Understanding and extending subgraph GNNs by rethinking their symmetries

Understanding and extending subgraph GNNs by rethinking their symmetries

Subgraph GNNs are a recent class of expressive Graph Neural Networks (GNNs) which model graphs as collections of subgraphs. So far, the design space of possible Subgraph GNN architectures as well as their basic theoretical properties are still largely unexplored. In this paper, we study the most pro...

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Dades bibliogràfiques
Autors principals:	Frasca, F, Bevilacqua, B, Bronstein, M, Maron, H
Format:	Conference item
Idioma:	English
Publicat:	Curran Associates 2022

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