Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting

While Graph Neural Networks (GNNs) have achieved remarkable results in a variety of applications, recent studies exposed important shortcomings in their ability to capture the structure of the underlying graph. It has been shown that the expressive power of standard GNNs is bounded by the Weisfeiler...

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Publié dans:	IEEE transactions on pattern analysis and machine intelligence. - 1979. - 45(2023), 1 vom: 24. Jan., Seite 657-668
Auteur principal:	Bouritsas, Giorgos (Auteur)
Autres auteurs:	Frasca, Fabrizio, Zafeiriou, Stefanos, Bronstein, Michael M
Format:	Article en ligne
Langue:	English
Publié:	2023
Accès à la collection:	IEEE transactions on pattern analysis and machine intelligence
Sujets:	Journal Article