Show simple item record

dc.contributor.authorGrattarola, Daniele
dc.contributor.authorZambon, Daniele
dc.contributor.authorBianchi, Filippo Maria
dc.contributor.authorAlippi, Cesare
dc.date.accessioned2022-12-09T13:41:19Z
dc.date.available2022-12-09T13:41:19Z
dc.date.issued2022-07-21
dc.description.abstractMany recent works in the field of graph machine learning have introduced pooling operators to reduce the size of graphs. In this article, we present an operational framework to unify this vast and diverse literature by describing pooling operators as the combination of three functions: selection, reduction, and connection (SRC). We then introduce a taxonomy of pooling operators, based on some of their key characteristics and implementation differences under the SRC framework. Finally, we propose three criteria to evaluate the performance of pooling operators and use them to investigate the behavior of different operators on a variety of tasks.en_US
dc.identifier.citationGrattarola, Zambon, Bianchi, Alippi. Understanding Pooling in Graph Neural Networks. IEEE Transactions on Neural Networks and Learning Systems. 2022en_US
dc.identifier.cristinIDFRIDAID 2069624
dc.identifier.doi10.1109/TNNLS.2022.3190922
dc.identifier.issn2162-237X
dc.identifier.issn2162-2388
dc.identifier.urihttps://hdl.handle.net/10037/27775
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.relation.journalIEEE Transactions on Neural Networks and Learning Systems
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2022 The Author(s)en_US
dc.titleUnderstanding Pooling in Graph Neural Networksen_US
dc.type.versionacceptedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


File(s) in this item

Thumbnail

This item appears in the following collection(s)

Show simple item record