph Information in Graph Neural Networks.** ICLR 2020. [paper](https://openreview.net/pdf?id=rkeIIkHKvS) *Yifan Hou, Jian Zhang, James Cheng, Kaili Ma, Richard T. B. Ma, Hongzhi Chen, Ming-Chang Yang.* 1. **Memory-Based Graph Networks.** ICLR 2020. [paper](https://openreview.net/pdf?id=r1laNeBYPB) *Amir Hosein Khasahmadi, Kaveh Hassani, Parsa Moradi, Leo Lee, Quaid Morris.* 1. **Pruned Graph Scattering Transforms.** ICLR 2020. [paper](https://openreview.net/pdf?id=rJeg7TEYwB) *Vassilis N. Ioannidis, Siheng Chen, Georgios B. Giannakis.* 1. **Neural Execution of Graph Algorithms.** ICLR 2020. [paper](https://openreview.net/pdf?id=SkgKO0EtvS) *Petar Veličković, Rex Ying, Matilde Padovano, Raia Hadsell, Charles Blundell.* 1. **GraphSAINT: Graph Sampling Based Inductive Learning Method.** ICLR 2020. [paper](https://openreview.net/pdf?id=BJe8pkHFwS) *Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, Viktor Prasanna.* 1. **Graph inference learning for semi-supervised classification.** ICLR 2020. [paper](https://openreview.net/pdf?id=r1evOhEKvH) *Chunyan Xu, Zhen Cui, Xiaobin Hong, Tong Zhang, Jian Yang, Wei Liu.* 1. **SGAS: Sequential Greedy Architecture Search.** CVPR 2020. [paper](https://arxiv.org/abs/1912.00195) *Guohao Li, Guocheng Qian, Itzel C. Delgadillo, Matthias Müller, Ali Thabet, Bernard Ghanem.* 1. **Adaptive Propagation Graph Convolutional Network.** IEEE TNNLS 2020. [paper](https://ieeexplore.ieee.org/document/9206045) *Indro Spinelli, Simone Scardapane, Aurelio Uncini.*

Graph Types

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

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

  2. Hypergraph Neural Networks. AAAI 2019. paper

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

  3. Heterogeneous Graph Attention Network. WWW 2019. paper

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

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

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

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

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

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

    Ziyao Li, Liang Zhang, Guojie Song.

  7. Dynamic Hypergraph Neural Networks. IJCAI 2019. paper

    Jianwen Jiang, Yuxuan Wei, Yifan Feng, Jingxuan Cao, Yue Gao.

  8. Exploiting Interaction Links for Node Classification with Deep Graph Neural Networks. IJCAI 2019. paper

    Hogun Park, Jennifer Neville.

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

    Liyu Gong, Qiang Cheng.

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

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

  11. Graph Transformer Networks. NeurIPS 2019. paper

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

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

    Andrei Nicolicioiu, Iulia Duta, Marius Leordeanu.

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

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

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

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

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

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

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

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

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

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

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

    Ruochi Zhang, Yuesong Zou, Jian Ma.

  19. Digraph Inception Convolutional Networks. NeurIPS 2020. paper

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

  20. Subgraph Neural Networks. NeurIPS 2020. paper

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

  21. Dynamic Graph Convolutional Networks Using the Tensor M-Product. SDM 2021. paper

    Osman Asif Malik, Shashanka Ubaru, Lior Horesh, Misha E. Kilmer, Haim Avron

Pooling Methods

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

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

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

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

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

    Junhyun Lee, Inyeop Lee, Jaewoo Kang.

  4. Graph U-Nets. ICML 2019. paper

    Hongyang Gao, Shuiwang Ji.

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

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

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

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

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

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

  8. Diffusion Improves Graph Learning. NeurIPS 2019. paper

    Johannes Klicpera, Stefan Weißenberger, Stephan Günnemann.

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

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

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

    Hao Yuan, Shuiwang Ji.

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

    Filippo Maria Bianchi, Daniele Grattarola, Cesare Alippi.

  12. Accurate Learning of Graph Representations with Graph Multiset Pooling. ICLR 2021. paper

    Jinheon Baek, Minki Kang, Sung Ju Hwang.

Analysis

  1. A Comparison between Recursive Neural Networks and Graph Neural Networks. IJCNN 2006. paper

    Vincenzo Di Massa, Gabriele Monfardini, Lorenzo Sarti, Franco Scarselli, Marco Maggini, Marco Gori.

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

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

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

    Tatsuro Kawamoto, Masashi Tsubaki, Tomoyuki Obuchi.

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

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

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

    Qimai Li, Zhichao Han, Xiao-Ming Wu.

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

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

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

    Saurabh Verma, Zhi-Li Zhang.

  8. Simplifying Graph Convolutional Networks. ICML 2019. paper

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

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

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

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

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

  11. Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks. AAAI 2019. paper

    Christopher Morris, Martin Ritzert, Matthias Fey, William L. Hamilton, Jan Eric Lenssen, Gaurav Rattan, Martin Grohe.

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

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

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

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

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

    Nicolas Keriven, Gabriel Peyré.

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

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

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

    Nima Dehmamy, Albert-Laszlo Barabasi, Rose Yu.

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

    Kenta Oono, Taiji Suzuki.

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

    Andreas Loukas.

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

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

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

    Balasubramaniam Srinivasan, Bruno Ribeiro.

  21. Can Graph Neural Networks Count Substructures? NeurIPS 2020. paper

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

Efficiency

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

    Jianfei Chen, Jun Zhu, Le Song.

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

    Jie Chen, Tengfei Ma, Cao Xiao.

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

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

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

    Hongyang Gao, Zhengyang Wang, Shuiwang Ji.

  5. Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks. KDD 2019. paper

    Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, Cho-Jui Hsieh.

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

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

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

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

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

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

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

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

  10. Memory Efficient Graph Convolutional Networkbased Distributed Link Prediction. IEEE Big Data 2020. paper code

    Damitha Senevirathne, Isuru Wijesiri, Suchitha Dehigaspitiya, Miyuru Dayarathna, Sanath Jayasena, and Toyotaro Suzumura.

Explainability

  1. Explainability Techniques for Graph Convolutional Networks. ICML 2019. paper

    Federico Baldassarre, Hossein Azizpour.

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

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

  3. GCN-LRP Explanation: Exploring Latent Attention of Graph Convolutional Networks. IJCNN 2020. paper

    Jinlong Hu, Tenghui Li, Shoubin Dong.

  4. Parameterized Explainer for Graph Neural Network. NeurIPS 2020. paper

    Dongsheng Luo, Wei Cheng, Dongkuan Xu, Wenchao Yu, Bo Zong, Haifeng Chen, Xiang Zhang.

  5. XGNN: Towards Model-Level Explanations of Graph Neural Networks. KDD 2020. paper

    Hao Yuan, Jiliang Tang, Xia Hu, Shuiwang Ji.

  6. Contrastive Graph Neural Network Explanation. ICML 2020. paper

    Lukas Faber, Amin K. Moghaddam, Roger Wattenhofer.

  7. Interpreting Graph Neural Networks for NLP With Differentiable Edge Maskin. ICLR 2021. paper

    Michael Sejr Schlichtkrull, Nicola De Cao, Ivan Titov.

  8. On Explainability of Graph Neural Networks via Subgraph Explorations. ICML 2021. paper

    Hao Yuan, Haiyang Yu, Jie Wang, Kang Li, Shuiwang Ji.

  9. Generative Causal Explanations for Graph Neural Networks. ICML 2021. paper

    Wanyu Lin, Hao Lan, Baochun Li.

  10. GraphSVX: Shapley Value Explanations for Graph Neural Networks. ECML PKDD 2021. paper

    Alexandre Duval, Fragkiskos D. Malliaros.

Applications

Physics

  1. Discovering objects and their relations from entangled scene representations. ICLR Workshop 2017. paper

    David Raposo, Adam Santoro, David Barrett, Razvan Pascanu, Timothy Lillicrap, Peter Battaglia.

  2. A simple neural network module for relational reasoning. NIPS 2017. paper

    Adam Santoro, David Raposo, David G.T. Barrett, Mateusz Malinowski, Razvan Pascanu, Peter Battaglia, Timothy Lillicrap.

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

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

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

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

  5. Graph networks as learnable physics engines for inference and control. ICML 2018. paper

    Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg, Josh Merel, Martin Riedmiller, Raia Hadsell, Peter Battaglia.

  6. Learning Multiagent Communication with Backpropagation. NIPS 2016. paper

    Sainbayar Sukhbaatar, Arthur Szlam, Rob Fergus.

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

    Yedid Hoshen.

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

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

  9. Graph Element Networks: adaptive, structured computation and memory. ICML 2019. paper

    Ferran Alet, Adarsh K. Jeewajee, Maria Bauza, Alberto Rodriguez, Tomas Lozano-Perez, Leslie Pack Kaelbling.

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

    Sungyong Seo, Chuizheng Meng, Yan Liu.

Chemistry and Biology

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

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

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

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

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

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

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

    Sungmin Rhee, Seokjun Seo, Sun Kim.

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

    Marinka Zitnik, Monica Agrawal, Jure Leskovec.

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

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

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

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

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

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

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

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

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

    Tianle Ma, Aidong Zhang.

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

    Kien Do, Truyen Tran, Svetha Venkatesh.

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

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

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

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

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

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

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

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

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

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

Knowledge Graph

  1. Modeling Relational Data with Graph Convolutional Networks. ESWC 2018. paper

    Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, Max Welling.

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

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

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

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

  4. End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completion. AAAI 2019. paper

    Chao Shang, Yun Tang, Jing Huang, Jinbo Bi, Xiaodong He, Bowen Zhou.

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

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

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

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

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

    Haoyu Wang, Defu Lian, Yong Ge.

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

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

  9. OAG: Toward Linking Large-scale Heterogeneous Entity Graphs. KDD 2019. paper

    Fanjin Zhang, Xiao Liu, Jie Tang, Yuxiao Dong, Peiran Yao, Jie Zhang, Xiaotao Gu, Yan Wang, Bin Shao, Rui Li, Kuansan Wang.

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

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

  11. Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Network. ACL 2019. paper

    Kun Xu, Mo Yu, Yansong Feng, Yan Song, Zhiguo Wang, Dong Yu.

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

    Ivana Balazevic, Carl Allen, Timothy Hospedales.

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

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

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

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

Recommender Systems

  1. Graph Convolutional Neural Networks for Web-Scale Recommender Systems. KDD 2018. paper

    Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, Jure Leskovec.

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

    Federico Monti, Michael M. Bronstein, Xavier Bresson.

  3. Graph Convolutional Matrix Completion. 2017. paper

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

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

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

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

    Haoyu Wang, Defu Lian, Yong Ge.

  6. Graph Contextualized Self-Attention Network for Session-based Recommendation. IJCAI 2019. paper

    Chengfeng Xu, Pengpeng Zhao, Yanchi Liu, Victor S. Sheng, Jiajie Xu, Fuzhen Zhuang, Junhua Fang, Xiaofang Zhou.

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

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

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

    Jin Shang, Mingxuan Sun.

  9. Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems. KDD 2019. paper

    Hongwei Wang, Fuzheng Zhang, Mengdi Zhang, Jure Leskovec, Miao Zhao, Wenjie Li, Zhongyuan Wang.

  10. Exact-K Recommendation via Maximal Clique Optimization. KDD 2019. paper

    Yu Gong, Yu Zhu, Lu Duan, Qingwen Liu, Ziyu Guan, Fei Sun, Wenwu Ou, Kenny Q. Zhu.

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

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

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

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

  13. Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems. WWW 2019. paper

    Qitian Wu, Hengrui Zhang, Xiaofeng Gao, Peng He, Paul Weng, Han Gao, Guihai Chen.

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

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

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

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

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

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

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

    Muhan Zhang, Yixin Chen.

Computer Vision

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

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

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

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

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

    Will Norcliffe-Brown, Efstathios Vafeias, Sarah Parisot.

  4. Symbolic Graph Reasoning Meets Convolutions. NeurIPS 2018. paper

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

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

    Medhini Narasimhan, Svetlana Lazebnik, Alexander Schwing.

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

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

  7. Relation Networks for Object Detection. CVPR 2018. paper

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

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

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

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

    Kenneth Marino, Ruslan Salakhutdinov, Abhinav Gupta.

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

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

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

    Damien Teney, Lingqiao Liu, Anton van den Hengel.

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

    Sijie Yan, Yuanjun Xiong, Dahua Lin.

  13. Dynamic Graph CNN for Learning on Point Clouds. CVPR 2018. paper

    Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E. Sarma, Michael M. Bronstein, Justin M. Solomon.

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

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

  15. 3D Graph Neural Networks for RGBD Semantic Segmentation. CVPR 2017. paper

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

  16. Iterative Visual Reasoning Beyond Convolutions. CVPR 2018. paper

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

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

    Martin Simonovsky, Nikos Komodakis.

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

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

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

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

  20. I Know the Relationships: Zero-Shot Action Recognition via Two-Stream Graph Convolutional Networks and Knowledge Graphs. AAAI 2019. paper

    Junyu Gao, Tianzhu Zhang, Changsheng Xu.

more 21. **Graph CNNs with Motif and Variable Temporal Block for Skeleton-based Action Recognition.** AAAI 2019. [paper](https://ecs.victoria.ac.nz/foswiki/pub/Groups/Graphics/RGB-DDataProcessingForRobotics/Graph%20CNNs%20with%20Motif%20and%20Variable%20Temporal%20Block%20for%20Skeleton-based%20Action%20Recognition.pdf) *Yu-Hui Wen, Lin Gao, Hongbo Fu, Fang-Lue Zhang, Shihong Xia.* 1. **Multi-Label Image Recognition with Graph Convolutional Networks.** CVPR 2019. [paper](https://arxiv.org/pdf/1904.03582.pdf) *Zhao-Min Chen, Xiu-Shen Wei, Peng Wang, Yanwen Guo.* 1. **Spatial-Aware Graph Relation Network for Large-Scale Object Detection.** CVPR 2019. [paper](http://openaccess.thecvf.com/content_CVPR_2019/papers/Xu_Spatial-Aware_Graph_Relation_Network_for_Large-Scale_Object_Detection_CVPR_2019_paper.pdf) *Hang Xu, Chenhan Jiang, Xiaodan Liang, Zhenguo Li.* 1. **GCAN: Graph Convolutional Adversarial Network for Unsupervised Domain Adaptation.** CVPR 2019. [paper](http://openaccess.thecvf.com/content_CVPR_2019/papers/Ma_GCAN_Graph_Convolutional_Adversarial_Network_for_Unsupervised_Domain_Adaptation_CVPR_2019_paper.pdf) *Xinhong Ma, Tianzhu Zhang, Changsheng Xu.* 1. **Mind Your Neighbours: Image Annotation With Metadata Neighbourhood Graph Co-Attention Networks.** CVPR 2019. [paper](http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhang_Mind_Your_Neighbours_Image_Annotation_With_Metadata_Neighbourhood_Graph_Co-Attention_CVPR_2019_paper.pdf) *Junjie Zhang, Qi Wu, Jian Zhang, Chunhua Shen, Jianfeng Lu.* 1. **Attentive Relational Networks for Mapping Images to Scene Graphs.** CVPR 2019. [paper](https://arxiv.org/pdf/1811.10696.pdf) *Mengshi Qi, Weijian Li, Zhengyuan Yang, Yunhong Wang, Jiebo Luo.* 1. **Knowledge-Embedded Routing Network for Scene Graph Generation.** CVPR 2019. [paper](https://arxiv.org/pdf/1903.03326.pdf) *Tianshui Chen, Weihao Yu, Riquan Chen, Liang Lin.* 1. **Auto-Encoding Scene Graphs for Image Captioning.** CVPR 2019. [paper](https://arxiv.org/pdf/1812.02378.pdf) *Xu Yang, Kaihua Tang, Hanwang Zhang, Jianfei Cai.* 1. **Learning to Cluster Faces on an Affinity Graph.** CVPR 2019. [paper](https://arxiv.org/pdf/1904.02749.pdf) *Lei Yang, Xiaohang Zhan, Dapeng Chen, Junjie Yan, Chen Change Loy, Dahua Lin.* 1. **Learning a Deep ConvNet for Multi-label Classification with Partial Labels.** CVPR 2019. [paper](https://arxiv.org/pdf/1902.09720.pdf) *Thibaut Durand, Nazanin Mehrasa, Greg Mori.* 1. **Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detection.** CVPR 2019. [paper](https://arxiv.org/pdf/1903.07256.pdf) *Jia-Xing Zhong, Nannan Li, Weijie Kong, Shan Liu, Thomas H. Li, Ge Li.* 1. **Learning Actor Relation Graphs for Group Activity Recognition.** CVPR 2019. [paper](https://arxiv.org/pdf/1904.10117.pdf) *Jianchao Wu, Limin Wang, Li Wang, Jie Guo, Gangshan Wu.* 1. **ABC: A Big CAD Model Dataset For Geometric Deep Learning.** CVPR 2019. [paper](https://arxiv.org/pdf/1812.06216.pdf) *Sebastian Koch, Albert Matveev, Zhongshi Jiang, Francis Williams, Alexey Artemov, Evgeny Burnaev, Marc Alexa, Denis Zorin, Daniele Panozzo.* 1. **Neighbourhood Watch: Referring Expression Comprehension via Language-guided Graph Attention Networks.** CVPR 2019. [paper](https://arxiv.org/pdf/1812.04794.pdf) *Peng Wang, Qi Wu, Jiewei Cao, Chunhua Shen, Lianli Gao, Anton van den Hengel.* 1. **Graph-Based Global Reasoning Networks.** CVPR 2019. [paper](https://arxiv.org/pdf/1811.12814.pdf) *Yunpeng Chen, Marcus Rohrbach, Zhicheng Yan, Shuicheng Yan, Jiashi Feng, Yannis Kalantidis.* 1. **Linkage Based Face Clustering via Graph Convolution Network.** CVPR 2019. [paper](https://arxiv.org/pdf/1903.11306.pdf) *Zhongdao Wang, Liang Zheng, Yali Li, Shengjin Wang.* 1. **Fast Interactive Object Annotation with Curve-GCN.** CVPR 2019. [paper](https://arxiv.org/pdf/1903.06874.pdf) *Huan Ling, Jun Gao, Amlan Kar, Wenzheng Chen, Sanja Fidler.* 1. **Semantic Graph Convolutional Networks for 3D Human Pose Regression.** CVPR 2019. [paper](https://arxiv.org/pdf/1904.03345.pdf) *Long Zhao, Xi Peng, Yu Tian, Mubbasir Kapadia, Dimitris N. Metaxas.* 1. **Neural Task Graphs: Generalizing to Unseen Tasks from a Single Video Demonstration.** CVPR 2019. [paper](https://arxiv.org/pdf/1807.03480.pdf) *De-An Huang, Suraj Nair, Danfei Xu, Yuke Zhu, Animesh Garg, Li Fei-Fei, Silvio Savarese, Juan Carlos Niebles.* 1. **Graphonomy: Universal Human Parsing via Graph Transfer Learning.** CVPR 2019. [paper](https://arxiv.org/pdf/1904.04536.pdf) *Ke Gong, Yiming Gao, Xiaodan Liang, Xiaohui Shen, Meng Wang, Liang Lin.* 1. **Learning Context Graph for Person Search.** CVPR 2019. [paper](https://arxiv.org/pdf/1904.01830.pdf) *Yichao Yan, Qiang Zhang, Bingbing Ni, Wendong Zhang, Minghao Xu, Xiaokang Yang.* 1. **Occlusion-Net: 2D/3D Occluded Keypoint Localization Using Graph Networks.** CVPR 2019. [paper](http://www.cs.cmu.edu/~mvo/index_files/Papers/ONet_19.pdf) *N. Dinesh Reddy, Minh Vo, Srinivasa G. Narasimhan.* 1. **MAN: Moment Alignment Network for Natural Language Moment Retrieval via Iterative Graph Adjustment.** CVPR 2019. [paper](https://arxiv.org/pdf/1812.00087.pdf) *Da Zhang, Xiyang Dai, Xin Wang, Yuan-Fang Wang, Larry S. Davis.* 1. **Context-Aware Visual Compatibility Prediction.** CVPR 2019. [paper](https://arxiv.org/pdf/1902.03646.pdf) *Guillem Cucurull, Perouz Taslakian, David Vazquez.* 1. **Graph Attention Convolution for Point Cloud Semantic Segmentation.** CVPR 2019. [paper](http://openaccess.thecvf.com/content_CVPR_2019/papers/Wang_Graph_Attention_Convolution_for_Point_Cloud_Semantic_Segmentation_CVPR_2019_paper.pdf) *Lei Wang, Yuchun Huang, Yaolin Hou, Shenman Zhang, Jie Shan.* 1. **An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition.** CVPR 2019. [paper](https://arxiv.org/pdf/1902.09130.pdf) *Chenyang Si, Wentao Chen, Wei Wang, Liang Wang, Tieniu Tan.* 1. **Actional-Structural Graph Convolutional Networks for Skeleton-based Action Recognition.** CVPR 2019. [paper](https://arxiv.org/pdf/1904.12659.pdf) *Maosen Li, Siheng Chen, Xu Chen, Ya Zhang, Yanfeng Wang, Qi Tian.* 1. **Graph Convolutional Tracking.** CVPR 2019. [paper](http://nlpr-web.ia.ac.cn/mmc/homepage/jygao/JY_Gao_files/Conference_Papers/GCT-CVPR2019-GJY.pdf) *Junyu Gao, Tianzhu Zhang, Changsheng Xu.* 1. **Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition.** CVPR 2019. [paper](http://openaccess.thecvf.com/content_CVPR_2019/papers/Shi_Two-Stream_Adaptive_Graph_Convolutional_Networks_for_Skeleton-Based_Action_Recognition_CVPR_2019_paper.pdf) *Lei Shi, Yifan Zhang, Jian Cheng, Hanqing Lu.* 1. **Skeleton-Based Action Recognition With Directed Graph Neural Networks.** CVPR 2019. [paper](http://openaccess.thecvf.com/content_CVPR_2019/papers/Shi_Skeleton-Based_Action_Recognition_With_Directed_Graph_Neural_Networks_CVPR_2019_paper.pdf) *Lei Shi, Yifan Zhang, Jian Cheng, Hanqing Lu.* 1. **Neural Module Networks.** CVPR 2016. [paper](https://arxiv.org/pdf/1511.02799.pdf) *Jacob Andreas, Marcus Rohrbach, Trevor Darrell, Dan Klein.* 1. **LatentGNN: Learning Efficient Non-local Relations for Visual Recognition.** ICML 2019. [paper](https://arxiv.org/pdf/1905.11634) *Songyang Zhang, Shipeng Yan, Xuming He.* 1. **Graph Convolutional Gaussian Processes.** ICML 2019. [paper](https://arxiv.org/pdf/1905.05739) *Ian Walker, Ben Glocker.* 1. **GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects.** ICML 2019. [paper](https://arxiv.org/pdf/1901.11461) *Edward J. Smith, Scott Fujimoto, Adriana Romero, David Meger.* 1. **Learning Cross­‐modal Context Graph Networks for Visual Grounding.** AAAI 2020. [paper](https://arxiv.org/abs/1911.09042) *Yongfei Liu, Bo Wan, Xiaodan Zhu, Xuming He.* 1. **Zero­‐Shot Sketch-based Image Retrieval via Graph Convolution Network.** AAAI 2020. [paper]() *Zhaolong Zhang, Yuejie Zhang, Rui Feng, Tao Zhang, Weiguo Fan.* 1. **Hybrid Graph Neural Networks for Crowd Counting.** AAAI 2020. [paper](https://arxiv.org/pdf/2002.00092) *Ao Luo, Fan Yang, Xin Li, Dong Nie, Zhicheng Jiao, Shangchen Zhou, Hong Cheng.* 1. **Learning Graph Convolutional Network for Skeleton-­‐based Human Action Recognition by Neural Searching.** AAAI 2020. [paper](https://arxiv.org/abs/1911.04131) *Wei Peng, Xiaopeng Hong, Haoyu Chen, Guoying Zhao.* 1. **STEP: Spatial Temporal Graph Convolutional Networks for Emotion Perception from Gaits.** AAAI 2020. [paper](https://arxiv.org/abs/1910.12906) *Uttaran Bhattacharya, Trisha Mittal, Rohan Chandra, Tanmay Randhavane, Aniket Bera, Dinesh Manocha.* 1. **Relation‐Aware Pedestrian Attribute Recognition with Graph Convolutional Networks.** AAAI 2020. [paper]() *Zichang Tan, Yang Yang, Jun Wan, Stan Li.* 1. **Deep Generative Probabilistic Graph Neural Networks for Scene Graph Generation.** AAAI 2020. [paper](https://grlearning.github.io/papers/135.pdf) *Mahmoud Khademi, Oliver Schulte.* 1. **Zero-­‐shot Ingredient Recognition by Multi-­‐Relational Graph Convolutional Network.** AAAI 2020. [paper](http://www.liangmingpan.com/files/publications/AAAI20_Paper.pdf) *Jingjing Chen, Liang-Ming Pan, Zhi-Peng Wei, Xiang Wang, Chong-Wah Ngo,Tat-Seng Chua.* 1. **Location-aware Graph Convolutional Networks for Video Question Answering.** AAAI 2020. [paper]() *Deng Huang, Peihao Chen, Runhao Zeng, Qing Du, Mingkui Tan, Chuang Gan.* 1. **Facial Action Unit Intensity Estimation via Semantic Correspondence Learning with Dynamic Graph Convolution.** AAAI 2020. [paper]() *Fan Yingruo, Jacqueline C.K. Lam, Victor Li.* 1. **Reasoning with Heterogeneous Graph Alignment for Video Question Answering.** AAAI 2020. [paper]() *Pin Jiang, Yahong Han.* 1. **Multi-Label Classification with Label Graph Superimposing.** AAAI 2020. [paper](https://arxiv.org/abs/1911.09243) *Ya Wang, Dongliang He, Fu Li, Xiang Long, Zhichao Zhou, Jinwen Ma, Shilei Wen.* 1. **Part-Level Graph Convolutional Network for Skeleton-Based Action Recognition.** AAAI 2020. [paper]() *Linjiang Huang, Yan Huang, Wanli Ouyang, Liang Wang.* 1. **SOGNet: Scene Overlap Graph Network for Panoptic Segmentation.** AAAI 2020. [paper](https://arxiv.org/abs/1911.07527) *Yibo Yang, Hongyang Li, Xia Li, Qijie Zhao, Jianlong Wu, Zhouchen Lin.* 1. **Universal-RCNN: Universal Object Detector via Transferable Graph R-CNN.** AAAI 2020. [paper](https://arxiv.org/abs/2002.07417) *Hang Xu, Linpu Fang, Xiaodan Liang, Wenxiong Kang, Zhenguo Li.* 1. **Abstract Diagrammatic Reasoning with Multiplex Graph Networks.** ICLR 2020. [paper](https://openreview.net/pdf?id=ByxQB1BKwH) *Duo Wang, Mateja Jamnik, Pietro Lio.*

Natural Language Processing

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

    Vicky Zayats, Mari Ostendorf.

  2. Learning Graphical State Transitions. ICLR 2017. paper

    Daniel D. Johnson.

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

    Xiao Liu, Zhunchen Luo, Heyan Huang.

  4. Recurrent Relational Networks. NeurIPS 2018. paper

    Rasmus Palm, Ulrich Paquet, Ole Winther.

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

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

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

    Diego Marcheggiani, Ivan Titov.

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

    Thien Huu Nguyen, Ralph Grishman.

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

    Diego Marcheggiani, Joost Bastings, Ivan Titov.

  9. Exploring Graph-structured Passage Representation for Multi-hop Reading Comprehension with Graph Neural Networks. 2018. paper

    Linfeng Song, Zhiguo Wang, Mo Yu, Yue Zhang, Radu Florian, Daniel Gildea.

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

    Yuhao Zhang, Peng Qi, Christopher D. Manning.

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

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

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

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

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

    Daniel Beck, Gholamreza Haffari, Trevor Cohn.

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

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

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

    Yue Zhang, Qi Liu, Linfeng Song.

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

    Makoto Miwa, Mohit Bansal.

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

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

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

    Afshin Rahimi, Trevor Cohn, Timothy Baldwin.

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

    Daniil Sorokin, Iryna Gurevych.

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

    Liang Yao, Chengsheng Mao, Yuan Luo.

more 21. **Constructing Narrative Event Evolutionary Graph for Script Event Prediction.** IJCAI 2018. [paper](https://arxiv.org/pdf/1805.05081.pdf) *Zhongyang Li, Xiao Ding, Ting Liu.* 1. **Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks.** ACL 2019. [paper](https://arxiv.org/pdf/1809.04283) *Shikhar Vashishth, Manik Bhandari, Prateek Yadav, Piyush Rai, Chiranjib Bhattacharyya, Partha Talukdar* 1. **PaperRobot: Incremental Draft Generation of Scientific Ideas.** ACL 2019. [paper](https://arxiv.org/pdf/1905.07870) *Qingyun Wang, Lifu Huang, Zhiying Jiang, Kevin Knight, Heng Ji, Mohit Bansal, Yi Luan.* 1. **Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network.** ACL 2019. [paper](https://arxiv.org/pdf/1906.04684) *Sunil Kumar Sahu, Fenia Christopoulou, Makoto Miwa, Sophia Ananiadou.* 1. **Textbook Question Answering with Multi-modal Context Graph Understanding and Self-supervised Open-set Comprehension.** ACL 2019. [paper](https://arxiv.org/pdf/1811.00232) *Daesik Kim, Seonhoon Kim, Nojun Kwak.* 1. **Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs.** ACL 2019. [paper](https://arxiv.org/pdf/1905.07374) *Ming Tu, Guangtao Wang, Jing Huang, Yun Tang, Xiaodong He, Bowen Zhou.* 1. **Dynamically Fused Graph Network for Multi-hop Reasoning.** ACL 2019. [paper](https://arxiv.org/pdf/1905.06933) *Yunxuan Xiao, Yanru Qu, Lin Qiu, Hao Zhou, Lei Li, Weinan Zhang, Yong Yu.* 1. **Cognitive Graph for Multi-Hop Reading Comprehension at Scale.** ACL 2019. [paper](https://arxiv.org/pdf/1905.05460) *Ming Ding, Chang Zhou, Qibin Chen, Hongxia Yang, Jie Tang.* 1. **Joint Type Inference on Entities and Relations via Graph Convolutional Networks.** ACL 2019. [paper](http://www.czsun.site/publications/joint_entrel_gcn.pdf) *Changzhi Sun, Yeyun Gong, Yuanbin Wu, Ming Gong, Daxing Jiang, Man Lan, Shiliang Sun1, Nan Duan.* 1. **Attention Guided Graph Convolutional Networks for Relation Extraction.** ACL 2019. [paper](http://www.statnlp.org/wp-content/uploads/2019/06/Attention_Guided_Graph_Convolutional_Networks_for_Relation_Extraction.pdf) *Zhijiang Guo, Yan Zhang, Wei Lu.* 1. **GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction.** ACL 2019. [paper](https://tsujuifu.github.io/pubs/acl19_graph-rel.pdf) *Tsu-Jui Fu, Peng-Hsuan Li, Wei-Yun Ma.* 1. **Graph Neural Networks with Generated Parameters for Relation Extraction.** ACL 2019. [paper](https://arxiv.org/pdf/1902.00756) *Hao Zhu, Yankai Lin, Zhiyuan Liu, Jie Fu, Tat-seng Chua, Maosong Sun.* 1. **Generating Logical Forms from Graph Representations of Text and Entities.** ACL 2019. [paper](https://arxiv.org/pdf/1905.08407) *Peter Shaw, Philip Massey, Angelica Chen, Francesco Piccinno, Yasemin Altun.* 1. **Matching Article Pairs with Graphical Decomposition and Convolutions.** ACL 2019. [paper](https://arxiv.org/pdf/1802.07459) *Bang Liu, Di Niu, Haojie Wei, Jinghong Lin, Yancheng He, Kunfeng Lai, Yu Xu.* 1. **Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing.** ACL 2019. [paper](https://arxiv.org/pdf/1905.06241) *Ben Bogin, Matt Gardner, Jonathan Berant.* 1. **Coherent Comment Generation for Chinese Articles with a Graph-to-Sequence Model.** ACL 2019. [paper](https://arxiv.org/pdf/1906.01231) *Wei Li, Jingjing Xu, Yancheng He, Shengli Yan, Yunfang Wu, Xu sun.* 1. **GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verification.** ACL 2019. [paper](https://www.aclweb.org/anthology/P19-1085) *Jie Zhou, Xu Han, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, Maosong Sun.* 1. **Look Again at the Syntax: Relational Graph Convolutional Network for Gendered Ambiguous Pronoun Resolution.** ACL 2019. [paper](https://arxiv.org/pdf/1905.08868.pdf) *Yinchuan Xu, Junlin Yang.* 1. **Structured Neural Summarization.** ICLR 2019. [paper](https://openreview.net/pdf?id=H1ersoRqtm) *Patrick Fernandes, Miltiadis Allamanis, Marc Brockschmidt.* 1. **Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks.** NAACL 2019. [paper](https://arxiv.org/pdf/1903.01306.pdf) *Ningyu Zhang, Shumin Deng, Zhanlin Sun, Guanying Wang, Xi Chen, Wei Zhang, Huajun Chen.* 1. **Text Generation from Knowledge Graphs with Graph Transformers.** NAACL 2019. [paper](https://arxiv.org/pdf/1904.02342.pdf) *Rik Koncel-Kedziorski, Dhanush Bekal, Yi Luan, Mirella Lapata, Hannaneh Hajishirzi.* 1. **Question Answering by Reasoning Across Documents with Graph Convolutional Networks.** NAACL 2019. [paper](https://arxiv.org/pdf/1808.09920.pdf) *Nicola De Cao, Wilker Aziz, Ivan Titov.* 1. **BAG: Bi-directional Attention Entity Graph Convolutional Network for Multi-hop Reasoning Question Answering.** NAACL 2019. [paper](https://arxiv.org/pdf/1904.04969.pdf) *Yu Cao, Meng Fang, Dacheng Tao.* 1. **GraphIE: A Graph-Based Framework for Information Extraction.** NAACL 2019. [paper](https://arxiv.org/pdf/1810.13083.pdf) *Yujie Qian, Enrico Santus, Zhijing Jin, Jiang Guo, Regina Barzilay.* 1. **Graph Convolution for Multimodal Information Extraction from Visually Rich Documents.** NAACL 2019. [paper](https://arxiv.org/pdf/1903.11279.pdf) *Xiaojing Liu, Feiyu Gao, Qiong Zhang, Huasha Zhao.* 1. **Structural Neural Encoders for AMR-to-text Generation.** NAACL 2019. [paper](https://arxiv.org/pdf/1903.11410.pdf) *Marco Damonte, Shay B. Cohen.* 1. **Abusive Language Detection with Graph Convolutional Networks.** NAACL 2019. [paper](https://arxiv.org/pdf/1904.04073.pdf) *Pushkar Mishra, Marco Del Tredici, Helen Yannakoudakis, Ekaterina Shutova.* 1. **Learning Graph Pooling and Hybrid Convolutional Operations for Text Representations.** WWW 2019. [paper](https://arxiv.org/pdf/1901.06965.pdf) *Hongyang Gao, Yongjun Chen, Shuiwang Ji.* 1. **Graph­‐based Transformer with Cross-candidate Verification for Semantic Parsing.** AAAI 2020. [paper]() *Bo Shao, Yeyun Gong, Weizhen Qi, Guihong Cao, Jianshu Ji, Xiaola Lin.* 1. **Efficient Multi-Person Pose Estimation with Provable Guarantees.** AAAI 2020. [paper](https://arxiv.org/abs/1711.07794) *Shaofei Wang, Konrad Paul Kording, Julian Yarkony.* 1. **Graph Transformer for Graph-to-Sequence Learning.** AAAI 2020. [paper](https://arxiv.org/abs/1911.07470) *Deng Cai, Wai Lam.* 1. **Multi-­‐label Patent Categorization with Non-­‐local Attention-­‐based Graph Convolutional Network.** AAAI 2020. [paper]() *Pingjie Tang, Meng Jiang, Bryan (Ning) Xia, Jed Pitera, Jeff Welser, Nitesh Chawla.* 1. **Multi-task Learning for Metaphor Detection with Graph Convolutional Neural Networks and Word Sense Disambiguation.** AAAI 2020. [paper]() *Duong Minh Le, My Thai and Thien Huu Nguyen.* 1. **Schema-Guided Multi-Domain Dialogue State Tracking with Graph Attention Neural Networks.** AAAI 2020. [paper]() *Lu Chen, Boer Lv, Chi Wang, Su Zhu, Bowen Tan, Kai Yu.* 1. **GraphER: Token-Centric Entity Resolution with Graph Convolutional Neural Networks.** AAAI 2020. [paper]() *Bing Li, Wei Wang, Yifang Sun, Linhan Zhang, Muhammad Asif Ali, Yi Wang.* 1. **CFGNN:Cross Flow Graph Neural Networks for Question Answering on Complex Tables.** AAAI 2020. [paper]() *Xuanyu Zhang.*

Generation

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

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

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

    Tengfei Ma, Jie Chen, Cao Xiao.

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

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

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

    Nicola De Cao, Thomas Kipf.

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

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

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

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

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

    Aditya Grover, Aaron Zweig, Stefano Ermon.

  8. Generative Code Modeling with Graphs. ICLR 2019. paper

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

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

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

  10. Graph Normalizing Flows. NeurIPS 2019. paper

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

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

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

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

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

Combinatorial Optimization

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

    Zhuwen Li, Qifeng Chen, Vladlen Koltun.

  2. Learning a SAT Solver from Single-Bit Supervision. ICLR 2019. paper

    Daniel Selsam, Matthew Lamm, Benedikt Bünz, Percy Liang, Leonardo de Moura, David L. Dill.

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

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

  4. Attention Solves Your TSP, Approximately. 2018. paper

    Wouter Kool, Herke van Hoof, Max Welling.

  5. Learning to Solve NP-Complete Problems - A Graph Neural Network for Decision TSP. AAAI 2019. paper

    Marcelo O. R. Prates, Pedro H. C. Avelar, Henrique Lemos, Luis Lamb, Moshe Vardi.

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

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

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

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

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

    Ryoma Sato, Makoto Yamada, Hisashi Kashima.

Adversarial Attack

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

    Daniel Zügner, Amir Akbarnejad, Stephan Günnemann.

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

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

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

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

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

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

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

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

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

    Daniel Zügner, Stephan Günnemann.

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

    Aleksandar Bojchevski, Stephan Günnemann.

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

    Daniel Zügner, Stephan Günnemann.

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

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

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

    Aleksandar Bojchevski, Stephan Günnemann.

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

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

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

    Xiang Zhang, Marinka Zitnik.

Graph Clustering

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

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

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

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

Graph Classification

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

    Davide Bacciu, Federico Errica, Alessio Micheli.

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

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

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

    Rami Al-Rfou, Dustin Zelle, Bryan Perozzi.

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

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

  5. Motif-matching based Subgraph-level Attentional Convolution Network for Graph Classification. AAAI 2020. paper

    Hao Peng, Jianxin Li, Qiran Gong, Yuanxing Ning, Senzhang Wang, Lifang He.

  6. InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization. ICLR 2020. paper

    Fan-Yun Sun, Jordan Hoffman, Vikas Verma, Jian Tang.

  7. A Fair Comparison of Graph Neural Networks for Graph Classification. ICLR 2020. paper

    Federico Errica, Marco Podda, Davide Bacciu, Alessio Micheli.

Reinforcement Learning

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

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

  4. 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.

  5. 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.

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

    Prithviraj Ammanabrolu, Mark O. Riedl.

  7. Learning Transferable Graph Exploration. NeurIPS 2019. paper

    Hanjun Dai, Yujia Li, Chenglong Wang, Rishabh Singh, Po-Sen Huang, Pushmeet Kohli.

  8. Graph Neural Networks and Reinforcement Learning for Behavior Generation in Semantic Environments. IV 2020. paper

    Patrick Hart, Alois Knoll.

  9. Multi-Agent Game Abstraction via Graph Attention Neural Network. AAAI 2020. paper

    Yong Liu, Weixun Wang, Yujing Hu, Jianye Hao, Xingguo Chen, Yang Gao.

  10. Graph Convolutional Reinforcement Learning. ICLR 2020. paper

    Jiechuan Jiang, Chen Dun, Tiejun Huang, Zongqing Lu.

  11. Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation. ICLR 2020. paper

    Yu Chen, Lingfei Wu, Mohammed J. Zaki.

  12. Reinforced Genetic Algorithm Learning for Optimizing Computation Graphs. ICLR 2020. paper

    Aditya Paliwal, Felix Gimeno, Vinod Nair, Yujia Li, Miles Lubin, Pushmeet Kohli, Oriol Vinyals.

Traffic Network

  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.

  10. Semi-Supervised Hierarchical Recurrent Graph Neural Network for City-Wide Parking Availability Prediction. AAAI 2020. paper

    Weijia Zhang, Hao Liu, Yanchi Liu, Jingbo Zhou, Hui Xiong.

  11. Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks. NeurIPS 2019. paper

    Vineet Kosaraju, Amir Sadeghian, Roberto Martín-Martín, Ian Reid, Hamid Rezatofighi, Silvio Savarese.

  12. GMAN: A Graph Multi-Attention Network for Traffic Prediction. AAAI 2020. paper

    Chuanpan Zheng, Xiaoliang Fan, Cheng Wang, Jianzhong Qi.

Few-shot and Zero-shot Learning

  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.

  8. Learning to Propagate for Graph Meta-Learning. NeurIPS 2019. paper

    LU LIU, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang.

  9. Zero-Shot Video Object Segmentation via Attentive Graph Neural Networks ICCV 2019. paper

    Wenguan Wang, Xiankai Lu, Jianbing Shen, David Crandall, Ling Shao.

  10. Attribute Propagation Network for Graph Zero-­shot Learning. AAAI 2020. paper

    LU LIU, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang.

  11. Graph Few-­‐shot Learning via Knowledge Transfer. AAAI 2020. paper

    Huaxiu Yao, Chuxu Zhang, Ying WEI, Meng Jiang, Suhang Wang, Junzhou Huang, Nitesh Chawla, Zhenhui Li.

  12. FEW-SHOT LEARNING ON GRAPHS VIA SUPER-CLASSES BASED ON GRAPH SPECTRAL MEASURES. ICLR 2020. paper

    Jatin Chauhan, Deepak Nathani, Manohar Kaul.

  13. Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction. NeurIPS 2020. paper

    Jinheon Baek, Dong Bok Lee, Sung Ju Hwang.

  14. Graph Meta Learning via Local Subgraphs. NeurIPS 2020. paper

    Kexin Huang, Marinka Zitnik.

Program Representation

  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.

  3. Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks. NeurIPS 2019. paper

    Yaqin Zhou, Shangqing Liu, Jingkai Siow, Xiaoning Du, Yang Liu.

  4. LambdaNet: Probabilistic Type Inference using Graph Neural Networks. ICLR 2020. paper

    Jiayi Wei, Maruth Goyal, Greg Durrett, Isil Dillig.

  5. HOPPITY: LEARNING GRAPH TRANSFORMATIONS TO DETECT AND FIX BUGS IN PROGRAMS. ICLR 2020. paper

    Elizabeth Dinella, Hanjun Dai, Ziyang Li, Mayur Naik, Le Song, Ke Wang.

Social Network

  1. Link Prediction Based on Graph Neural Networks. NeurIPS 2018. paper

    Muhan Zhang, Yixin Chen.

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

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

  3. 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.

  4. 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.

  5. 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.

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

    Chang Li, Dan Goldwasser.

  7. 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.

  8. Graph Convolutional Networks with Markov Random Field Reasoning for Social Spammer Detection. AAAI 2020. paper

    Yongji Wu, Defu Lian, Yiheng Xu, Le Wu, Enhong Chen.

  9. Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks. AAAI 2020. paper

    Tian Bian, Xi Xiao, Tingyang Xu, Peilin Zhao, Wenbing Huang, Yu Rong, Junzhou Huang.

Graph Matching

  1. Deep Graph Matching Consensus. ICLR 2020. paper

    Matthias Fey, Jan E. Lenssen, Christopher Morris, Jonathan Masci, Nils M. Kriege.

Computer Network

  1. Unveiling the potential of Graph Neural Networks for network modeling and optimization in SDN. ACM SOSR 2019. paper

    Krzysztof Rusek, José Suárez-Varela, Albert Mestres, Pere Barlet-Ros, Albert Cabellos-Aparicio.

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