[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-71589":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":10,"languages":10,"totalLinesOfCode":10,"stars":11,"forks":12,"watchers":13,"openIssues":14,"contributorsCount":15,"subscribersCount":15,"size":15,"stars1d":16,"stars7d":17,"stars30d":18,"stars90d":15,"forks30d":15,"starsTrendScore":19,"compositeScore":20,"rankGlobal":10,"rankLanguage":10,"license":10,"archived":21,"fork":21,"defaultBranch":22,"hasWiki":23,"hasPages":21,"topics":24,"createdAt":10,"pushedAt":10,"updatedAt":27,"readmeContent":28,"aiSummary":29,"trendingCount":15,"starSnapshotCount":15,"syncStatus":16,"lastSyncTime":30,"discoverSource":31},71589,"GNNPapers","thunlp\u002FGNNPapers","thunlp","Must-read papers on graph neural networks (GNN)","",null,16790,3009,594,9,0,2,5,27,6,76.2,false,"master",true,[25,26],"gnn","paper-list","2026-06-12 04:01:01","# Must-read papers on GNN\nGNN: graph neural network\n\nContributed by Jie Zhou, Ganqu Cui, Zhengyan Zhang and Yushi Bai.\n\n## [Content](#content)\n\n\u003Ctable>\n\u003Ctr>\u003Ctd colspan=\"2\">\u003Ca href=\"#survey-papers\">1. Survey\u003C\u002Fa>\u003C\u002Ftd>\u003C\u002Ftr> \n\u003Ctr>\u003Ctd colspan=\"2\">\u003Ca href=\"#models\">2. Models\u003C\u002Fa>\u003C\u002Ftd>\u003C\u002Ftr>\n\u003Ctr>\n    \u003Ctd> \u003Ca href=\"#basic-models\">2.1 Basic Models\u003C\u002Fa>\u003C\u002Ftd>\n    \u003Ctd> \u003Ca href=\"#graph-types\">2.2 Graph Types\u003C\u002Fa>\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n    \u003Ctd> \u003Ca href=\"#pooling-methods\">2.3 Pooling Methods\u003C\u002Fa>\u003C\u002Ftd>\n    \u003Ctd> \u003Ca href=\"#analysis\">2.4 Analysis\u003C\u002Fa>\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n    \u003Ctd> \u003Ca href=\"#efficiency\">2.5 Efficiency\u003C\u002Fa>\u003C\u002Ftd>\n    \u003Ctd> \u003Ca href=\"#explainability\">2.6 Explainability\u003C\u002Fa>\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\u003Ctd colspan=\"2\">\u003Ca href=\"#applications\">3. Applications\u003C\u002Fa>\u003C\u002Ftd>\u003C\u002Ftr> \n\u003Ctr>\n    \u003Ctd> \u003Ca href=\"#physics\">3.1 Physics\u003C\u002Fa>\u003C\u002Ftd>\n    \u003Ctd> \u003Ca href=\"#chemistry-and-biology\">3.2 Chemistry and Biology\u003C\u002Fa>\u003C\u002Ftd>\n\u003C\u002Ftr> \n\u003Ctr>\n    \u003Ctd> \u003Ca href=\"#knowledge-graph\">3.3 Knowledge Graph\u003C\u002Fa>\u003C\u002Ftd>\n    \u003Ctd> \u003Ca href=\"#recommender-systems\">3.4 Recommender Systems\u003C\u002Fa>\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n    \u003Ctd> \u003Ca href=\"#computer-vision\">3.5 Computer Vision\u003C\u002Fa>\u003C\u002Ftd>\n    \u003Ctd> \u003Ca href=\"#natural-language-processing\">3.6 Natural Language Processing\u003C\u002Fa>\u003C\u002Ftd>\n\u003C\u002Ftr> \n\u003Ctr>\n    \u003Ctd> \u003Ca href=\"#generation\">3.7 Generation\u003C\u002Fa>\u003C\u002Ftd>\n    \u003Ctd> \u003Ca href=\"#combinatorial-optimization\">3.8 Combinatorial Optimization\u003C\u002Fa>\u003C\u002Ftd>\n\u003C\u002Ftr> \n\u003Ctr>\n    \u003Ctd> \u003Ca href=\"#adversarial-attack\">3.9 Adversarial Attack\u003C\u002Fa>\u003C\u002Ftd>\n    \u003Ctd> \u003Ca href=\"#graph-clustering\">3.10 Graph Clustering\u003C\u002Fa>\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n    \u003Ctd> \u003Ca href=\"#graph-classification\">3.11 Graph Classification\u003C\u002Fa>\u003C\u002Ftd>\n    \u003Ctd> \u003Ca href=\"#reinforcement-learning\">3.12 Reinforcement Learning\u003C\u002Fa>\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n    \u003Ctd> \u003Ca href=\"#traffic-network\">3.13 Traffic Network\u003C\u002Fa>\u003C\u002Ftd>\n    \u003Ctd> \u003Ca href=\"#few-shot-and-zero-shot-learning\">3.14 Few-shot and Zero-shot Learning\u003C\u002Fa>\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n    \u003Ctd> \u003Ca href=\"#program-representation\">3.15 Program Representation\u003C\u002Fa>\u003C\u002Ftd>\n    \u003Ctd> \u003Ca href=\"#social-network\">3.16 Social Network\u003C\u002Fa>\u003C\u002Ftd>\n\u003C\u002Ftr> \n\u003Ctr>\n    \u003Ctd> \u003Ca href=\"#graph-matching\">3.17 Graph Matching\u003C\u002Fa>\u003C\u002Ftd>\n    \u003Ctd> \u003Ca href=\"#computer-network\">3.18 Computer Network\u003C\u002Fa>\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003C\u002Ftable>\n\n## [Survey papers](#content)\n1. **Introduction to Graph Neural Networks.** Synthesis Lectures on Artificial Intelligence and Machine Learning, Morgan & Claypool Publishers, 2020. [book](https:\u002F\u002Fwww.morganclaypool.com\u002Fdoi\u002F10.2200\u002FS00980ED1V01Y202001AIM045)\n\n    *Zhiyuan Liu, Jie Zhou.* \n\n1. **Graph Neural Networks: A Review of Methods and Applications.** AI Open 2020. [paper](https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.aiopen.2021.01.001)\n   \n    *Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Maosong Sun.* \n\n1. **A Comprehensive Survey on Graph Neural Networks.** arxiv 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1901.00596.pdf)\n\n    *Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, Philip S. Yu.*\n    \n1. **Adversarial Attack and Defense on Graph Data: A Survey.** arxiv 2018. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1812.10528.pdf)\n\n    *Lichao Sun, Yingtong Dou, Carl Yang, Ji Wang, Philip S. Yu, Bo Li.* \n\n1. **Deep Learning on Graphs: A Survey.** arxiv 2018. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1812.04202.pdf)\n\n    *Ziwei Zhang, Peng Cui, Wenwu Zhu.*\n\n1. **Relational Inductive Biases, Deep Learning, and Graph Networks.** arxiv 2018. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1806.01261.pdf)\n\n    *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.*\n\n1. **Geometric Deep Learning: Going beyond Euclidean data.** IEEE SPM 2017. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1611.08097.pdf)\n\n    *Bronstein, Michael M and Bruna, Joan and LeCun, Yann and Szlam, Arthur and Vandergheynst, Pierre.*\n\n1. **Computational Capabilities of Graph Neural Networks.** IEEE TNN 2009. [paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=4703190)\n\n    *Scarselli, Franco and Gori, Marco and Tsoi, Ah Chung and Hagenbuchner, Markus and Monfardini, Gabriele.*\n\n1. **Neural Message Passing for Quantum Chemistry.** ICML 2017. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1704.01212.pdf)\n\n    *Gilmer, Justin and Schoenholz, Samuel S and Riley, Patrick F and Vinyals, Oriol and Dahl, George E.*\n\n1. **Non-local Neural Networks.** CVPR 2018. [paper](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_cvpr_2018\u002Fpapers\u002FWang_Non-Local_Neural_Networks_CVPR_2018_paper.pdf)\n\n    *Wang, Xiaolong and Girshick, Ross and Gupta, Abhinav and He, Kaiming.*\n\n1. **The Graph Neural Network Model.** IEEE TNN 2009. [paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=4700287)\n\n    *Scarselli, Franco and Gori, Marco and Tsoi, Ah Chung and Hagenbuchner, Markus and Monfardini, Gabriele.*\n    \n1. **Benchmarking Graph Neural Networks.** arxiv 2020. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2003.00982.pdf)\n\n    *Dwivedi, Vijay Prakash and Joshi, Chaitanya K. and Laurent, Thomas and Bengio, Yoshua and Bresson, Xavier.*\n\n1. **Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey.** arxiv 2020. [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.07496)\n\n    *Skarding, Joakim and Gabrys, Bogdan and Musial, Katarzyna.*\n\n1. **Bridging the Gap between Spatial and Spectral Domains: A Survey on Graph Neural Networks.** arxiv 2020. [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2002.11867)\n\n    *Zhiqian Chen, Fanglan Chen, Lei Zhang, Taoran Ji, Kaiqun Fu, Liang Zhao, Feng Chen, Chang-Tien Lu.*\n    \n1. **Explainability in Graph Neural Networks: A Taxonomic Survey.** arxiv 2020. [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.15445)\n\n    *Hao Yuan, Haiyang Yu, Shurui Gui, Shuiwang Ji.*\n\n1. **Self-Supervised Learning of Graph Neural Networks: A unified view.** TPAMI 2022. [paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9764632)\n\n    *Yaochen Xie, Zhao Xu, Jingtun Zhang, Zhangyang Wang, Shuiwang Ji.*\n    \n## [Models](#content)   \n\n### [Basic Models](#content)\n1. **Supervised Neural Networks for the Classification of Structures.** IEEE TNN 1997. [paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F572108)\n\n    *Alessandro Sperduti and Antonina Starita.*\n\n1. **Graphical-Based Learning Environments for Pattern Recognition.** SSPR\u002FSPR 2004. [paper](https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007%2F978-3-540-27868-9_4.pdf)\n\n    *Franco Scarselli, Ah Chung Tsoi, Marco Gori, Markus Hagenbuchner.*\n\n1. **A new model for learning in graph domains.** IJCNN 2005. [paper](https:\u002F\u002Fwww.researchgate.net\u002Fprofile\u002FFranco_Scarselli\u002Fpublication\u002F4202380_A_new_model_for_earning_in_raph_domains\u002Flinks\u002F0c9605188cd580504f000000.pdf)\n\n    *Marco Gori, Gabriele Monfardini, Franco Scarselli.*\n\n1. **Graph Neural Networks for Ranking Web Pages.** WI 2005. [paper](https:\u002F\u002Fwww.researchgate.net\u002Fprofile\u002FFranco_Scarselli\u002Fpublication\u002F221158677_Graph_Neural_Networks_for_Ranking_Web_Pages\u002Flinks\u002F0c9605188cd5090ede000000\u002FGraph-Neural-Networks-for-Ranking-Web-Pages.pdf)\n\n    *Franco Scarselli, Sweah Liang Yong, Marco Gori, Markus Hagenbuchner, Ah Chung Tsoi, Marco Maggini.*\n\n1. **Automatic Generation of Complementary Descriptors with Molecular Graph Networks.** J.Chem.Inf.Model. 2005. [paper](https:\u002F\u002Fpubs.acs.org\u002Fdoi\u002F10.1021\u002Fci049613b)\n\n    *Christian Merkwirth and Thomas Lengauer.*\n\n1. **Neural Network for Graphs: A Contextual Constructive Approach.** IEEE TNN 2009. [paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F4773279)\n\n    *Alessio Micheli.*\n    \n1. **Spectral Networks and Locally Connected Networks on Graphs.** ICLR 2014. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1312.6203.pdf)\n\n    *Joan Bruna, Wojciech Zaremba, Arthur Szlam, Yann LeCun.*\n    \n1. **Deep Convolutional Networks on Graph-Structured Data.** arxiv 2015. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1506.05163.pdf)\n\n    *Mikael Henaff, Joan Bruna, Yann LeCun.*\n    \n1. **Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering.** NIPS 2016. [paper](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F6081-convolutional-neural-networks-on-graphs-with-fast-localized-spectral-filtering.pdf)\n\n    *Michaël Defferrard, Xavier Bresson, Pierre Vandergheynst.*\n\n1. **Diffusion-Convolutional Neural Networks.** NIPS 2016. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1511.02136.pdf)\n\n    *James Atwood, Don Towsley.*\n    \n1. **Gated Graph Sequence Neural Networks.** ICLR 2016. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1511.05493.pdf)\n\n    *Yujia Li, Daniel Tarlow, Marc Brockschmidt, Richard Zemel.*\n    \n1. **Learning Convolutional Neural Networks for Graphs.** ICML 2016. [paper](http:\u002F\u002Fproceedings.mlr.press\u002Fv48\u002Fniepert16.pdf)\n\n    *Mathias Niepert, Mohamed Ahmed, Konstantin Kutzkov.* \n    \n1. **Semantic Object Parsing with Graph LSTM.** ECCV 2016. [paper](https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007%2F978-3-319-46448-0_8.pdf)\n\n    *Xiaodan Liang, Xiaohui Shen, Jiashi Feng, Liang Lin, Shuicheng Yan.*\n    \n1. **Semi-Supervised Classification with Graph Convolutional Networks.** ICLR 2017. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1609.02907.pdf)\n\n    *Thomas N. Kipf, Max Welling.*\n    \n1. **Inductive Representation Learning on Large Graphs.** NIPS 2017. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1706.02216.pdf)\n\n    *William L. Hamilton, Rex Ying, Jure Leskovec.*\n    \n1. **Geometric deep learning on graphs and manifolds using mixture model cnns.** CVPR 2017. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1611.08402.pdf)\n\n    *Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodolà, Jan Svoboda, Michael M. Bronstein.*\n    \n1. **Graph Attention Networks.** ICLR 2018. [paper](https:\u002F\u002Fmila.quebec\u002Fwp-content\u002Fuploads\u002F2018\u002F07\u002Fd1ac95b60310f43bb5a0b8024522fbe08fb2a482.pdf)\n\n    *Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, Yoshua Bengio.*\n\n1. **Covariant Compositional Networks For Learning Graphs.** ICLR 2018. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1801.02144.pdf)\n\n    *Risi Kondor, Hy Truong Son, Horace Pan, Brandon Anderson, Shubhendu Trivedi.*\n\n1. **Graph Partition Neural Networks for Semi-Supervised Classification.** ICLR 2018. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1803.06272.pdf)\n\n    *Renjie Liao, Marc Brockschmidt, Daniel Tarlow, Alexander L. Gaunt, Raquel Urtasun, Richard Zemel.*\n    \n1. **Inference in Probabilistic Graphical Models by Graph Neural Networks.** ICLR Workshop 2018. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1803.07710.pdf)\n\n    *KiJung Yoon, Renjie Liao, Yuwen Xiong, Lisa Zhang, Ethan Fetaya, Raquel Urtasun, Richard Zemel, Xaq Pitkow.*\n\n1. **Structure-Aware Convolutional Neural Networks.** NeurIPS 2018. [paper](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F7287-structure-aware-convolutional-neural-networks.pdf)\n\n    *Jianlong Chang, Jie Gu, Lingfeng Wang, Gaofeng Meng, Shiming Xiang, Chunhong Pan.*\n    \n\n\u003Cdetails>\u003Csummary> more \u003C\u002Fsummary> \n\n21. **Bayesian Semi-supervised Learning with Graph Gaussian Processes.** NeurIPS 2018. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1809.04379)\n\n    *Yin Cheng Ng, Nicolò Colombo, Ricardo Silva.*\n\n22. **Adaptive Graph Convolutional Neural Networks.** AAAI 2018. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1801.03226.pdf)\n\n    *Ruoyu Li, Sheng Wang, Feiyun Zhu, Junzhou Huang.*  \n\n1. **Dual Graph Convolutional Networks for Graph-Based Semi-Supervised Classification.** WWW 2018. [paper](http:\u002F\u002Fdelivery.acm.org\u002F10.1145\u002F3190000\u002F3186116\u002Fp499-zhuang.pdf?ip=123.134.247.159&id=3186116&acc=OPEN&key=4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E6D218144511F3437&__acm__=1564108433_6b20d1fe2ab710632bc8434ad3a00bc8)\n\n    *Chenyi Zhuang, Qiang Ma.*\n\n1. **Learning Steady-States of Iterative Algorithms over Graphs.** ICML 2018. [paper](http:\u002F\u002Fproceedings.mlr.press\u002Fv80\u002Fdai18a\u002Fdai18a.pdf)\n\n    *Hanjun Dai, Zornitsa Kozareva, Bo Dai, Alex Smola, Le Song.*\n\n1. **Graph Capsule Convolutional Neural Networks.** ICML 2018 Workshop. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1805.08090.pdf)\n\n    *Saurabh Verma, Zhi-Li Zhang.*\n    \n1. **Capsule Graph Neural Network.** ICLR 2019. [paper](https:\u002F\u002Fopenreview.net\u002Fpdf?id=Byl8BnRcYm)\n\n    *Zhang Xinyi, Lihui Chen.*\n    \n1. **Graph Wavelet Neural Network.** ICLR 2019. [paper](https:\u002F\u002Fopenreview.net\u002Fpdf?id=H1ewdiR5tQ)\n\n    *Bingbing Xu, Huawei Shen, Qi Cao, Yunqi Qiu, Xueqi Cheng.*\n\n1. **Deep Graph Infomax.** ICLR 2019. [paper](https:\u002F\u002Fopenreview.net\u002Fpdf?id=rklz9iAcKQ)\n\n    *Petar Veličković, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, R Devon Hjelm.*\n    \n1. **Predict then Propagate: Graph Neural Networks meet Personalized PageRank.** ICLR 2019. [paper](https:\u002F\u002Fopenreview.net\u002Fpdf?id=H1gL-2A9Ym)\n\n    *Johannes Klicpera, Aleksandar Bojchevski, Stephan Günnemann.*\n\n1. **LanczosNet: Multi-Scale Deep Graph Convolutional Networks.** ICLR 2019. [paper](https:\u002F\u002Fopenreview.net\u002Fpdf?id=BkedznAqKQ)\n\n    *Renjie Liao, Zhizhen Zhao, Raquel Urtasun, Richard Zemel.*\n\n1. **Invariant and Equivariant Graph Networks.** ICLR 2019. [paper](https:\u002F\u002Fopenreview.net\u002Fpdf?id=Syx72jC9tm)\n\n    *Haggai Maron, Heli Ben-Hamu, Nadav Shamir, Yaron Lipman.*\n\n1. **GMNN: Graph Markov Neural Networks.** ICML 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.06214)\n\n    *Meng Qu, Yoshua Bengio, Jian Tang.*\n    \n1. **Position-aware Graph Neural Networks.** ICML 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1906.04817)\n\n    *Jiaxuan You, Rex Ying, Jure Leskovec.*\n\n1. **Disentangled Graph Convolutional Networks.** ICML 2019. [paper](http:\u002F\u002Fproceedings.mlr.press\u002Fv97\u002Fma19a\u002Fma19a.pdf)\n\n    *Jianxin Ma, Peng Cui, Kun Kuang, Xin Wang, Wenwu Zhu.*\n    \n1. **Stochastic Blockmodels meet Graph Neural Networks.** ICML 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.05738)\n\n    *Nikhil Mehta, Lawrence Carin, Piyush Rai.*\n\n1. **Learning Discrete Structures for Graph Neural Networks.** ICML 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1903.11960)\n\n    *Luca Franceschi, Mathias Niepert, Massimiliano Pontil, Xiao He.*\n\n1. **MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing.** ICML 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.00067)\n\n    *Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, Aram Galstyan.*\n\n1. **DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification.** KDD 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1906.02319.pdf)\n\n    *Jun Wu, Jingrui He, Jiejun Xu.*\n\n1. **Graph Representation Learning via Hard and Channel-Wise Attention Networks.** KDD 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1907.04652)\n\n    *Hongyang Gao, Shuiwang Ji.*\n    \n1. **Graph Learning-Convolutional Networks.** CVPR 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1811.09971.pdf)\n\n    *Bo Jiang, Ziyan Zhang, Doudou Lin, Jin Tang.*\n\n1. **Data Representation and Learning with Graph Diffusion-Embedding Networks.** CVPR 2019. [paper](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPR_2019\u002Fpapers\u002FJiang_Data_Representation_and_Learning_With_Graph_Diffusion-Embedding_Networks_CVPR_2019_paper.pdf)\n\n    *Bo Jiang, Doudou Lin, Jin Tang, Bin Luo.*\n    \n1. **Label Efficient Semi-Supervised Learning via Graph Filtering.** CVPR 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1901.09993.pdf)\n\n    *Qimai Li, Xiao-Ming Wu, Han Liu, Xiaotong Zhang, Zhichao Guan.*\n    \n1. **SPAGAN: Shortest Path Graph Attention Network.** IJCAI 2019. [paper](https:\u002F\u002Fcse.buffalo.edu\u002F~jsyuan\u002Fpapers\u002F2019\u002FSPAGAN_Shortest_Path_Graph_Attention_Network.pdf)\n\n    *Yiding Yang, Xinchao Wang, Mingli Song, Junsong Yuan, Dacheng Tao.*\n    \n1. **Topology Optimization based Graph Convolutional Network.** IJCAI 2019. [paper](https:\u002F\u002Fyangliang.github.io\u002Fpdf\u002Fijcai19_to.pdf)\n\n    *Liang Yang, Zesheng Kang, Xiaochun Cao, Di Jin, Bo Yang, Yuanfang Guo.*\n    \n1. **Hierarchical Graph Convolutional Networks for Semi-supervised Node Classification.** IJCAI 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1902.06667.pdf)\n\n    *Fenyu Hu, Yanqiao Zhu, Shu Wu, Liang Wang, Tieniu Tan.*\n    \n1. **Masked Graph Convolutional Network.** IJCAI 2019. [paper](https:\u002F\u002Fyangliang.github.io\u002Fpdf\u002Fijcai19_mask.pdf)\n\n    *Liang Yang, Fan Wu, Yingkui Wang, Junhua Gu, Yuanfang Guo.*\n    \n1. **Dual Self-Paced Graph Convolutional Network: Towards Reducing Attribute Distortions Induced by Topology.** IJCAI 2019. [paper](https:\u002F\u002Fyangliang.github.io\u002Fpdf\u002Fijcai19_paced.pdf)\n\n    *Liang Yang, Zhiyang Chen, Junhua Gu, Yuanfang Guo.* \n\n1. **Bayesian graph convolutional neural networks for semi-supervised classification.** AAAI 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1811.11103.pdf)\n\n    *Yingxue Zhang, Soumyasundar Pal, Mark Coates, Deniz Üstebay.*\n    \n1. **GeniePath: Graph Neural Networks with Adaptive Receptive Paths.** AAAI 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1802.00910.pdf)\n\n    *Ziqi Liu, Chaochao Chen, Longfei Li, Jun Zhou, Xiaolong Li, Le Song, Yuan Qi.*\n    \n1. **Gaussian-Induced Convolution for Graphs.** AAAI 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1811.04393.pdf)\n\n    *Jiatao Jiang, Zhen Cui, Chunyan Xu, Jian Yang.*\n    \n1. **Fisher-Bures Adversary Graph Convolutional Networks.** UAI 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1903.04154.pdf)\n\n    *Ke Sun, Piotr Koniusz, Zhen Wang.*\n    \n1. **N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification.** UAI 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1802.08888.pdf)\n\n    *Sami Abu-El-Haija, Amol Kapoor, Bryan Perozzi, Joonseok Lee.*\n    \n1. **Confidence-based Graph Convolutional Networks for Semi-Supervised Learning.** AISTATS 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1901.08255.pdf)\n\n    *Shikhar Vashishth, Prateek Yadav, Manik Bhandari, Partha Talukdar.*\n    \n1. **Lovasz Convolutional Networks.** AISTATS 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1805.11365.pdf)\n\n    *Prateek Yadav, Madhav Nimishakavi, Naganand Yadati, Shikhar Vashishth, Arun Rajkumar, Partha Talukdar.* \n\n1. **Provably Powerful Graph Networks.** NeurIPS 2019. [paper](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002Fby-source-2019-1275)\n\n    *Haggai Maron, Heli Ben-Hamu, Hadar Serviansky, Yaron Lipman.*\n\n1. **Graph Agreement Models for Semi-Supervised Learning.** NeurIPS 2019. [paper](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002Fby-source-2019-4699)\n\n\t*Otilia Stretcu, Krishnamurthy Viswanathan, Dana Movshovitz-Attias, Emmanouil Platanios. Sujith Ravi, Andrew Tomkins.*\n\n1. **Graph-Based Semi-Supervised Learning with Non-ignorable Non-response.** NeurIPS 2019. [paper](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002Fby-source-2019-3795)\n\n\t*Fan Zhou, Tengfei Li, Haibo Zhou, Hongtu Zhu, Ye Jieping.*\n\n1. **A Flexible Generative Framework for Graph-based Semi-supervised Learning.** NeurIPS 2019. [paper](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002Fby-source-2019-1831)\n\n\t*Jiaqi Ma, Weijing Tang, Ji Zhu, Qiaozhu Mei.*\n\n1. **Semi-Implicit Graph Variational Auto-Encoders.** NeurIPS 2019. [paper](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002Fby-source-2019-5716)\n\n\t*Arman Hasanzadeh, Ehsan Hajiramezanali, Krishna Narayanan, Nick Duffield, Mingyuan Zhou, Xiaoning Qian.*\n\n1. **Hyperbolic Graph Neural Networks.** NeurIPS 2019. [paper](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002Fby-source-2019-4472)\n\n\t*Qi Liu, Maximilian Nickel, Douwe Kiela.*\n\n1. **Hyperbolic Graph Convolutional Neural Networks.** NeurIPS 2019. [paper](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002Fby-source-2019-2699)\n\n\t*Ines Chami, Zhitao Ying, Christopher Ré, Jure Leskovec.*\n\n1. **Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels.** NeurIPS 2019. [paper](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002Fby-source-2019-3077)\n\n\t*Simon Du, Kangcheng Hou, Russ Salakhutdinov, Barnabas Poczos, Ruosong Wang, Keyulu Xu.*\n\n1. **SNEQ: Semi-supervised Attributed Network Embedding with Attention-based Quantisation.** AAAI 2020. [paper](http:\u002F\u002Fusers.monash.edu\u002F~yli\u002Fassets\u002Fpdf\u002Fsneq-aaai-20.pdf)\n\n\t*Tao He, Lianli Gao, Jingkuan Song, Xin Wang, Kejie Huang, Yuan-­‐Fang Li.*\n\n1. **Going Deep: Graph Convolutional Ladder-Shape Networks.** AAAI 2020. [paper](https:\u002F\u002Fshiruipan.github.io\u002Fpublication\u002Faaai-2020-hu\u002F)\n\n\t*Ruiqi Hu, Shirui Pan, Guodong Long, Qinghua Lu, Liming Zhu, Jing Jiang.*\n\n1. **Co-GCN for Multi-View Semi-Supervised Learning.** AAAI 2020. [paper]()\n\n\t*Shu Li, Wen-­‐Tao Li, Wei Wang.*\n\n1. **Graph Representation Learning via Ladder Gamma Variational Autoencoders.** AAAI 2020. [paper]()\n\n\t*Arindam Sarkar, Nikhil Mehta, Piyush Rai.*\n\n1. **GSSNN: Graph Smoothing Splines Neural Networks.** AAAI 2020. [paper](https:\u002F\u002Fshiruipan.github.io\u002Fpublication\u002Faaai-2020-zhu\u002F)\n\n\t*Shichao Zhu, Lewei Zhou, Shirui Pan, Chuan Zhou, Guiying Yan, Bin Wang.*\n\n1. **Effective Decoding in Graph Auto-Encoder using Triadic Closure.** AAAI 2020. [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1911.11322)\n\n\t*Han Shi, Haozheng Fan, James T. Kwok.*\n\n1. **An Attention-based Graph Neural Network for Heterogeneous Structural Learning.** AAAI 2020. [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1912.10832)\n\n\t*Huiting Hong, Hantao Guo, Yucheng Lin, Xiaoqing Yang, Zang Li, Jieping Ye.*\n\n1. **Fast and Deep Graph Neural Networks.** AAAI 2020. [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1911.08941)\n\n\t*Claudio Gallicchio, Alessio Micheli.*\n\n1. **Hypergraph Label Propagation Network.** AAAI 2020. [paper]()\n\n\t*Yubo Zhang, Nan Wang, Yufeng Chen, Changqing Zou, Hai Wan, Xibin Zhao, Yue Gao.*\n\n1. **Learning Signed Network Embedding via Graph Attention.** AAAI 2020. [paper]()\n\n\t*Yu Li, Yuan Tian, Jiawei Zhang, Yi Chang.*\n\n1. **GraLSP: Graph Neural Networks with Local Structural Patterns.** AAAI 2020. [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1911.07675)\n\n\t*Yilun Jin, Guojie Song, Chuan Shi.*\n\n1. **ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations.** AAAI 2020. [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1911.07979)\n\n\t*Ekagra Ranjan, Soumya Sanyal, Partha Pratim Talukdar.*\n\n1. **Multi‐Stage Self­‐Supervised Learning for Graph Convolutional Networks on Graphs with Few Labeled Nodes.** AAAI 2020. [paper](https:\u002F\u002Fzhouchenlin.github.io\u002FPublications\u002F2020-AAAI-M3S.pdf)\n\n\t*Ke Sun, Zhouchen Lin, Zhanxing Zhu.*\n\n1. **Collaborative Graph Convolutional Networks: Unsupervised Learning Meets Semi-­‐Supervised Learning.** AAAI 2020. [paper]()\n\n\t*Binyuan Hui,  Pengfei Zhu, Qinghua, Hu.*\n\n1. **A Multi­‐Scale Approach for Graph Link Prediction.** AAAI 2020. [paper]()\n\n\t*Lei Cai, Shuiwang Ji.*\n\n1. **Adaptive Structural Fingerprints for Graph Attention Networks.** ICLR 2020. [paper](https:\u002F\u002Fopenreview.net\u002Fpdf?id=BJxWx0NYPr)\n\n\t*Kai Zhang, Yaokang Zhu, Jun Wang, Jie Zhang.*\n\n1. **Strategies for Pre-training Graph Neural Networks.** ICLR 2020. [paper](https:\u002F\u002Fopenreview.net\u002Fpdf?id=HJlWWJSFDH)\n\n\t*Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, Jure Leskovec.*\n\n1. **DropEdge: Towards Deep Graph Convolutional Networks on Node Classification.** ICLR 2020. [paper](https:\u002F\u002Fopenreview.net\u002Fpdf?id=Hkx1qkrKPr)\n\n\t*Yu Rong, Wenbing Huang, Tingyang Xu, Junzhou Huang.*\n\n1. **Directional Message Passing for Molecular Graphs.** ICLR 2020. [paper](https:\u002F\u002Fopenreview.net\u002Fpdf?id=B1eWbxStPH)\n\n\t*Johannes Klicpera, Janek Groß, Stephan Günnemann.*\n\n1. **DeepSphere: a graph-based spherical CNN.** ICLR 2020. [paper](https:\u002F\u002Fopenreview.net\u002Fpdf?id=B1e3OlStPB)\n\n\t*Michaël Defferrard, Martino Milani, Frédérick Gusset, Nathanaël Perraudin.*\n\n1. **Geom-GCN: Geometric Graph Convolutional Networks.** ICLR 2020. [paper](https:\u002F\u002Fopenreview.net\u002Fpdf?id=S1e2agrFvS)\n\n\t*Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, Bo Yang.*\n\n1. **Curvature Graph Network.** ICLR 2020. [paper](https:\u002F\u002Fopenreview.net\u002Fpdf?id=BylEqnVFDB)\n\n\t*Ze Ye, Kin Sum Liu, Tengfei Ma, Jie Gao, Chao Chen.*\n\n1. **Measuring and Improving the Use of Graph Information in Graph Neural Networks.** ICLR 2020. [paper](https:\u002F\u002Fopenreview.net\u002Fpdf?id=rkeIIkHKvS)\n\n\t*Yifan Hou, Jian Zhang, James Cheng, Kaili Ma, Richard T. B. Ma, Hongzhi Chen, Ming-Chang Yang.*\n\n1. **Memory-Based Graph Networks.** ICLR 2020. [paper](https:\u002F\u002Fopenreview.net\u002Fpdf?id=r1laNeBYPB)\n\n\t*Amir Hosein Khasahmadi, Kaveh Hassani, Parsa Moradi, Leo Lee, Quaid Morris.*\n\n1. **Pruned Graph Scattering Transforms.** ICLR 2020. [paper](https:\u002F\u002Fopenreview.net\u002Fpdf?id=rJeg7TEYwB)\n\n\t*Vassilis N. Ioannidis, Siheng Chen, Georgios B. Giannakis.*\n\n1. **Neural Execution of Graph Algorithms.** ICLR 2020. [paper](https:\u002F\u002Fopenreview.net\u002Fpdf?id=SkgKO0EtvS)\n\n\t*Petar Veličković, Rex Ying, Matilde Padovano, Raia Hadsell, Charles Blundell.*\n\n1. **GraphSAINT: Graph Sampling Based Inductive Learning Method.** ICLR 2020. [paper](https:\u002F\u002Fopenreview.net\u002Fpdf?id=BJe8pkHFwS)\n\n\t*Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, Viktor Prasanna.*\n\n1. **Graph inference learning for semi-supervised classification.** ICLR 2020. [paper](https:\u002F\u002Fopenreview.net\u002Fpdf?id=r1evOhEKvH)\n\n\t*Chunyan Xu, Zhen Cui, Xiaobin Hong, Tong Zhang, Jian Yang, Wei Liu.*\n\n1. **SGAS: Sequential Greedy Architecture Search.** CVPR 2020. [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1912.00195)\n\n\t*Guohao Li, Guocheng Qian, Itzel C. Delgadillo, Matthias Müller, Ali Thabet, Bernard Ghanem.*\n\n1. **Adaptive Propagation Graph Convolutional Network.** IEEE TNNLS 2020. [paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9206045)\n\n\t*Indro Spinelli, Simone Scardapane, Aurelio Uncini.*\n\n     \u003C\u002Fdetails>\n\n### [Graph Types](#content)\n1. **DyRep: Learning Representations over Dynamic Graphs.** ICLR 2019. [paper](https:\u002F\u002Fopenreview.net\u002Fpdf?id=HyePrhR5KX)\n\n    *Rakshit Trivedi, Mehrdad Farajtabar, Prasenjeet Biswal, Hongyuan Zha.*\n\n1. **Hypergraph Neural Networks.** AAAI 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1809.09401.pdf)\n\n    *Yifan Feng, Haoxuan You, Zizhao Zhang, Rongrong Ji, Yue Gao.*\n\n1. **Heterogeneous Graph Attention Network.** WWW 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1903.07293.pdf)\n\n    *Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Peng Cui, P. Yu, Yanfang Ye.*\n    \n1. **Representation Learning for Attributed Multiplex Heterogeneous Network.** KDD 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.01669.pdf)\n\n    *Yukuo Cen, Xu Zou, Jianwei Zhang, Hongxia Yang, Jingren Zhou, Jie Tang.*\n    \n1. **ActiveHNE: Active Heterogeneous Network Embedding.** IJCAI 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.05659.pdf)\n   \n    *Xia Chen, Guoxian Yu, Jun Wang, Carlotta Domeniconi, Zhao Li, Xiangliang Zhang.*\n    \n1. **GCN-LASE: Towards Adequately Incorporating Link Attributes in Graph Convolutional Networks.** IJCAI 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1902.09817.pdf)\n\n    *Ziyao Li, Liang Zhang, Guojie Song.*\n    \n1. **Dynamic Hypergraph Neural Networks.** IJCAI 2019. [paper](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2019\u002F0366.pdf)\n\n    *Jianwen Jiang, Yuxuan Wei, Yifan Feng, Jingxuan Cao, Yue Gao.*\n    \n1. **Exploiting Interaction Links for Node Classification with Deep Graph Neural Networks.** IJCAI 2019. [paper](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2019\u002F0447.pdf)\n\n    *Hogun Park, Jennifer Neville.*\n    \n1. **Exploiting Edge Features in Graph Neural Networks.** CVPR 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1809.02709.pdf)\n\n    *Liyu Gong, Qiang Cheng.*  \n\n1. **HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs.** NeurIPS 2019. [paper](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002Fby-source-2019-850)\n\n\t*Naganand Yadati, Madhav Nimishakavi, Prateek Yadav, Vikram Nitin, Anand Louis, Partha Talukdar.*\n\n1. **Graph Transformer Networks.** NeurIPS 2019. [paper](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002Fby-source-2019-6458)\n\n\t*Seongjun Yun, Minbyul Jeong, Raehyun Kim, Jaewoo Kang, Hyunwoo Kim.*\n\n1. **Recurrent Space-time Graph Neural Networks.** NeurIPS 2019. [paper](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002Fby-source-2019-6993)\n\n\t*Andrei Nicolicioiu, Iulia Duta, Marius Leordeanu.*\n\n1. **EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs.** AAAI 2020. [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1902.10191)\n\n\t*Aldo Pareja, Giacomo Domeniconi, Jie Chen, Tengfei Ma, Toyotaro Suzumura, Hiroki Kanezashi, Tim Kaler, Tao B. Schardl, Charles E. Leiserson.*\n\n1. **Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting.** AAAI 2020. [paper](https:\u002F\u002Fgithub.com\u002FDavidham3\u002FSTSGCN\u002Fblob\u002Fmaster\u002Fpaper\u002FAAAI2020-STSGCN.pdf)\n\n\t*Chao Song, Youfang Lin, Shengnan Guo, Huaiyu Wan.*\n\n1. **Type-aware Anchor Link Prediction across Heterogeneous Networks based on Graph Attention Network.** AAAI 2020. [paper]()\n\n\t*Xiaoxue Li, Yanmin Shang, Yanan Cao, Yangxi Li, Jianlong Tan, Yanbing Liu.*    \n\n1. **Composition-based Multi-Relational Graph Convolutional Networks.** ICLR 2020. [paper](https:\u002F\u002Fopenreview.net\u002Fpdf?id=BylA_C4tPr)\n\n\t*Shikhar Vashishth, Soumya Sanyal, Vikram Nitin, Partha Talukdar.*\n\n1. **Inductive representation learning on temporal graphs.** ICLR 2020. [paper](https:\u002F\u002Fopenreview.net\u002Fpdf?id=rJeW1yHYwH)\n\n\t*da Xu, chuanwei ruan, evren korpeoglu, sushant kumar, kannan achan.*\n\n1. **Hyper-SAGNN: a self-attention based graph neural network for hypergraphs.** ICLR 2020. [paper](https:\u002F\u002Fopenreview.net\u002Fpdf?id=ryeHuJBtPH)\n\n\t*Ruochi Zhang, Yuesong Zou, Jian Ma.*\n\n1. **Digraph Inception Convolutional Networks.** NeurIPS 2020. [paper](https:\u002F\u002Fproceedings.neurips.cc\u002F\u002Fpaper\u002F2020\u002Ffile\u002Fcffb6e2288a630c2a787a64ccc67097c-Paper.pdf)\n\n\t*Zekun Tong, Yuxuan Liang, Changsheng Sun, Xinke Li, David S. Rosenblum, Andrew Lim.*\n\n1. **Subgraph Neural Networks.** NeurIPS 2020. [paper](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2020\u002Fhash\u002F5bca8566db79f3788be9efd96c9ed70d-Abstract.html)\n\n    *Emily Alsentzer, Samuel Finlayson, Michelle Li, Marinka Zitnik.*\n    \n1. **Dynamic Graph Convolutional Networks Using the Tensor M-Product.** SDM 2021. [paper](https:\u002F\u002Fdoi.org\u002F10.1137\u002F1.9781611976700.82)\n\n    *Osman Asif Malik, Shashanka Ubaru, Lior Horesh, Misha E. Kilmer, Haim Avron*\n\n### [Pooling Methods](#content)\n1. **An End-to-End Deep Learning Architecture for Graph Classification.** AAAI 2018. [paper](https:\u002F\u002Fwww.aaai.org\u002Focs\u002Findex.php\u002FAAAI\u002FAAAI18\u002Fpaper\u002Fview\u002F17146\u002F16755)\n\n    *Muhan Zhang, Zhicheng Cui, Marion Neumann, Yixin Chen.*\n\n1. **Hierarchical Graph Representation Learning with Differentiable Pooling.** NeurIPS 2018. [paper](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F7729-hierarchical-graph-representation-learning-with-differentiable-pooling.pdf)\n\n    *Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, Will Hamilton, Jure Leskovec.*\n\n1. **Self-Attention Graph Pooling.** ICML 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1904.08082)\n\n    *Junhyun Lee, Inyeop Lee, Jaewoo Kang.*\n\n1. **Graph U-Nets.** ICML 2019. [paper](http:\u002F\u002Fproceedings.mlr.press\u002Fv97\u002Fgao19a\u002Fgao19a.pdf)\n\n    *Hongyang Gao, Shuiwang Ji.*\n    \n1. **Graph Convolutional Networks with EigenPooling.** KDD 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1904.13107.pdf)\n\n    *Yao Ma, Suhang Wang, Charu C. Aggarwal, Jiliang Tang.*\n    \n1. **Relational Pooling for Graph Representations.** ICML 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1903.02541)\n\n    *Ryan L. Murphy, Balasubramaniam Srinivasan, Vinayak Rao, Bruno Ribeiro.*\n\n1. **Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks.** NeurIPS 2019. [paper](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002Fby-source-2019-5861)\n\n\t*Sitao Luan, Mingde Zhao, Xiao-Wen Chang, Doina Precup.*\n\n1. **Diffusion Improves Graph Learning.** NeurIPS 2019. [paper](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F9490-diffusion-improves-graph-learning)\n\n\t*Johannes Klicpera, Stefan Weißenberger, Stephan Günnemann.*\n\n1. **Hierarchical Graph Pooling with Structure Learning.** AAAI 2020. [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1911.05954)\n\n\t*Zhen Zhang, Jiajun Bu, Martin Ester, Jianfeng Zhang, Chengwei Yao, Zhi Yu, Can Wang.*\n\n1. **StructPool: Structured Graph Pooling via Conditional Random Fields.** ICLR 2020. [paper](https:\u002F\u002Fopenreview.net\u002Fpdf?id=BJxg_hVtwH)\n\n\t*Hao Yuan, Shuiwang Ji.*\n\n1. **Spectral Clustering with Graph Neural Networks for Graph Pooling.** ICML 2020. [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1907.00481)\n\n\t*Filippo Maria Bianchi, Daniele Grattarola, Cesare Alippi.*\n\n1. **Accurate Learning of Graph Representations with Graph Multiset Pooling.** ICLR 2021. [paper](https:\u002F\u002Fopenreview.net\u002Fpdf?id=JHcqXGaqiGn)\n\n\t*Jinheon Baek, Minki Kang, Sung Ju Hwang.*\n\n### [Analysis](#content)\n1. **A Comparison between Recursive Neural Networks and Graph Neural Networks.** IJCNN 2006. [paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=1716174)\n\n    *Vincenzo Di Massa, Gabriele Monfardini, Lorenzo Sarti, Franco Scarselli, Marco Maggini, Marco Gori.*\n    \n1. **Neural networks for relational learning: an experimental comparison.** Machine Learning 2011. [paper](https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007%2Fs10994-010-5196-5.pdf)\n\n    *Werner Uwents, Gabriele Monfardini, Hendrik Blockeel, Marco Gori, Franco Scarselli.*\n    \n1. **Mean-field theory of graph neural networks in graph partitioning.** NeurIPS 2018. [paper](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F7689-mean-field-theory-of-graph-neural-networks-in-graph-partitioning.pdf)\n\n    *Tatsuro Kawamoto, Masashi Tsubaki, Tomoyuki Obuchi.*\n\n1. **Representation Learning on Graphs with Jumping Knowledge Networks.** ICML 2018. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1806.03536.pdf)\n\n    *Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, Stefanie Jegelka.* \n    \n1. **Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning.** AAAI 2018. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1801.07606.pdf)\n   \n    *Qimai Li, Zhichao Han, Xiao-Ming Wu.*\n\n1. **How Powerful are Graph Neural Networks?** ICLR 2019. [paper](https:\u002F\u002Fopenreview.net\u002Fpdf?id=ryGs6iA5Km)\n\n    *Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka.*\n\n1. **Stability and Generalization of Graph Convolutional Neural Networks.** KDD 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.01004.pdf)\n\n    *Saurabh Verma, Zhi-Li Zhang.*\n\n1. **Simplifying Graph Convolutional Networks.** ICML 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1902.07153)\n\n    *Felix Wu, Tianyi Zhang, Amauri Holanda de Souza Jr., Christopher Fifty, Tao Yu, Kilian Q. Weinberger.*\n\n1. **Explainability Methods for Graph Convolutional Neural Networks.** CVPR 2019. [paper](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPR_2019\u002Fpapers\u002FPope_Explainability_Methods_for_Graph_Convolutional_Neural_Networks_CVPR_2019_paper.pdf)\n\n    *Phillip E. Pope, Soheil Kolouri, Mohammad Rostami, Charles E. Martin, Heiko Hoffmann.*\n\n1. **Can GCNs Go as Deep as CNNs?** ICCV 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1904.03751.pdf)\n\n    *Guohao Li, Matthias Müller, Ali Thabet, Bernard Ghanem.*\n\n1. **Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks.** AAAI 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1810.02244.pdf)    \n\n    *Christopher Morris, Martin Ritzert, Matthias Fey, William L. Hamilton, Jan Eric Lenssen, Gaurav Rattan, Martin Grohe.*\n\n1. **Understanding Attention and Generalization in Graph Neural Networks.** NeurIPS 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.02850.pdf)\n\n    *Boris Knyazev, Graham W. Taylor, Mohamed R. Amer.*\n\n1. **GNNExplainer: Generating Explanations for Graph Neural Networks.** NeurIPS 2019. [paper](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002Fby-source-2019-4956)\n\n\t*Zhitao Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, Jure Leskovec.*\n\n1. **Universal Invariant and Equivariant Graph Neural Networks.** NeurIPS 2019. [paper](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002Fby-source-2019-3832)\n\n\t*Nicolas Keriven, Gabriel Peyré.*\n\n1. **On the equivalence between graph isomorphism testing and function approximation with GNNs.** NeurIPS 2019. [paper](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002Fby-source-2019-9347)\n\n\t*Zhengdao Chen, Soledad Villar, Lei Chen, Joan Bruna.*\n\n1. **Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology.** NeurIPS 2019. [paper](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002Fby-source-2019-8876)\n\n\t*Nima Dehmamy, Albert-Laszlo Barabasi, Rose Yu.*\n\n1. **Graph Neural Networks Exponentially Lose Expressive Power for Node Classification.** ICLR 2020. [paper](https:\u002F\u002Fopenreview.net\u002Fpdf?id=S1ldO2EFPr)\n\n\t*Kenta Oono, Taiji Suzuki.*\n\n1. **What graph neural networks cannot learn: depth vs width.** ICLR 2020. [paper](https:\u002F\u002Fopenreview.net\u002Fpdf?id=B1l2bp4YwS)\n\n\t*Andreas Loukas.*\n\n1. **The Logical Expressiveness of Graph Neural Networks.** ICLR 2020. [paper](https:\u002F\u002Fopenreview.net\u002Fpdf?id=r1lZ7AEKvB)\n\n\t*Pablo Barceló, Egor V. Kostylev, Mikael Monet, Jorge Pérez, Juan Reutter, Juan Pablo Silva.*\n\n1. **On the Equivalence between Positional Node Embeddings and Structural Graph Representations.** ICLR 2020. [paper](https:\u002F\u002Fopenreview.net\u002Fpdf?id=SJxzFySKwH)\n\n\t*Balasubramaniam Srinivasan, Bruno Ribeiro.*\n\t\n1. **Can Graph Neural Networks Count Substructures?** NeurIPS 2020. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2002.04025.pdf)\n\n\t*Zhengdao Chen, Lei Chen, Soledad Villar, Joan Bruna.*\n\n### [Efficiency](#content)\n1. **Stochastic Training of Graph Convolutional Networks with Variance Reduction.** ICML 2018. [paper](http:\u002F\u002Fwww.scipaper.net\u002Fuploadfile\u002F2018\u002F0716\u002F20180716100330880.pdf)\n   \n    *Jianfei Chen, Jun Zhu, Le Song.*\n    \n1. **FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling.** ICLR 2018. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1801.10247.pdf)\n\n    *Jie Chen, Tengfei Ma, Cao Xiao.*\n    \n1. **Adaptive Sampling Towards Fast Graph Representation Learning.** NeurIPS 2018. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1809.05343.pdf)\n\n    *Wenbing Huang, Tong Zhang, Yu Rong, Junzhou Huang.*\n    \n1. **Large-Scale Learnable Graph Convolutional Networks.** KDD 2018. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1808.03965.pdf)\n\n    *Hongyang Gao, Zhengyang Wang, Shuiwang Ji.*\n    \n1. **Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks.** KDD 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.07953.pdf)\n\n    *Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, Cho-Jui Hsieh.*\n    \n1. **A Degeneracy Framework for Scalable Graph Autoencoders.** IJCAI 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1902.08813.pdf)\n\n    *Guillaume Salha, Romain Hennequin, Viet Anh Tran, Michalis Vazirgiannis.*\n\n1. **Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks.** NeurIPS 2019. [paper](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002Fby-source-2019-6006)\n\n\t*Difan Zou, Ziniu Hu, Yewen Wang, Song Jiang, Yizhou Sun, Quanquan Gu.*\n\n1. **GraphSAINT: Graph Sampling Based Inductive Learning Method.** ICLR 2020. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1907.04931.pdf) [code](https:\u002F\u002Fgithub.com\u002FGraphSAINT\u002FGraphSAINT)\n\n    *Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, Viktor Prasanna.*\n\n1. **Scalable Graph Convolutional Network Based Link Prediction on a Distributed Graph Database Server.** IEEE CLOUD 2020. [paper](https:\u002F\u002Fgithub.com\u002Fmiyurud\u002Fmiyurud.github.io\u002Fblob\u002Fmaster\u002Fpapers\u002F2020\u002FIEEE_CLOUD_2020_JasmineGraph-web.pdf) [code](https:\u002F\u002Fgithub.com\u002Fmiyurud\u002Fjasminegraph)\n\n    *Anuradha Karunarathna, Dinika Senarath, Shalika Madhushanki, Chinthaka Weerakkody, Miyuru Dayarathna, Sanath Jayasena, Toyotaro Suzumura.*\n\n\n1. **Memory Efficient Graph Convolutional Networkbased Distributed Link Prediction.** IEEE Big Data 2020. [paper](https:\u002F\u002Fraw.githubusercontent.com\u002Fmiyurud\u002Fmiyurud.github.io\u002Fmaster\u002Fpapers\u002F2020\u002FIEEE_BigData_Workshop_2020_JasmineGraph_web.pdf) [code](https:\u002F\u002Fgithub.com\u002Fmiyurud\u002Fjasminegraph)\n\n    *Damitha Senevirathne, Isuru Wijesiri, Suchitha Dehigaspitiya, Miyuru Dayarathna, Sanath Jayasena, and Toyotaro Suzumura.*\n\n\n### [Explainability](#content)\n\n1. **Explainability Techniques for Graph Convolutional Networks.** ICML 2019. [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1905.13686)\n\n    *Federico Baldassarre, Hossein Azizpour.*\n\n1. **GNNExplainer: Generating Explanations for Graph Neural Networks.** NeurIPS 2019. [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1903.03894)\n\n\t*Rex Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, Jure Leskovec.*\n\n1. **GCN-LRP Explanation: Exploring Latent Attention of Graph Convolutional Networks.** IJCNN 2020. [paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9207639)\n\n    *Jinlong Hu, Tenghui Li, Shoubin Dong.*\n\n1. **Parameterized Explainer for Graph Neural Network.** NeurIPS 2020. [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2011.04573)\n\n\t*Dongsheng Luo, Wei Cheng, Dongkuan Xu, Wenchao Yu, Bo Zong, Haifeng Chen, Xiang Zhang.*\n\n1. **XGNN: Towards Model-Level Explanations of Graph Neural Networks.** KDD 2020. [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.02587)\n\n\t*Hao Yuan, Jiliang Tang, Xia Hu, Shuiwang Ji.*\n\n1. **Contrastive Graph Neural Network Explanation.** ICML 2020. [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.13663)\n\n\t*Lukas Faber, Amin K. Moghaddam, Roger Wattenhofer.*\n\n1. **Interpreting Graph Neural Networks for NLP With Differentiable Edge Maskin.** ICLR 2021. [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.00577)\n\n\t*Michael Sejr Schlichtkrull, Nicola De Cao, Ivan Titov.*\n\n1. **On Explainability of Graph Neural Networks via Subgraph Explorations.** ICML 2021. [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.05152)\n\n\t*Hao Yuan, Haiyang Yu, Jie Wang, Kang Li, Shuiwang Ji.*\n\n1. **Generative Causal Explanations for Graph Neural Networks.** ICML 2021. [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2104.06643)\n\n\t*Wanyu Lin, Hao Lan, Baochun Li.*\n\n1. **GraphSVX: Shapley Value Explanations for Graph Neural Networks.** ECML PKDD 2021. [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2104.10482)\n\n\t*Alexandre Duval, Fragkiskos D. Malliaros.*\n\n\n## [Applications](#content)\n\n### [Physics](#content)\n\n1. **Discovering objects and their relations from entangled scene representations.** ICLR Workshop 2017. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1702.05068.pdf)\n\n    *David Raposo, Adam Santoro, David Barrett, Razvan Pascanu, Timothy Lillicrap, Peter Battaglia.*  \n\n1. **A simple neural network module for relational reasoning.** NIPS 2017. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1706.01427.pdf)\n\n    *Adam Santoro, David Raposo, David G.T. Barrett, Mateusz Malinowski, Razvan Pascanu, Peter Battaglia, Timothy Lillicrap.*  \n\n1. **Interaction Networks for Learning about Objects, Relations and Physics.** NIPS 2016. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1612.00222.pdf)\n\n    *Peter Battaglia, Razvan Pascanu, Matthew Lai, Danilo Rezende, Koray Kavukcuoglu.* \n    \n1. **Visual Interaction Networks: Learning a Physics Simulator from Video.** NIPS 2017. [paper](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F7040-visual-interaction-networks-learning-a-physics-simulator-from-video.pdf)\n\n    *Nicholas Watters, Andrea Tacchetti, Théophane Weber, Razvan Pascanu, Peter Battaglia, Daniel Zoran.* \n\n1. **Graph networks as learnable physics engines for inference and control.** ICML 2018. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1806.01242.pdf)\n\n    *Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg, Josh Merel, Martin Riedmiller, Raia Hadsell, Peter Battaglia.* \n\n1. **Learning Multiagent Communication with Backpropagation.** NIPS 2016. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1605.07736.pdf)\n\n    *Sainbayar Sukhbaatar, Arthur Szlam, Rob Fergus.* \n\n1. **VAIN: Attentional Multi-agent Predictive Modeling.** NIPS 2017 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1706.06122.pdf)\n\n    *Yedid Hoshen.* \n\n1. **Neural Relational Inference for Interacting Systems.** ICML 2018. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1802.04687.pdf)\n\n    *Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, Richard Zemel.* \n\n1. **Graph Element Networks: adaptive, structured computation and memory.** ICML 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1904.09019)\n\n    *Ferran Alet, Adarsh K. Jeewajee, Maria Bauza, Alberto Rodriguez, Tomas Lozano-Perez, Leslie Pack Kaelbling.*\n\n1. **Physics-aware Difference Graph Networks for Sparsely-Observed Dynamics.** ICLR 2020. [paper](https:\u002F\u002Fopenreview.net\u002Fpdf?id=r1gelyrtwH)\n\n\t*Sungyong Seo, Chuizheng Meng, Yan Liu.*\n\n### [Chemistry and Biology](#content)\n\n1. **Convolutional networks on graphs for learning molecular fingerprints.** NIPS 2015. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1509.09292.pdf)\n\n    *David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael Gómez-Bombarelli, Timothy Hirzel, Alán Aspuru-Guzik, Ryan P. Adams.* \n\n1. **Molecular Graph Convolutions: Moving Beyond Fingerprints.** Journal of computer-aided molecular design 2016. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1603.00856.pdf)\n\n    *Steven Kearnes, Kevin McCloskey, Marc Berndl, Vijay Pande, Patrick Riley.* \n\n1. **Protein Interface Prediction using Graph Convolutional Networks.** NIPS 2017. [paper](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F7231-protein-interface-prediction-using-graph-convolutional-networks.pdf)\n\n    *Alex Fout, Jonathon Byrd, Basir Shariat, Asa Ben-Hur.* \n\n1. **Hybrid Approach of Relation Network and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classification.** IJCAI 2018. [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.05859)\n\n    *Sungmin Rhee, Seokjun Seo, Sun Kim.*\n\n1. **Modeling polypharmacy side effects with graph convolutional networks.** ISMB 2018. [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1802.00543)\n\n    *Marinka Zitnik, Monica Agrawal, Jure Leskovec.*\n\n1. **Spectral Multigraph Networks for Discovering and Fusing Relationships in Molecules.** NeurIPS Workshop 2018. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1811.09595.pdf)\n\n    *Boris Knyazev, Xiao Lin, Mohamed R. Amer, Graham W. Taylor.*\n\n1. **MR-GNN: Multi-Resolution and Dual Graph Neural Network for Predicting Structured Entity Interactions.** IJCAI 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.09558.pdf)\n\n    *Nuo Xu, Pinghui Wang, Long Chen, Jing Tao, Junzhou Zhao.*\n    \n1. **Pre-training of Graph Augmented Transformers for Medication Recommendation.** IJCAI 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1906.00346.pdf)\n\n    *Junyuan Shang, Tengfei Ma, Cao Xiao, Jimeng Sun.*\n\n1. **GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination.** AAAI 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1809.01852.pdf)\n\n    *Junyuan Shang, Cao Xiao, Tengfei Ma, Hongyan Li, Jimeng Sun.*\n    \n1. **AffinityNet: semi-supervised few-shot learning for disease type prediction.** AAAI 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1805.08905.pdf)\n\n    *Tianle Ma, Aidong Zhang.*\n\n1. **Graph Transformation Policy Network for Chemical Reaction Prediction.** KDD 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1812.09441)\n\n    *Kien Do, Truyen Tran, Svetha Venkatesh.*\n\n1. **Functional Transparency for Structured Data: a Game-Theoretic Approach.** ICML 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1902.09737)\n\n    *Guang-He Lee, Wengong Jin, David Alvarez-Melis, Tommi S. Jaakkola.*\n\n1. **Learning Multimodal Graph-to-Graph Translation for Molecular Optimization.** ICLR 2019. [paper](https:\u002F\u002Fopenreview.net\u002Fpdf?id=B1xJAsA5F7)\n\n    *Wengong Jin, Kevin Yang, Regina Barzilay, Tommi Jaakkola.*\n\n1. **A Generative Model For Electron Paths.** ICLR 2019. [paper](https:\u002F\u002Fopenreview.net\u002Fpdf?id=r1x4BnCqKX)\n\n    *John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato.*\n\n1. **Retrosynthesis Prediction with Conditional Graph Logic Network.** NeurIPS 2019. [paper](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002Fby-source-2019-4761)\n\n\t*Hanjun Dai, Chengtao Li, Connor Coley, Bo Dai, Le Song.*\n\n1. **Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer.** AAAI 2020. [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1906.04716)\n\n\t*Edward Choi, Zhen Xu, Yujia Li, Michael W. Dusenberry, Gerardo Flores, Yuan Xue, Andrew M. Dai.*\n\n### [Knowledge Graph](#content)\n1. **Modeling Relational Data with Graph Convolutional Networks.** ESWC 2018. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1703.06103.pdf)\n\n    *Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, Max Welling.*\n\n1. **Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks.** EMNLP 2018. [paper](http:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FD18-1032)\n\n    *Zhichun Wang, Qingsong Lv, Xiaohan Lan, Yu Zhang.*\n\n1. **Representation learning for visual-relational knowledge graphs.** arxiv 2017. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1709.02314.pdf)\n\n    *Daniel Oñoro-Rubio, Mathias Niepert, Alberto García-Durán, Roberto González, Roberto J. López-Sastre.*\n    \n1. **End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completion.** AAAI 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1811.04441.pdf)\n\n    *Chao Shang, Yun Tang, Jing Huang, Jinbo Bi, Xiaodong He, Bowen Zhou.*\n    \n1. **Knowledge Transfer for Out-of-Knowledge-Base Entities : A Graph Neural Network Approach.** IJCAI 2017. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1706.05674.pdf)\n\n    *Takuo Hamaguchi, Hidekazu Oiwa, Masashi Shimbo, Yuji Matsumoto.* \n\n1. **Logic Attention Based Neighborhood Aggregation for Inductive Knowledge Graph Embedding.** AAAI 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1811.01399.pdf)\n\n    *Peifeng Wang, Jialong Han, Chenliang Li, Rong Pan.*\n\n1. **Dynamic Graph Generation Network: Generating Relational Knowledge from Diagrams.** CVPR 2018. [paper](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_cvpr_2018\u002Fpapers\u002FKim_Dynamic_Graph_Generation_CVPR_2018_paper.pdf)\n\n    *Haoyu Wang, Defu Lian, Yong Ge.*\n\n1. **Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks.** KDD 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.08865)\n\n    *Namyong Park, Andrey Kan, Xin Luna Dong, Tong Zhao, Christos Faloutsos.*\n\n1. **OAG: Toward Linking Large-scale Heterogeneous Entity Graphs.** KDD 2019. [paper](http:\u002F\u002Fkeg.cs.tsinghua.edu.cn\u002Fjietang\u002Fpublications\u002FKDD19-Zhang-et-al-Open_Academic_Graph.pdf)\n\n    *Fanjin Zhang, Xiao Liu, Jie Tang, Yuxiao Dong, Peiran Yao, Jie Zhang, Xiaotao Gu, Yan Wang, Bin Shao, Rui Li, Kuansan Wang.*\n\n1. **Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs.** ACL 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1906.01195)\n\n    *Deepak Nathani, Jatin Chauhan, Charu Sharma, Manohar Kaul.*\n\n1. **Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Network.** ACL 2019. [paper](https:\u002F\u002F128.84.21.199\u002Fpdf\u002F1905.11605)\n\n    *Kun Xu, Mo Yu, Yansong Feng, Yan Song, Zhiguo Wang, Dong Yu.*\n\n1. **Multi-relational Poincaré Graph Embeddings.** NeurIPS 2019. [paper](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002Fby-source-2019-2511)\n\n\t*Ivana Balazevic, Carl Allen, Timothy Hospedales.*\n\n1. **Dynamically Pruned Message Passing Networks for Large-scale Knowledge Graph Reasoning.** ICLR 2020. [paper](https:\u002F\u002Fopenreview.net\u002Fpdf?id=rkeuAhVKvB)\n\n\t*Xiaoran Xu, Wei Feng, Yunsheng Jiang, Xiaohui Xie, Zhiqing Sun, Zhi-Hong Deng.*\n\n1. **Efficient Probabilistic Logic Reasoning with Graph Neural Networks.** ICLR 2020. [paper](https:\u002F\u002Fopenreview.net\u002Fpdf?id=rJg76kStwH)\n\n\t*Yuyu Zhang, Xinshi Chen, Yuan Yang, Arun Ramamurthy, Bo Li, Yuan Qi, Le Song.*\n\n### [Recommender Systems](#content)\n\n1. **Graph Convolutional Neural Networks for Web-Scale Recommender Systems.** KDD 2018. [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.01973)\n\n    *Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, Jure Leskovec.*\n\n1. **Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks.** NIPS 2017. [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1704.06803)\n\n    *Federico Monti, Michael M. Bronstein, Xavier Bresson.*\n\n1. **Graph Convolutional Matrix Completion.** 2017. [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.02263)\n\n    *Rianne van den Berg, Thomas N. Kipf, Max Welling.*\n\n1. **STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems.** IJCAI 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.13129.pdf)\n\n    *Jiani Zhang, Xingjian Shi, Shenglin Zhao, Irwin King.*\n    \n1. **Binarized Collaborative Filtering with Distilling Graph Convolutional Networks.** IJCAI 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1906.01829.pdf)\n\n    *Haoyu Wang, Defu Lian, Yong Ge.*\n    \n1. **Graph Contextualized Self-Attention Network for Session-based Recommendation.** IJCAI 2019. [paper](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2019\u002F0547.pdf)\n   \n    *Chengfeng Xu, Pengpeng Zhao, Yanchi Liu, Victor S. Sheng, Jiajie Xu, Fuzhen Zhuang, Junhua Fang, Xiaofang Zhou.*\n\n1. **Session-based Recommendation with Graph Neural Networks.** AAAI 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1811.00855.pdf)\n\n    *Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, Tieniu Tan.*\n    \n1. **Geometric Hawkes Processes with Graph Convolutional Recurrent Neural Networks.** AAAI 2019. [paper](https:\u002F\u002Fjshang2.github.io\u002Fpubs\u002Fgeo.pdf)\n\n    *Jin Shang, Mingxuan Sun.*\n\n1. **Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems.** KDD 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.04413)\n\n    *Hongwei Wang, Fuzheng Zhang, Mengdi Zhang, Jure Leskovec, Miao Zhao, Wenjie Li, Zhongyuan Wang.*\n\n1. **Exact-K Recommendation via Maximal Clique Optimization.** KDD 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.07089)\n\n    *Yu Gong, Yu Zhu, Lu Duan, Qingwen Liu, Ziyu Guan, Fei Sun, Wenwu Ou, Kenny Q. Zhu.*\n\n1. **KGAT: Knowledge Graph Attention Network for Recommendation.** KDD 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.07854)\n\n    *Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, Tat-Seng Chua.*\n    \n1. **Knowledge Graph Convolutional Networks for Recommender Systems.** WWW 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1904.12575.pdf)\n\n    *Hongwei Wang, Miao Zhao, Xing Xie, Wenjie Li, Minyi Guo.*\n    \n1. **Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems.** WWW 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1903.10433.pdf)\n\n    *Qitian Wu, Hengrui Zhang, Xiaofeng Gao, Peng He, Paul Weng, Han Gao, Guihai Chen.*\n    \n1. **Graph Neural Networks for Social Recommendation.** WWW 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1902.07243.pdf)\n\n    *Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, Dawei Yin.*\n\n1. **Memory Augmented Graph Neural Networks for Sequential Recommendation.** AAAI 2020. [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1912.11730)\n\n\t*Chen Ma, Liheng Ma, Yingxue Zhang, Jianing Sun, Xue Liu, Mark Coates.*\n\n1. **Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach.** AAAI 2020. [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2001.10167)\n\n\t*Lei Chen, Le Wu, Richang Hong, Kun Zhang, Meng Wang.*\n\n1. **Inductive Matrix Completion Based on Graph Neural Networks.** ICLR 2020. [paper](https:\u002F\u002Fopenreview.net\u002Fpdf?id=ByxxgCEYDS)\n\n\t*Muhan Zhang, Yixin Chen.*\n\n### [Computer Vision](#content)\n1. **Graph Neural Networks for Object Localization.** ECAI 2006. [paper](http:\u002F\u002Febooks.iospress.nl\u002Fvolumearticle\u002F2775)\n\n    *Gabriele Monfardini, Vincenzo Di Massa, Franco Scarselli, Marco Gori.*\n\n1. **Learning Human-Object Interactions by Graph Parsing Neural Networks.** ECCV 2018. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1808.07962.pdf)\n\n    *Siyuan Qi, Wenguan Wang, Baoxiong Jia, Jianbing Shen, Song-Chun Zhu.*\n\n1. **Learning Conditioned Graph Structures for Interpretable Visual Question Answering.** NeurIPS 2018. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1806.07243)\n\n    *Will Norcliffe-Brown, Efstathios Vafeias, Sarah Parisot.*\n\n1. **Symbolic Graph Reasoning Meets Convolutions.** NeurIPS 2018. [paper](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F7456-symbolic-graph-reasoning-meets-convolutions.pdf)\n\n    *Xiaodan Liang, Zhiting Hu, Hao Zhang, Liang Lin, Eric P. Xing.*\n\n1. **Out of the Box: Reasoning with Graph Convolution Nets for Factual Visual Question Answering.** NeurIPS 2018. [paper](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F7531-out-of-the-box-reasoning-with-graph-convolution-nets-for-factual-visual-question-answering.pdf)\n\n    *Medhini Narasimhan, Svetlana Lazebnik, Alexander Schwing.*\n\n1. **Structural-RNN: Deep Learning on Spatio-Temporal Graphs.** CVPR 2016. [paper](https:\u002F\u002Fwww.cv-foundation.org\u002Fopenaccess\u002Fcontent_cvpr_2016\u002Fpapers\u002FJain_Structural-RNN_Deep_Learning_CVPR_2016_paper.pdf)\n\n    *Ashesh Jain, Amir R. Zamir, Silvio Savarese, Ashutosh Saxena.*\n\n1. **Relation Networks for Object Detection.** CVPR 2018. [paper](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_cvpr_2018\u002Fpapers_backup\u002FHu_Relation_Networks_for_CVPR_2018_paper.pdf)\n\n    *Han Hu, Jiayuan Gu, Zheng Zhang, Jifeng Dai, Yichen Wei.*\n\n1. **Learning Region features for Object Detection.** ECCV 2018. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1803.07066)\n\n    *Jiayuan Gu, Han Hu, Liwei Wang, Yichen Wei, Jifeng Dai.*\n\n1. **The More You Know: Using Knowledge Graphs for Image Classification.** CVPR 2017. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1612.04844.pdf)\n\n    *Kenneth Marino, Ruslan Salakhutdinov, Abhinav Gupta.* \n\n1. **Understanding Kin Relationships in a Photo.** TMM 2012. [paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=6151163)\n\n    *Siyu Xia, Ming Shao, Jiebo Luo, Yun Fu.* \n    \n1. **Graph-Structured Representations for Visual Question Answering.** CVPR 2017. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1609.05600.pdf)\n\n    *Damien Teney, Lingqiao Liu, Anton van den Hengel.* \n\n1. **Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition.** AAAI 2018. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1801.07455.pdf)\n\n    *Sijie Yan, Yuanjun Xiong, Dahua Lin.* \n\n1. **Dynamic Graph CNN for Learning on Point Clouds.** CVPR 2018. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1801.07829.pdf)\n\n    *Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E. Sarma, Michael M. Bronstein, Justin M. Solomon.* \n\n1. **PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation.** CVPR 2018. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1612.00593.pdf)\n   \n    *Charles R. Qi, Hao Su, Kaichun Mo, Leonidas J. Guibas.* \n\n1. **3D Graph Neural Networks for RGBD Semantic Segmentation.** CVPR 2017. [paper](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_ICCV_2017\u002Fpapers\u002FQi_3D_Graph_Neural_ICCV_2017_paper.pdf)\n\n    *Xiaojuan Qi, Renjie Liao, Jiaya Jia, Sanja Fidler, Raquel Urtasun.* \n\n1. **Iterative Visual Reasoning Beyond Convolutions.** CVPR 2018. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1803.11189)\n\n    *Xinlei Chen, Li-Jia Li, Li Fei-Fei, Abhinav Gupta.* \n\n1. **Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs.** CVPR 2017. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1704.02901)\n\n    *Martin Simonovsky, Nikos Komodakis.* \n\n1. **Situation Recognition with Graph Neural Networks.** ICCV 2017. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1708.04320)\n\n    *Ruiyu Li, Makarand Tapaswi, Renjie Liao, Jiaya Jia, Raquel Urtasun, Sanja Fidler.* \n\n1. **Deep Reasoning with Knowledge Graph for Social Relationship Understanding.** IJCAI 2018. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1807.00504.pdf)\n\n    *Zhouxia Wang, Tianshui Chen, Jimmy Ren, Weihao Yu, Hui Cheng, Liang Lin.* \n\n1. **I Know the Relationships: Zero-Shot Action Recognition via Two-Stream Graph Convolutional Networks and Knowledge Graphs.** AAAI 2019. [paper](http:\u002F\u002Fnlpr-web.ia.ac.cn\u002Fmmc\u002Fhomepage\u002Fjygao\u002FJY_Gao_files\u002FConference_Papers\u002FAAAI2019-GJY.pdf)\n   \n    *Junyu Gao, Tianzhu Zhang, Changsheng Xu.*\n    \n\u003Cdetails>\u003Csummary>more\u003C\u002Fsummary>\n\n21. **Graph CNNs with Motif and Variable Temporal Block for Skeleton-based Action Recognition.** AAAI 2019. [paper](https:\u002F\u002Fecs.victoria.ac.nz\u002Ffoswiki\u002Fpub\u002FGroups\u002FGraphics\u002FRGB-DDataProcessingForRobotics\u002FGraph%20CNNs%20with%20Motif%20and%20Variable%20Temporal%20Block%20for%20Skeleton-based%20Action%20Recognition.pdf)\n\n    *Yu-Hui Wen, Lin Gao, Hongbo Fu, Fang-Lue Zhang, Shihong Xia.*\n    \n1. **Multi-Label Image Recognition with Graph Convolutional Networks.** CVPR 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1904.03582.pdf)\n\n    *Zhao-Min Chen, Xiu-Shen Wei, Peng Wang, Yanwen Guo.*\n    \n1. **Spatial-Aware Graph Relation Network for Large-Scale Object Detection.** CVPR 2019. [paper](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPR_2019\u002Fpapers\u002FXu_Spatial-Aware_Graph_Relation_Network_for_Large-Scale_Object_Detection_CVPR_2019_paper.pdf)\n\n    *Hang Xu, Chenhan Jiang, Xiaodan Liang, Zhenguo Li.*\n    \n1. **GCAN: Graph Convolutional Adversarial Network for Unsupervised Domain Adaptation.** CVPR 2019. [paper](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPR_2019\u002Fpapers\u002FMa_GCAN_Graph_Convolutional_Adversarial_Network_for_Unsupervised_Domain_Adaptation_CVPR_2019_paper.pdf)\n\n    *Xinhong Ma, Tianzhu Zhang, Changsheng Xu.*\n    \n1. **Mind Your Neighbours: Image Annotation With Metadata Neighbourhood Graph Co-Attention Networks.** CVPR 2019. [paper](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPR_2019\u002Fpapers\u002FZhang_Mind_Your_Neighbours_Image_Annotation_With_Metadata_Neighbourhood_Graph_Co-Attention_CVPR_2019_paper.pdf)\n\n    *Junjie Zhang, Qi Wu, Jian Zhang, Chunhua Shen, Jianfeng Lu.*\n    \n1. **Attentive Relational Networks for Mapping Images to Scene Graphs.** CVPR 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1811.10696.pdf)\n\n    *Mengshi Qi, Weijian Li, Zhengyuan Yang, Yunhong Wang, Jiebo Luo.*\n    \n1. **Knowledge-Embedded Routing Network for Scene Graph Generation.** CVPR 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1903.03326.pdf)\n\n    *Tianshui Chen, Weihao Yu, Riquan Chen, Liang Lin.*\n    \n1. **Auto-Encoding Scene Graphs for Image Captioning.** CVPR 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1812.02378.pdf)\n\n    *Xu Yang, Kaihua Tang, Hanwang Zhang, Jianfei Cai.*\n    \n1. **Learning to Cluster Faces on an Affinity Graph.** CVPR 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1904.02749.pdf)\n\n    *Lei Yang, Xiaohang Zhan, Dapeng Chen, Junjie Yan, Chen Change Loy, Dahua Lin.*\n    \n1. **Learning a Deep ConvNet for Multi-label Classification with Partial Labels.** CVPR 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1902.09720.pdf)\n\n    *Thibaut Durand, Nazanin Mehrasa, Greg Mori.*\n    \n1. **Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detection.** CVPR 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1903.07256.pdf)\n\n    *Jia-Xing Zhong, Nannan Li, Weijie Kong, Shan Liu, Thomas H. Li, Ge Li.*\n    \n1. **Learning Actor Relation Graphs for Group Activity Recognition.** CVPR 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1904.10117.pdf)\n\n    *Jianchao Wu, Limin Wang, Li Wang, Jie Guo, Gangshan Wu.*\n    \n1. **ABC: A Big CAD Model Dataset For Geometric Deep Learning.** CVPR 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1812.06216.pdf)\n\n    *Sebastian Koch, Albert Matveev, Zhongshi Jiang, Francis Williams, Alexey Artemov, Evgeny Burnaev, Marc Alexa, Denis Zorin, Daniele Panozzo.*\n    \n1. **Neighbourhood Watch: Referring Expression Comprehension via Language-guided Graph Attention Networks.** CVPR 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1812.04794.pdf)\n\n    *Peng Wang, Qi Wu, Jiewei Cao, Chunhua Shen, Lianli Gao, Anton van den Hengel.*\n    \n1. **Graph-Based Global Reasoning Networks.** CVPR 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1811.12814.pdf)\n\n    *Yunpeng Chen, Marcus Rohrbach, Zhicheng Yan, Shuicheng Yan, Jiashi Feng, Yannis Kalantidis.*\n\n1. **Linkage Based Face Clustering via Graph Convolution Network.** CVPR 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1903.11306.pdf)\n\n    *Zhongdao Wang, Liang Zheng, Yali Li, Shengjin Wang.*\n    \n1. **Fast Interactive Object Annotation with Curve-GCN.** CVPR 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1903.06874.pdf)\n\n    *Huan Ling, Jun Gao, Amlan Kar, Wenzheng Chen, Sanja Fidler.*\n    \n1. **Semantic Graph Convolutional Networks for 3D Human Pose Regression.** CVPR 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1904.03345.pdf)\n\n    *Long Zhao, Xi Peng, Yu Tian, Mubbasir Kapadia, Dimitris N. Metaxas.*\n    \n1. **Neural Task Graphs: Generalizing to Unseen Tasks from a Single Video Demonstration.** CVPR 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1807.03480.pdf)\n\n    *De-An Huang, Suraj Nair, Danfei Xu, Yuke Zhu, Animesh Garg, Li Fei-Fei, Silvio Savarese, Juan Carlos Niebles.*\n    \n1. **Graphonomy: Universal Human Parsing via Graph Transfer Learning.** CVPR 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1904.04536.pdf)\n\n    *Ke Gong, Yiming Gao, Xiaodan Liang, Xiaohui Shen, Meng Wang, Liang Lin.*\n    \n1. **Learning Context Graph for Person Search.** CVPR 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1904.01830.pdf)\n\n    *Yichao Yan, Qiang Zhang, Bingbing Ni, Wendong Zhang, Minghao Xu, Xiaokang Yang.*\n    \n1. **Occlusion-Net: 2D\u002F3D Occluded Keypoint Localization Using Graph Networks.** CVPR 2019. [paper](http:\u002F\u002Fwww.cs.cmu.edu\u002F~mvo\u002Findex_files\u002FPapers\u002FONet_19.pdf)\n\n    *N. Dinesh Reddy, Minh Vo, Srinivasa G. Narasimhan.*\n    \n1. **MAN: Moment Alignment Network for Natural Language Moment Retrieval via Iterative Graph Adjustment.** CVPR 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1812.00087.pdf)\n\n    *Da Zhang, Xiyang Dai, Xin Wang, Yuan-Fang Wang, Larry S. Davis.*\n    \n1. **Context-Aware Visual Compatibility Prediction.** CVPR 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1902.03646.pdf)\n\n    *Guillem Cucurull, Perouz Taslakian, David Vazquez.*\n    \n1. **Graph Attention Convolution for Point Cloud Semantic Segmentation.** CVPR 2019. [paper](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPR_2019\u002Fpapers\u002FWang_Graph_Attention_Convolution_for_Point_Cloud_Semantic_Segmentation_CVPR_2019_paper.pdf)\n\n    *Lei Wang, Yuchun Huang, Yaolin Hou, Shenman Zhang, Jie Shan.*\n    \n1. **An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition.** CVPR 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1902.09130.pdf)\n\n    *Chenyang Si, Wentao Chen, Wei Wang, Liang Wang, Tieniu Tan.*\n    \n1. **Actional-Structural Graph Convolutional Networks for Skeleton-based Action Recognition.** CVPR 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1904.12659.pdf)\n\n    *Maosen Li, Siheng Chen, Xu Chen, Ya Zhang, Yanfeng Wang, Qi Tian.*\n    \n1. **Graph Convolutional Tracking.** CVPR 2019. [paper](http:\u002F\u002Fnlpr-web.ia.ac.cn\u002Fmmc\u002Fhomepage\u002Fjygao\u002FJY_Gao_files\u002FConference_Papers\u002FGCT-CVPR2019-GJY.pdf)\n\n    *Junyu Gao, Tianzhu Zhang, Changsheng Xu.*\n    \n1. **Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition.** CVPR 2019. [paper](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPR_2019\u002Fpapers\u002FShi_Two-Stream_Adaptive_Graph_Convolutional_Networks_for_Skeleton-Based_Action_Recognition_CVPR_2019_paper.pdf)\n\n    *Lei Shi, Yifan Zhang, Jian Cheng, Hanqing Lu.*\n\n1. **Skeleton-Based Action Recognition With Directed Graph Neural Networks.** CVPR 2019. [paper](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPR_2019\u002Fpapers\u002FShi_Skeleton-Based_Action_Recognition_With_Directed_Graph_Neural_Networks_CVPR_2019_paper.pdf)\n\n    *Lei Shi, Yifan Zhang, Jian Cheng, Hanqing Lu.*\n    \n1. **Neural Module Networks.** CVPR 2016. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1511.02799.pdf)\n\n    *Jacob Andreas, Marcus Rohrbach, Trevor Darrell, Dan Klein.*\n\n1. **LatentGNN: Learning Efficient Non-local Relations for Visual Recognition.** ICML 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.11634)\n\n    *Songyang Zhang, Shipeng Yan, Xuming He.*\n\n1. **Graph Convolutional Gaussian Processes.** ICML 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.05739)\n\n    *Ian Walker, Ben Glocker.*\n\n1. **GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects.** ICML 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1901.11461)\n\n    *Edward J. Smith, Scott Fujimoto, Adriana Romero, David Meger.*\n\n1. **Learning Cross­‐modal Context Graph Networks for Visual Ground","thunlp\u002FGNNPapers 是一个汇集了图神经网络（GNN）必读论文的资源库。该项目详细分类了GNN领域的关键研究，包括基础模型、图类型、池化方法、分析技术以及效率和可解释性等多个方面，并涵盖了物理、化学与生物、知识图谱、推荐系统、计算机视觉、自然语言处理等广泛的应用场景。适合研究人员、学生以及对GNN感兴趣的开发者作为学习和研究的参考指南。通过这个项目，用户可以快速了解GNN领域的重要进展和技术趋势。","2026-06-11 03:38:42","high_star"]