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Graph convolution networks, like message passing graph convolution networks (MPGCNs), have been a powerful tool in representation learning of networked data.
Wavelets on graphs via spectral graph theory
D. K. Hammond, P. Vandergheynst, and R. Gribonval · 2011
Earlier work this paper cites.
Perfect reconstruction two-channel wavelet filter banks for graph structured data
S. K. Narang and A. Ortega · 2012
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Compact support biorthogonal wavelet filterbanks for arbitrary undirected graphs
S. K. Narang and A. Ortega · 2013
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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
Earlier work this paper cites.
Spectral networks and locally connected networks on graphs
J. Bruna, W. Zaremba, A. Szlam, and Y. LeCun · 2014
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m m -channel oversampled graph filter banks
Y. Tanaka and A. Sakiyama · 2014
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Learning parametric dictionaries for signals on graphs
D. Thanou, D. I. Shuman, and P. Frossard · 2014
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
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Convolutional neural networks on graphs with fast localized spectral filtering
M. Defferrard, X. Bresson, and P. Vandergheynst · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Benchmark data sets for graph kernels, 2016
K. Kersting, N. M. Kriege, C. Morris, P. Mutzel, and M. Neumann · 2016
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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
Earlier work this paper cites.
Inductive representation learning on large graphs
W. Hamilton, Z. Ying, and J. Leskovec · 2017
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Graph-based isometry invariant representation learning
R. Khasanova and P. Frossard · 2017
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Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2017
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Automatic differentiation in PyTorch
A. Paszke et al · 2017
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CayleyNets: Graph convolutional neural networks with complex rational spectral filters
R. Levie, F. Monti, X. Bresson, and M. M. Bronstein · 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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Geometric scattering for graph data analysis
F. Gao, G. Wolf, and M. Hirn · 2019
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Diffusion improves graph learning
J. Klicpera, S. Weißenberger, and S. Günnemann · 2019
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Self-attention graph pooling
J. Lee, I. Lee, and J. Kang · 2019
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Transferability of spectral graph convolutional neural networks
R. Levie, W. Huang, L. Bucci, M. M. Bronstein, and G. Kutyniok · 2019
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Revisiting graph neural networks: All we have is low-pass filters
H. NT and T. Maehara · 2019
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How powerful are graph neural networks?
K. Xu, W. Hu, J. Leskovec, and S. Jegelka · 2019
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Design of graph filters and filterbanks
N. Tremblay, P. Gonçalves, and P. Borgnat · 2018
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Graph attention networks
P. Veličković et al · 2018
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Representation learning on graphs with jumping knowledge networks
K. Xu et al · 2018
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Graph neural networks with convolutional arma filters
F. M. Bianchi, D. Grattarola, L. Livi, and C. Alippi · 2019
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Fast graph representation learning with PyTorch Geometric
M. Fey and J. E. Lenssen · 2019
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Principal neighbourhood aggregation for graph nets
G. Corso, L. Cavalleri, D. Beaini, P. Liò, and P. Veličković · 2020
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Benchmarking graph neural networks
V. P. Dwivedi, C. K. Joshi, T. Laurent, Y. Bengio, and X. Bresson · 2020
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Open graph benchmark: Datasets for machine learning on graphs
W. Hu et al · 2020
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Scattering GCN: overcoming oversmoothness in graph convolutional networks
Y. Min, F. Wenkel, and G. Wolf · 2020
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Analyzing the expressive power of graph neural networks in a spectral perspective
M. Balcilar, R. Guillaume, P. Héroux, B. Gaüzère, S. Adam, and P. Honeine · 2021
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