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In this paper, we propose a simple yet effective graph neural network for directed graphs (digraph) based on the classic Singular Value Decomposition (SVD), named SVD-GCN.
Supervised neural networks for the classification of structures
Alessandro Sperduti and Antonina Starita · 1997
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A new model for learning in graph domains
Marco Gori, Gabriele Monfardini, and Franco Scarselli · 2005
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The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2008
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Discrete signal processing on graphs
Aliaksei Sandryhaila and José MF Moura · 2013
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Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst · 2016
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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Revisiting semi-supervised learning with graph embeddings
Zhilin Yang, William Cohen, and Ruslan Salakhudinov · 2016
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Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking
Aleksandar Bojchevski and Stephan Günnemann · 2017
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Geometric deep learning: going beyond euclidean data
Michael M Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, and Pierre Vandergheynst · 2017
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Sparse representation on graphs by tight wavelet frames and applications
Bin Dong · 2017
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On the shift operator, graph frequency, and optimal filtering in graph signal processing
Adnan Gavili and Xiao-Ping Zhang · 2017
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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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Fast singular value shrinkage with chebyshev polynomial approximation based on signal sparsity
Masaki Onuki, Shunsuke Ono, Keiichiro Shirai, and Yuichi Tanaka · 2017
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Relational inductive biases, deep learning, and graph networks
Peter W Battaglia, Jessica B Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, et al · 2018
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Deep Gaussian embedding of graphs: Unsupervised inductive learning via ranking
Aleksandar Bojchevski and Stephan Günnemann · 2018
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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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Motifnet: a motif-based graph convolutional network for directed graphs
Federico Monti, Karl Otness, and Michael M Bronstein · 2018
Hodge Laplacians on graphs
Lek-Heng Lim · 2020
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Sign: Scalable inception graph neural networks
Emanuele Rossi, Fabrizio Frasca, Ben Chamberlain, Davide Eynard, Michael M. Bronstein, and Federico Monti · 2020
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Digraph inception convolutional networks
Zekun Tong, Yuxuan Liang, Changsheng Sun, Xinke Li, David Rosenblum, and Andrew Lim · 2020
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Directed graph convolutional network
Zekun Tong, Yuxuan Liang, Changsheng Sun, David S. Rosenblum, and Andrew Lim · 2020
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A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip · 2020
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Deep learning on graphs: a survey
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Pitfalls of graph neural network evaluation
Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, and Stephan Günnemann · 2018
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Directed strongly walk-regular graphs
E. R. van Dam and G. R. Omidi · 2018
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Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
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Predict then propagate: Graph neural networks meet personalized pageran
Johannes Klicpera, Aleksandar Bojchevski, and Stephan Günnemann · 2019
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Spectral-based graph convolutional network for directed graphs
Yi Ma, Jianye Hao, Yaodong Yang, Han Li, Junqi Jin, and Guangyong Chen · 2019
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Deep graph infomax
Petar Velićković, William Fedus, William L, Hamilton, Pietro Lió, Yoshua Bengio, and R DevonHjelm · 2019
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Simplifying graph convolutional networks
Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger · 2019
Cited alongside, same era.
Ziwei Zhang, Peng Cui, and Wenwu Zhu · 2020
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Graph neural networks: a review of methods and applications
Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun · 2020
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Geometric deep learning on molecular representations
Kenneth Atz, Francesca Grisoni, and Gisbert Schneider · 2021
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Spectral graph attention network with fast eigen-approximation
Heng Chang, Yu Rong, Tingyang Xu, Wenbing Huang, Somayeh Sojoudi, Junzhou Huang, and Wenwu Zhu · 2021
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Magnet: A neural network for directed graphs
Xitong Zhang, Yixuan He, Nathan Brugnone, Michael Perlmutter, and Matthew Hirn · 2021
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How framelets enhance graph neural networks
Xuebin Zheng, Bingxin Zhou, Junbin Gao, Yuguang Wang, Pietro Lió, Ming Li, and Guido Montúfar · 2021
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Quasi-framelets: Another improvement to spectral graph neural networks
Mengxi Yang, Xuebin Zhou, Jie Yin, and Junbin Gao · 2022
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