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Graph Neural Networks (GNNs) extend basic Neural Networks (NNs) by additionally making use of graph structure based on the relational inductive bias (edge bias), rather than treating the nodes as collections of independent and identically distributed (i.i.d.) samples.
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Gradient-based learning applied to document recognition
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Birds of a feather: Homophily in social networks
M. McPherson, L. Smith-Lovin, and J. M. Cook · 2001
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Learning with local and global consistency
D. Zhou, O. Bousquet, T. N. Lal, J. Weston, and B. Schölkopf · 2004
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Signal recovery on graphs: Variation minimization
S. Chen, A. Sandryhaila, J. M. Moura, and J. Kovacevic · 2015
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Deep learning
Y. LeCun, Y. Bengio, and G. Hinton · 2015
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Convolutional neural networks on graphs with fast localized spectral filtering
M. Defferrard, X. Bresson, and P. Vandergheynst · 2016
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Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2016
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Graph signals classification using total variation and graph energy informations
H. B. Ahmed, D. Dare, and A.-O. Boudraa · 2017
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Inductive representation learning on large graphs
W. Hamilton, Z. Ying, and J. Leskovec · 2017
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Relational inductive biases, deep learning, and graph networks
P. W. Battaglia, J. B. Hamrick, V. Bapst, A. Sanchez-Gonzalez, V. Zambaldi, M. Malinowski, A. Tacchetti, D. Raposo, A. Santoro, R. Faulkner, et al · 2018
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Deeper insights into graph convolutional networks for semi-supervised learning
Q. Li, Z. Han, and X.-M. Wu · 2018
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Graph attention networks
P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio · 2018
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Local smoothness of graph signals
M. Daković, L. Stanković, and E. Sejdić · 2019
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Break the ceiling: Stronger multi-scale deep graph convolutional networks
S. Luan, M. Zhao, X.-W. Chang, and D. Precup · 2019
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Revisiting graph neural networks: All we have is low-pass filters
Geom-gcn: Geometric graph convolutional networks
H. Pei, B. Wei, K. C.-C. Chang, Y. Lei, and B. Yang · 2020
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Generalizing graph neural networks beyond homophily
J. Zhu, Y. Yan, L. Zhao, M. Heimann, L. Akoglu, and D. Koutra · 2020
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On provable benefits of depth in training graph convolutional networks
W. Cong, M. Ramezani, and M. Mahdavi · 2021
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Large scale learning on non-homophilous graphs: New benchmarks and strong simple methods
D. Lim, F. Hohne, X. Li, S. L. Huang, V. Gupta, O. Bhalerao, and S. N. Lim · 2021
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New benchmarks for learning on non-homophilous graphs
D. Lim, X. Li, F. Hohne, and S.-N. Lim · 2021
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T. Maehara · 2019
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Simplifying graph convolutional networks
F. Wu, A. Souza, T. Zhang, C. Fifty, T. Yu, and K. Weinberger · 2019
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Graph representation learning
W. L. Hamilton · 2020
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Non-local graph neural networks
M. Liu, Z. Wang, and S. Ji · 2020
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Training matters: Unlocking potentials of deeper graph convolutional neural networks
S. Luan, M. Zhao, X.-W. Chang, and D. Precup · 2020
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S. Luan, C. Hua, Q. Lu, J. Zhu, M. Zhao, S. Zhang, X.-W. Chang, and D. Precup · 2021
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Revisiting heterophily for graph neural networks
S. Luan, C. Hua, Q. Lu, J. Zhu, M. Zhao, S. Zhang, X.-W. Chang, and D. Precup · 2022
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Complete the missing half: Augmenting aggregation filtering with diversification for graph convolutional networks
S. Luan, M. Zhao, C. Hua, X.-W. Chang, and D. Precup · 2022
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On addressing the limitations of graph neural networks
S. Luan · 2023
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When do graph neural networks help with node classification: Investigating the homophily principle on node distinguishability
S. Luan, C. Hua, M. Xu, Q. Lu, J. Zhu, X.-W. Chang, J. Fu, J. Leskovec, and D. Precup · 2023
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