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dc.contributor.authorBianchi, Filippo Maria
dc.contributor.authorLachi, Veronica
dc.date.accessioned2024-02-26T13:28:32Z
dc.date.available2024-02-26T13:28:32Z
dc.date.issued2023
dc.description.abstractIn Graph Neural Networks (GNNs), hierarchical pooling operators generate local summaries of the data by coarsening the graph structure and the vertex features. While considerable attention has been devoted to analyzing the expressive power of message-passing (MP) layers in GNNs, a study on how graph pooling affects the expressiveness of a GNN is still lacking. Additionally, despite the recent advances in the design of pooling operators, there is not a principled criterion to compare them. In this work, we derive sufficient conditions for a pooling operator to fully preserve the expressive power of the MP layers before it. These conditions serve as a universal and theoretically grounded criterion for choosing among existing pooling operators or designing new ones. Based on our theoretical findings, we analyze several existing pooling operators and identify those that fail to satisfy the expressiveness conditions. Finally, we introduce an experimental setup to verify empirically the expressive power of a GNN equipped with pooling layers, in terms of its capability to perform a graph isomorphism test.en_US
dc.identifier.citationBianchi, Lachi. The expressive power of pooling in Graph Neural Networks. Advances in Neural Information Processing Systems. 2023en_US
dc.identifier.cristinIDFRIDAID 2181672
dc.identifier.doi10.48550/arXiv.2304.01575
dc.identifier.issn1049-5258
dc.identifier.urihttps://hdl.handle.net/10037/33043
dc.language.isoengen_US
dc.publisherNeurIPS Proceedingsen_US
dc.relation.journalAdvances in Neural Information Processing Systems
dc.relation.urihttps://arxiv.org/pdf/2304.01575.pdf
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2023 The Author(s)en_US
dc.subjectVDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550::Datateknologi: 551en_US
dc.subjectVDP::Technology: 500::Information and communication technology: 550::Computer technology: 551en_US
dc.subjectMachine learning / Machine learningen_US
dc.titleThe expressive power of pooling in Graph Neural Networksen_US
dc.type.versionacceptedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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