Fetching the paper…
Reading the bibliography…
Graph convolutional networks (GCNs) and their variants have achieved great success in dealing with graph-structured data.
Mean-field behaviour of neural tangent kernel for deep neural networks
Soufiane Hayou, Arnaud Doucet, and Judith Rousseau · 1905
Earlier work this paper cites.
On the strength of connectedness of a random graph
Paul Erdős and Alfréd Rényi · 1961
Earlier work this paper cites.
Convergence rates for markov chains
Jeffrey S Rosenthal · 1995
Earlier work this paper cites.
Priors for infinite networks
Radford M Neal · 1996
Earlier work this paper cites.
Spectral distributions of adjacency and laplacian matrices of random graphs
Xue Ding and Tiefeng Jiang · 2010
Earlier work this paper cites.
On the spectra of general random graphs
Fan Chung and Mary Radcliffe · 2011
Earlier work this paper cites.
On the evolution of random graphs
Paul Erdös and Alfréd Rényi · 2011
Earlier work this paper cites.
Variational graph auto-encoders
Thomas N Kipf and Max Welling · 2016
Earlier work this paper cites.
Exponential expressivity in deep neural networks through transient chaos
Ben Poole, Subhaneil Lahiri, Maithra Raghu, Jascha Sohl-Dickstein, and Surya Ganguli · 2016
Earlier work this paper cites.
Convergence theorem for finite markov chains
Ari Freedman · 2017
Earlier work this paper cites.
Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
Earlier work this paper cites.
Graph neural networks exponentially lose expressive power for node classification
Samuel S Schoenholz, Justin Gilmer, Surya Ganguli, and Jascha Sohl-Dickstein · 2017
Earlier work this paper cites.
Gaussian process behaviour in wide deep neural networks
Alexander G. de G. Matthews, Jiri Hron, Mark Rowland, Richard E. Turner, and Zoubin Ghahramani · 2018
Earlier work this paper cites.
Critical percolation clusters in seven dimensions and on a complete graph
Wei Huang, Pengcheng Hou, Junfeng Wang, Robert M Ziff, and Youjin Deng · 2018
Earlier work this paper cites.
Neural tangent kernel: Convergence and generalization in neural networks
Arthur Jacot, Franck Gabriel, and Clément Hongler · 2018
Earlier work this paper cites.
Deep neural networks as gaussian processes
Jaehoon Lee, Jascha Sohl-dickstein, Jeffrey Pennington, Roman Novak, Sam Schoenholz, and Yasaman Bahri · 2018
Earlier work this paper cites.
Deeper insights into graph convolutional networks for semi-supervised learning
Qimai Li, Zhichao Han, and Xiao-Ming Wu · 2018
Cited alongside, same era.
Pitfalls of graph neural network evaluation
Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, and Stephan Günnemann · 2018
Cited alongside, same era.
Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
Cited alongside, same era.
Representation learning on graphs with jumping knowledge networks
Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, and Stefanie Jegelka · 2018
Cited alongside, same era.
Stochastic gradient descent optimizes over-parameterized deep relu networks. arxiv e-prints, art
Difan Zou, Yuan Cao, Dongruo Zhou, and Quanquan Gu · 2018
Cited alongside, same era.
Infinite attention: Nngp and ntk for deep attention networks
Jiri Hron, Yasaman Bahri, Jascha Sohl-Dickstein, and Roman Novak · 2020
Later among the works it cites.
Tackling over-smoothing for general graph convolutional networks
Wenbing Huang, Yu Rong, Tingyang Xu, Fuchun Sun, and Junzhou Huang · 2020
Later among the works it cites.
How to find your friendly neighborhood: Graph attention design with self-supervision
Dongkwan Kim and Alice Oh · 2020
Later among the works it cites.
Deepergcn: All you need to train deeper gcns
Guohao Li, Chenxin Xiong, Ali Thabet, and Bernard Ghanem · 2020
Later among the works it cites.
Towards deeper graph neural networks
Meng Liu, Hongyang Gao, and Shuiwang Ji · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A convergence theory for deep learning via over-parameterization
Zeyuan Allen-Zhu, Yuanzhi Li, and Zhao Song · 2019
Cited alongside, same era.
On exact computation with an infinitely wide neural net
Sanjeev Arora, Simon S Du, Wei Hu, Zhiyuan Li, Russ R Salakhutdinov, and Ruosong Wang · 2019
Cited alongside, same era.
Measuring and improving the use of graph information in graph neural networks
Yifan Hou, Jian Zhang, James Cheng, Kaili Ma, Richard TB Ma, Hongzhi Chen, and Ming-Chang Yang · 2019
Cited alongside, same era.
Predict then propagate: Graph neural networks meet personalized pagerank
Johannes Klicpera, Aleksandar Bojchevski, and Stephan Günnemann · 2019
Cited alongside, same era.
Deepgcns: Can GCNs go as deep as CNNs?
Guohao Li, Matthias Muller, Ali Thabet, and Bernard Ghanem · 2019
Cited alongside, same era.
Dropedge: Towards deep graph convolutional networks on node classification
Yu Rong, Wenbing Huang, Tingyang Xu, and Junzhou Huang · 2019
Cited alongside, same era.
How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
Cited alongside, same era.
What graph neural networks cannot learn: depth vs width
Andreas Loukas · 2020
Later among the works it cites.
Graph neural networks exponentially lose expressive power for node classification
Kenta Oono and Taiji Suzuki · 2020
Later among the works it cites.
Disentangling trainability and generalization in deep neural networks
Lechao Xiao, Jeffrey Pennington, and Samuel Schoenholz · 2020
Later among the works it cites.
How neural networks extrapolate: From feedforward to graph neural networks
Keyulu Xu, Jingling Li, Mozhi Zhang, Simon S Du, Ken-ichi Kawarabayashi, and Stefanie Jegelka · 2020
Later among the works it cites.
Structpool: Structured graph pooling via conditional random fields
Hao Yuan and S. Ji · 2020
Later among the works it cites.
Deep graph neural networks with shallow subgraph samplers
Hanqing Zeng, Muhan Zhang, Yinglong Xia, Ajitesh Srivastava, Rajgopal Kannan, Viktor Prasanna, Long Jin, Andrey Malevich, and Ren Chen · 2020
Later among the works it cites.
Pairnorm: Tackling oversmoothing in gnns
Lingxiao Zhao and Leman Akoglu · 2020
Later among the works it cites.
Towards deeper graph neural networks with differentiable group normalization
Kaixiong Zhou, Xiao Huang, Yuening Li, Daochen Zha, Rui Chen, and Xia Hu · 2020
Later among the works it cites.
On the equivalence between neural network and support vector machine
Yilan Chen, Wei Huang, Lam Nguyen, and Tsui-Wei Weng · 2021
Closest in time.
On provable benefits of depth in training graph convolutional networks
Weilin Cong, Morteza Ramezani, and Mehrdad Mahdavi · 2021
Closest in time.
On the neural tangent kernel of deep networks with orthogonal initialization
Wei Huang, Weitao Du, and Richard Yi Da Xu · 2021
Closest in time.