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Graph Neural Networks (GNNs) have achieved much success on graph-structured data.
The reduction of a graph to canonical form and the algebra which appears therein
B Weisfeiler and A Leman · 1968
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Random graph isomorphism
László Babai, Paul Erdos, and Stanley M Selkow · 1980
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Graph isomorphism and theorems of birkhoff type
Gottfried Tinhofer · 1986
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Matrix multiplication via arithmetic progressions
D. Coppersmith and S. Winograd · 1987
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Approximation by superpositions of a sigmoidal function
George Cybenko · 1989
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Approximation capabilities of multilayer feedforward networks
Kurt Hornik · 1991
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A note on compact graphs
G. Tinhofer · 1991
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An optimal lower bound on the number of variables for graph identification
Jin-Yi Cai, Martin Fürer, and Neil Immerman · 1992
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Fractional isomorphism of graphs
Motakuri V Ramana, Edward R Scheinerman, and Daniel Ullman · 1994
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Semidefinite programming relaxations for the quadratic assignment problem
Qing Zhao, Stefan E Karisch, Franz Rendl, and Henry Wolkowicz · 1998
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On the power of combinatorial and spectral invariants
Martin Fürer · 2008
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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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The elements of integration and Lebesgue measure
Robert G Bartle · 2014
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Hardness of robust graph isomorphism, lasserre gaps, and asymmetry of random graphs
Ryan O’Donnell, John Wright, Chenggang Wu, and Yuan Zhou · 2014
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Convolutional networks on graphs for learning molecular fingerprints
David K Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alán Aspuru-Guzik, and Ryan P Adams · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Deep graph kernels
Pinar Yanardag and SVN Vishwanathan · 2015
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Graph isomorphism in quasipolynomial time
László Babai · 2016
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Geometric deep learning: Going beyond euclidean data
M. M. Bronstein, J. Bruna, Y. LeCun, A. Szlam, and P. Vandergheynst · 2017
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 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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Lanczosnet: Multi-scale deep graph convolutional networks
Renjie Liao, Zhizhen Zhao, Raquel Urtasun, and Richard Zemel · 2019
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Provably powerful graph networks
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Invariant and equivariant graph networks
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On the universality of invariant networks
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Weisfeiler and leman go neural: Higher-order graph neural networks
Christopher Morris, Martin Ritzert, Matthias Fey, William L Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe · 2019
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Relational pooling for graph representations
Ryan Murphy, Balasubramaniam Srinivasan, Vinayak Rao, and Bruno Ribeiro · 2019
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Mapping images to scene graphs with permutation-invariant structured prediction
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Revised note on learning quadratic assignment with graph neural networks
Alex Nowak, Soledad Villar, Afonso S Bandeira, and Joan Bruna · 2018
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Graph attention networks
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Representation learning on graphs with jumping knowledge networks
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A comprehensive survey on graph neural networks
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