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The core operation of current Graph Neural Networks (GNNs) is the aggregation enabled by the graph Laplacian or message passing, which filters the neighborhood node information.
New spectral methods for ratio cut partitioning and clustering
L. Hagen and A. B. Kahng · 1992
Earlier work this paper cites.
Spectral graph theory
F. R. Chung and F. C. Graham · 1997
Earlier work this paper cites.
Partial differential equations. graduate studies in mathematics
L. C. Evans · 1998
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, et al · 1998
Earlier work this paper cites.
Birds of a feather: Homophily in social networks
M. McPherson, L. Smith-Lovin, and J. M. Cook · 2001
Earlier work this paper cites.
Laplacian eigenmaps and spectral techniques for embedding and clustering
M. Belkin and P. Niyogi · 2002
Earlier work this paper cites.
Mixing patterns in networks
M. E. Newman · 2003
Earlier work this paper cites.
Semi-supervised learning using gaussian fields and harmonic functions
X. Zhu, Z. Ghahramani, and J. D. Lafferty · 2003
Earlier work this paper cites.
Locality preserving projections
X. He and P. Niyogi · 2004
Earlier work this paper cites.
Learning with local and global consistency
D. Zhou, O. Bousquet, T. N. Lal, J. Weston, and B. Schölkopf · 2004
Earlier work this paper cites.
X. Zhu and J. Lafferty · 2005
Earlier work this paper cites.
Manifold regularization: A geometric framework for learning from labeled and unlabeled examples
M. Belkin, P. Niyogi, and V. Sindhwani · 2006
Earlier work this paper cites.
Netprobe: a fast and scalable system for fraud detection in online auction networks
S. Pandit, D. H. Chau, S. Wang, and C. Faloutsos · 2007
Earlier work this paper cites.
Nonlocal discrete regularization on weighted graphs: a framework for image and manifold processing
A. Elmoataz, O. Lezoray, and S. Bougleux · 2008
Earlier work this paper cites.
Collective classification in network data
P. Sen, G. M. Namata, M. Bilgic, L. Getoor, B. Gallagher, and T. Eliassi-Rad · 2008
Earlier work this paper cites.
Local and nonlocal discrete regularization on weighted graphs for image and mesh processing
S. Bougleux, A. Elmoataz, and M. Melkemi · 2009
Earlier work this paper cites.
Probabilistic dyadic data analysis with local and global consistency
D. Cai, X. Wang, and X. He · 2009
Earlier work this paper cites.
Graph regularized nonnegative matrix factorization for data representation
D. Cai, X. He, J. Han, and T. S. Huang · 2010
Earlier work this paper cites.
Diffusion of innovations
E. M. Rogers · 2010
Earlier work this paper cites.
Graph regularized sparse coding for image representation
M. Zheng, J. Bu, C. Chen, C. Wang, L. Zhang, G. Qiu, and D. Cai · 2010
Earlier work this paper cites.
Critically-sampled perfect-reconstruction spline-wavelet filterbanks for graph signals
V. N. Ekambaram, G. Fanti, B. Ayazifar, and K. Ramchandran · 2013
Earlier work this paper cites.
Matrix Analysis
R. Horn and C. Johnson · 2013
Earlier work this paper cites.
Assortative mating in animals
Y. Jiang, D. I. Bolnick, and M. Kirkpatrick · 2013
Cited alongside, same era.
The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains
D. I. Shuman, S. K. Narang, P. Frossard, A. Ortega, and P. Vandergheynst · 2013
Cited alongside, same era.
Spectral networks and locally connected networks on graphs
J. Bruna, W. Zaremba, A. Szlam, and Y. LeCun · 2014
Cited alongside, same era.
Graph structured data viewed through a fourier lens
V. N. Ekambaram · 2014
Cited alongside, same era.
Deep learning
Y. LeCun, Y. Bengio, and G. Hinton · 2015
Cited alongside, same era.
Multi-scale attributed node embedding, 2019
B. Rozemberczki, C. Allen, and R. Sarkar · 2019
Later among the works it cites.
Graph signal processing–part i: Graphs, graph spectra, and spectral clustering
L. Stankovic, D. Mandic, M. Dakovic, M. Brajovic, B. Scalzo, and T. Constantinides · 2019
Later among the works it cites.
Simplifying graph convolutional networks
F. Wu, T. Zhang, A. H. d. Souza Jr, C. Fifty, T. Yu, and K. Q. Weinberger · 2019
Later among the works it cites.
A comprehensive survey on graph neural networks
Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and P. S. Yu · 2019
Later among the works it cites.
B. Xu, H. Shen, Q. Cao, Y. Qiu, and X. Cheng · 2019
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M. M. Bronstein, J. Bruna, Y. LeCun, A. Szlam, and P. Vandergheynst · 2016
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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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How to learn a graph from smooth signals
V. Kalofolias · 2016
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Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2016
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On the shift operator, graph frequency, and optimal filtering in graph signal processing
A. Gavili and X.-P. Zhang · 2017
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Neural message passing for quantum chemistry
J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl · 2017
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Inductive representation learning on large graphs
W. L. Hamilton, R. Ying, and J. Leskovec · 2017
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Graphsaint: Graph sampling based inductive learning method
H. Zeng, H. Zhou, A. Srivastava, R. Kannan, and V. Prasanna · 2019
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Graph convolutional networks: a comprehensive review
S. Zhang, H. Tong, J. Xu, and R. Maciejewski · 2019
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Pairnorm: Tackling oversmoothing in gnns
L. Zhao and L. Akoglu · 2019
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Simple and deep graph convolutional networks
M. Chen, Z. Wei, Z. Huang, B. Ding, and Y. Li · 2020
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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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Geom-gcn: Geometric graph convolutional networks
H. Pei, B. Wei, K. C.-C. Chang, Y. Lei, and B. Yang · 2020
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Graph neural networks with heterophily
J. Zhu, R. A. Rossi, A. Rao, T. Mai, N. Lipka, N. K. Ahmed, and D. Koutra · 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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Beyond low-frequency information in graph convolutional networks
D. Bo, X. Wang, C. Shi, and H. Shen · 2021
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Adaptive universal generalized pagerank graph neural network
E. Chien, J. Peng, P. Li, and O. Milenkovic · 2021
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Bernnet: Learning arbitrary graph spectral filters via bernstein approximation
M. He, Z. Wei, H. Xu, et al · 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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Is heterophily a real nightmare for graph neural networks to do node classification?
S. Luan, C. Hua, Q. Lu, J. Zhu, M. Zhao, S. Zhang, X.-W. Chang, and D. Precup · 2021
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When do we need gnn for node classification?
S. Luan, C. Hua, Q. Lu, J. Zhu, X.-W. Chang, and D. Precup · 2022
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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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