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A key requirement for graph neural networks is that they must process a graph in a way that does not depend on how the graph is described.
A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and Philip S. Yu · 1901
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A new model for learning in graph domains
M. Gori, G. Monfardini, and F. Scarselli · 2005
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Spectral networks and locally connected networks on graphs
Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun · 2014
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Deep graph kernels
Pinar Yanardag and SVN Vishwanathan · 2015
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Group equivariant convolutional networks
Taco Cohen and Max Welling · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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Basic Category Theory
Tom Leinster · 2016
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Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst · 2016
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Learning convolutional neural networks for graphs
Mathias Niepert, Mohamed Ahmed, and Konstantin Kutzkov · 2016
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Diffusion-convolutional neural networks
James Atwood and Don Towsley · 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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Gated graph sequence neural networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel · 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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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2018
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Invariant and equivariant graph networks
Haggai Maron, Heli Ben-Hamu, Nadav Shamir, and Yaron Lipman · 2018
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An end-to-end deep learning architecture for graph classification
Muhan Zhang, Zhicheng Cui, Marion Neumann, and Yixin Chen · 2018
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Hierarchical graph representation learning with differentiable pooling
Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, Will Hamilton, and Jure Leskovec · 2018
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Emiel Hoogeboom, Jorn WT Peters, Taco S Cohen, and Max Welling · 2018
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Gauge equivariant convolutional networks and the icosahedral cnn
Taco Cohen, Maurice Weiler, Berkay Kicanaoglu, and Max Welling · 2019
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Provably powerful graph networks
Haggai Maron, Heli Ben-Hamu, Hadar Serviansky, and Yaron Lipman · 2019
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Seven sketches in compositionality: An invitation to applied category theory
Brendan Fong and David I Spivak · 2018
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
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Deep models of interactions across sets
Jason Hartford, Devon R Graham, Kevin Leyton-Brown, and Siamak Ravanbakhsh · 2018
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Nathanaël Perraudin, Michaël Defferrard, Tomasz Kacprzak, and Raphael Sgier · 2018
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Covariant compositional networks for learning graphs
Risi Kondor, Hy Truong Son, Horace Pan, Brandon Anderson, and Shubhendu Trivedi · 2018
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Universal invariant and equivariant graph neural networks
Nicolas Keriven and Gabriel Peyré · 2019
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Incidence networks for geometric deep learning
Marjan Albooyeh, Daniele Bertolini, and Siamak Ravanbakhsh · 2019
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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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Improving graph neural network expressivity via subgraph isomorphism counting
Giorgos Bouritsas et al · 2020
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