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Graph Convolutional Networks (GCNs) have attracted more and more attentions in recent years.
On the limited memory BFGS method for large scale optimization
Dong C Liu and Jorge Nocedal · 1989
Earlier work this paper cites.
Spectral graph theory
Fan RK Chung and Fan Chung Graham · 1997
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Semi-supervised learning using gaussian fields and harmonic functions
Xiaojin Zhu, Zoubin Ghahramani, and John D Lafferty · 2003
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Collective classification in network data
Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad · 2008
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Stochastic stability of continuous time consensus protocols
Georgi S Medvedev · 2012
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The nonlinear heat equation on dense graphs and graph limits
Georgi S Medvedev · 2014
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DeepWalk: Online learning of social representations
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena · 2014
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Hyperopt: a python library for model selection and hyperparameter optimization
James Bergstra, Brent Komer, Chris Eliasmith, Dan Yamins, and David D Cox · 2015
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Revisiting semi-supervised learning with graph embeddings
Zhilin Yang, William Cohen, and Ruslan Salakhudinov · 2016
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2017
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FastGCN: fast learning with graph convolutional networks via importance sampling
Jie Chen, Tengfei Ma, and Cao Xiao · 2018
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Neural ordinary differential equations
Ricky TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud · 2018
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Deeper insights into graph convolutional networks for semi-supervised learning
Qimai Li, Zhichao Han, and Xiao-Ming Wu · 2018
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Beyond finite layer neural networks: Bridging deep architectures and numerical differential equations
Yiping Lu, Aoxiao Zhong, Quanzheng Li, and Bin Dong · 2018
DeepGCNs: Can gcns go as deep as cnns?
Guohao Li, Matthias Muller, Ali Thabet, and Bernard Ghanem · 2019
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Graph neural ordinary differential equations
Michael Poli, Stefano Massaroli, Junyoung Park, Atsushi Yamashita, Hajime Asama, and Jinkyoo Park · 2019
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DropEdge: Towards deep graph convolutional networks on node classification
Yu Rong, Wenbing Huang, Tingyang Xu, and Junzhou Huang · 2019
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Deep graph infomax
Petar Velickovic, William Fedus, William L Hamilton, Pietro Liò, Yoshua Bengio, and R Devon Hjelm · 2019
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Simplifying graph convolutional networks
Felix Wu, Tianyi Zhang, Amauri Holanda de Souza Jr, Christopher Fifty, Tao Yu, and Kilian Q Weinberger · 2019
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Graph wavelet neural network
Bingbing Xu, Huawei Shen, Qi Cao, Yunqi Qiu, and Xueqi Cheng · 2019
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Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2018
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Representation learning on graphs with jumping knowledge networks
Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, and Stefanie Jegelka · 2018
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Certifiable robustness to graph perturbations
Aleksandar Bojchevski and Stephan Günnemann · 2019
Cited alongside, same era.
Predict then propagate: Graph neural networks meet personalized pagerank
Johannes Klicpera, Aleksandar Bojchevski, and Stephan Günnemann · 2019
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SIGN: Scalable inception graph neural networks
Emanuele Rossi, Fabrizio Frasca, Ben Chamberlain, Davide Eynard, Michael Bronstein, and Federico Monti · 2020
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Continuous graph neural networks
Louis-Pascal Xhonneux, Meng Qu, and Jian Tang · 2020
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Graph convolutional networks using heat kernel for semi-supervised learning
Bingbing Xu, Huawei Shen, Qi Cao, Keting Cen, and Xueqi Cheng · 2020
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PairNorm: Tackling oversmoothing in gnns
Lingxiao Zhao and Leman Akoglu · 2020
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