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Graph neural networks (GNNs) have emerged recently as a powerful architecture for learning node and graph representations.
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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Canonical labelling of graphs in linear average time
László Babai and Ludik Kucera · 1979
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Multilayer Feedforward Networks are Universal Approximators
Kurt Hornik, Maxwell Stinchcombe, and Halbert White · 1989
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Shortest-path kernels on graphs
Karsten M Borgwardt and Hans-Peter Kriegel · 2005
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Protein function prediction via graph kernels
Karsten M Borgwardt, Cheng Soon Ong, Stefan Schönauer, SVN Vishwanathan, Alex J Smola, and Hans-Peter Kriegel · 2005
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A New Model for Learning in Graph Domains
Marco Gori, Gabriele Monfardini, and Franco Scarselli · 2005
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Computational Capabilities of Graph Neural Networks
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2008
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The Graph Neural Network Model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2009
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Efficient Graphlet Kernels for Large Graph Comparison
Nino Shervashidze, SVN Vishwanathan, Tobias Petri, Kurt Mehlhorn, and Karsten Borgwardt · 2009
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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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Like like alike - Joint Friendship and Interest Propagation in Social Networks
Shuang-Hong Yang, Bo Long, Alex Smola, Narayanan Sadagopan, Zhaohui Zheng, and Hongyuan Zha · 2011
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RolX: Structural Role Extraction & Mining in Large Graphs
Keith Henderson, Brian Gallagher, Tina Eliassi-Rad, Hanghang Tong, Sugato Basu, Leman Akoglu, Danai Koutra, Christos Faloutsos, and Lei Li · 2012
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Network-Based High Level Data Classification
Thiago Christiano Silva and Liang Zhao · 2012
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Gated Graph Sequence Neural Networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel · 2015
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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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On valid optimal assignment kernels and applications to graph classification
Nils M Kriege, Pierre-Louis Giscard, and Richard Wilson · 2016
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Learning Convolutional Neural Networks for Graphs
Mathias Niepert, Mohamed Ahmed, and Konstantin Kutzkov · 2016
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Geometric Deep Learning: Going beyond Euclidean data
Michael M Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, and Pierre Vandergheynst · 2017
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Organizational Data Classification Based on the Importance Concept of Complex Networks
Learning Structural Node Embeddings via Diffusion Wavelets
Claire Donnat, Marinka Zitnik, David Hallac, and Jure Leskovec · 2018
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A Property Testing Framework for the Theoretical Expressivity of Graph Kernels
Nils M Kriege, Christopher Morris, Anja Rey, and Christian Sohler · 2018
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Graph Attention Networks
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2018
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How Powerful are Graph Neural Networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 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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Murillo Guimarães Carneiro and Liang Zhao · 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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Introduction to property testing
Oded Goldreich · 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
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Deriving Neural Architectures from Sequence and Graph Kernels
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struc2vec: Learning Node Representations from Structural Identity
Leonardo FR Ribeiro, Pedro HP Saverese, and Daniel R Figueiredo · 2017
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