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Message Passing Neural Networks (MPNNs) are a common type of Graph Neural Network (GNN), in which each node's representation is computed recursively by aggregating representations (messages) from its immediate neighbors akin to a star-shaped pattern.
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Multilayer feedforward networks are universal approximators
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Mathias Niepert, Mohamed Ahmed, and Konstantin Kutzkov · 2016
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Inductive representation learning on large graphs
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Deep sets
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Motifnet: a motif-based graph convolutional network for directed graphs
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Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, and Aram Galstyan · 2019
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On the equivalence between graph isomorphism testing and function approximation with gnns
Zhengdao Chen, Soledad Villar, Lei Chen, and Joan Bruna · 2019
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A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip · 2020
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Can graph neural networks count substructures?
Chen Zhengdao, Chen Lei, Villar Soledad, and Joan Bruna · 2020
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The surprising power of graph neural networks with random node initialization
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