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Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks

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dc.contributor.authorChen, Zhiqianen
dc.contributor.authorChen, Fanglanen
dc.contributor.authorZhang, Leien
dc.contributor.authorJi, Taoranen
dc.contributor.authorFu, Kaiqunen
dc.contributor.authorZhao, Liangen
dc.contributor.authorChen, Fengen
dc.contributor.authorWu, Lingfeien
dc.contributor.authorAggarwal, Charuen
dc.contributor.authorLu, Chang-Tienen
dc.date.accessioned2023-11-02T13:02:42Zen
dc.date.available2023-11-02T13:02:42Zen
dc.date.issued2023-10en
dc.date.updated2023-11-01T08:00:34Zen
dc.description.abstractDeep learning's performance has been extensively recognized recently. Graph neural networks (GNNs) are designed to deal with graph-structural data that classical deep learning does not easily manage. Since most GNNs were created using distinct theories, direct comparisons are impossible. Prior research has primarily concentrated on categorizing existing models, with little attention paid to their intrinsic connections. The purpose of this study is to establish a unified framework that integrates GNNs based on spectral graph and approximation theory. The framework incorporates a strong integration between spatial- and spectral-based GNNs while tightly associating approaches that exist within each respective domain.en
dc.description.versionAccepted versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1145/3627816en
dc.identifier.urihttp://hdl.handle.net/10919/116587en
dc.language.isoenen
dc.publisherACMen
dc.rightsIn Copyrighten
dc.rights.holderThe author(s)en
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.titleBridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networksen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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