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We propose a new graph neural network (GNN) module, based on relaxations of recently proposed geometric scattering transforms, which consist of a cascade of graph wavelet filters.
“Multivariable functional interpolation and adaptive networks,”
D. Broomhead and D. Lowe, · 1988
Earlier work this paper cites.
“Diffusion wavelets,”
R. Coifman and M. Maggioni, · 2006
Earlier work this paper cites.
“Protein structure prediction using Rosetta in CASP12,”
S. Ovchinnikov, H. Park, D. Kim, F. DiMaio, and D. Baker, · 2010
Earlier work this paper cites.
“Group invariant scattering,”
S. Mallat, · 2012
Earlier work this paper cites.
“Semi-supervised classification with graph convolutional networks,”
T. Kipf and M. Welling, · 2016
Earlier work this paper cites.
“Assessment of Template-Based Modeling of Protein Structure in CASP11,”
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Earlier work this paper cites.
“Geometric deep learning: Going beyond Euclidean data,”
M. Bronstein, J. Bruna, Y. LeCun, A. Szlam, and P. Vandergheynst, · 2017
Earlier work this paper cites.
“Inductive representation learning on large graphs,”
W. Hamilton, R. Ying, and J. Leskovec, · 2017
Earlier work this paper cites.
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M. Perlmutter, G. Wolf, and M. Hirn, · 2018
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P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio, · 2018
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“Critical assessment of methods of protein structure prediction (CASP)—Round XII,”
J. Moult, K. Fidelis, A. Kryshtafovych, T. Schwede, and A. Tramontano, · 2018
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“How powerful are graph neural networks?,”
K. Xu, W. Hu, J. Leskovec, and S. Jegelka, · 2019
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D. Zou and G. Lerman, · 2019
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“Stability of graph scattering transforms,”
F. Gama, J. Bruna, and A. Ribeiro, · 2019
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“Understanding graph neural networks with asymmetric geometric scattering transforms,”
M. Perlmutter, F. Gao, G. Wolf, and M. Hirn, · 2019
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“Break the ceiling: Stronger multi-scale deep graph convolutional networks,”
S. Luan, M. Zhao, X. Chang, and D. Precup, · 2019
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“Generative models for Graph-Based Protein Design,”
J. Ingraham, V. Garg, R. Barzilay, and T. Jaakkola, · 2019
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“The logical expressiveness of graph neural networks,”
P. Barceló, E. Kostylev, M. Monet, J. Pérez, J. Reutter, and J. Silva, · 2020
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