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A hallmark of graph neural networks is their ability to distinguish the isomorphism class of their inputs.
Graph neural tangent kernel: Fusing graph neural networks with graph kernels
Simon S. Du, Kangcheng Hou, Barnabás Póczos, Ruslan Salakhutdinov, Ruosong Wang, and Keyulu Xu · 1905
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The number of trees
Richard Otter · 1948
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Approximation by superpositions of a sigmoidal function
George Cybenko · 1989
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Multilayer feedforward networks are universal approximators
Kurt Hornik, Maxwell Stinchcombe, and Halbert White · 1989
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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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Analytic combinatorics
Philippe Flajolet and Robert Sedgewick · 2009
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Algebraic distance on graphs
Jie Chen and Ilya Safro · 2011
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Practical graph isomorphism, {II}
Brendan D. McKay and Adolfo Piperno · 2013
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Gated graph sequence neural networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel · 2015
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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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Advanced coarsening schemes for graph partitioning
Ilya Safro, Peter Sanders, and Christian Schulz · 2015
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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Interaction networks for learning about objects, relations and physics
Peter Battaglia, Razvan Pascanu, Matthew Lai, Danilo Jimenez Rezende, et al · 2016
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Molecular graph convolutions: moving beyond fingerprints
Steven Kearnes, Kevin McCloskey, Marc Berndl, Vijay Pande, and Patrick Riley · 2016
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Adaptive computation time for recurrent neural networks
Alex Graves · 2016
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Introduction to random graphs
Alan Frieze and Michał Karoński · 2016
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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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Dynamic edge-conditioned filters in convolutional neural networks on graphs
Martin Simonovsky and Nikos Komodakis · 2017
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Graph convolutional matrix completion
Rianne van den Berg, Thomas N Kipf, and Max Welling · 2017
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Three notes on distributed property testing
Guy Even, Orr Fischer, Pierre Fraigniaud, Tzlil Gonen, Reut Levi, Moti Medina, Pedro Montealegre, Dennis Olivetti, Rotem Oshman, Ivan Rapaport, et al · 2017
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The expressive power of neural networks: A view from the width
Zhou Lu, Hongming Pu, Feicheng Wang, Zhiqiang Hu, and Liwei Wang · 2017
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How powerful are graph neural networks?
Graph warp module: an auxiliary module for boosting the power of graph neural networks
Katsuhiko Ishiguro, Shin-ichi Maeda, and Masanori Koyama · 2019
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Neural execution of graph algorithms
Petar Veličković, Rex Ying, Matilde Padovano, Raia Hadsell, and Charles Blundell · 2019
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Mincut pooling in graph neural networks
Filippo Maria Bianchi, Daniele Grattarola, and Cesare Alippi · 2019
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Graph reduction with spectral and cut guarantees
Andreas Loukas · 2019
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The logical expressiveness of graph neural networks
Pablo Barceló, Egor V Kostylev, Mikael Monet, Jorge Pérez, Juan Reutter, and Juan Pablo Silva · 2019
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Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2018
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Relational inductive biases, deep learning, and graph networks
Peter W Battaglia, Jessica B Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, et al · 2018
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Lower bounds for subgraph detection in the congest model
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Hierarchical graph representation learning with differentiable pooling
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Spectrally approximating large graphs with smaller graphs
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On the universality of invariant networks
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Universal invariant and equivariant graph neural networks
Nicolas Keriven and Gabriel Peyré · 2019
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Generalization and representational limits of graph neural networks
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