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Graph Neural Networks (GNNs) are an effective framework for representation learning of graphs.
A reduction of a graph to a canonical form and an algebra arising during this reduction
Boris Weisfeiler and AA Lehman · 1968
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Generalized linear models
J. A. Nelder and R. W. M. Wedderburn · 1972
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Canonical labelling of graphs in linear average time
László Babai and Ludik Kucera · 1979
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A guide to the theory of np-completeness
Michael R Garey · 1979
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Multilayer feedforward networks are universal approximators
Kurt Hornik, Maxwell Stinchcombe, and Halbert White · 1989
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Approximation capabilities of multilayer feedforward networks
Kurt Hornik · 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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Isomorphism of coloured graphs with slowly increasing multiplicity of jordan blocks
Sergei Evdokimov and Ilia Ponomarenko · 1999
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Computers and intractability , volume 29
Michael R Garey and David S Johnson · 2002
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Libsvm: a library for support vector machines
Chih-Chung Chang and Chih-Jen Lin · 2011
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The weisfeiler-lehman method and graph isomorphism testing
Brendan L Douglas · 2011
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Weisfeiler-lehman graph kernels
Nino Shervashidze, Pascal Schweitzer, Erik Jan van Leeuwen, Kurt Mehlhorn, and Karsten M Borgwardt · 2011
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 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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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 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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Diffusion-convolutional neural networks
James Atwood and Don Towsley · 2016
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Graph isomorphism in quasipolynomial time
László Babai · 2016
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Deriving neural architectures from sequence and graph kernels
Tao Lei, Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2017
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Pointnet: Deep learning on point sets for 3d classification and segmentation
Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas · 2017
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A simple neural network module for relational reasoning
Adam Santoro, David Raposo, David G Barrett, Mateusz Malinowski, Razvan Pascanu, Peter Battaglia, and Timothy Lillicrap · 2017
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Deep sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Ruslan R Salakhutdinov, and Alexander J Smola · 2017
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Anonymous walk embeddings
Sergey Ivanov and Evgeny Burnaev · 2018
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Janossy pooling: Learning deep permutation-invariant functions for variable-size inputs
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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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Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst · 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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Gated graph sequence neural networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel · 2016
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Learning convolutional neural networks for graphs
Mathias Niepert, Mohamed Ahmed, and Konstantin Kutzkov · 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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Ryan L Murphy, Balasubramaniam Srinivasan, Vinayak Rao, and Bruno Ribeiro · 2018
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Adam Santoro, Felix Hill, David Barrett, Ari Morcos, and Timothy Lillicrap · 2018
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Graph attention networks
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Graph capsule convolutional neural networks
Saurabh Verma and Zhi-Li Zhang · 2018
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Representation learning on graphs with jumping knowledge networks
Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, and Stefanie Jegelka · 2018
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Hierarchical graph representation learning with differentiable pooling
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An end-to-end deep learning architecture for graph classification
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