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Local Graph Clustering with Network Lasso

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Jung, Alexander
dc.contributor.author Sarcheshmehpour, Yasmin
dc.date.accessioned 2021-08-04T06:43:54Z
dc.date.available 2021-08-04T06:43:54Z
dc.date.issued 2021
dc.identifier.citation Jung , A & Sarcheshmehpour , Y 2021 , ' Local Graph Clustering with Network Lasso ' , IEEE Signal Processing Letters , vol. 28 , 9298875 , pp. 106-110 . https://doi.org/10.1109/LSP.2020.3045832 en
dc.identifier.issn 1070-9908
dc.identifier.issn 1558-2361
dc.identifier.other PURE UUID: a15c89bc-6a9c-4d2d-818a-758cc2ae4d16
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/a15c89bc-6a9c-4d2d-818a-758cc2ae4d16
dc.identifier.other PURE LINK: http://www.scopus.com/inward/record.url?scp=85098748083&partnerID=8YFLogxK
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/65676585/Local_Graph_Clustering_With_Network_Lasso.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/108935
dc.description.abstract We study the statistical and computational properties of a network Lasso method for local graph clustering. The clusters delivered by nLasso can be characterized elegantly via network flows between cluster boundaries and seed nodes. While spectral clustering methods are guided by a minimization of the graph Laplacian quadratic form, nLasso minimizes the total variation of cluster indicator signals. As demonstrated theoretically and numerically, nLasso methods can handle very sparse clusters (chain-like) which are difficult for spectral clustering. We also verify that a primal-dual method for non-smooth optimization allows to approximate nLasso solutions with optimal worst-case convergence rate. en
dc.format.extent 5
dc.format.extent 106-110
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartofseries IEEE Signal Processing Letters en
dc.relation.ispartofseries Volume 28 en
dc.rights openAccess en
dc.title Local Graph Clustering with Network Lasso en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department Professorship Jung Alexander
dc.contributor.department Sharif University of Technology
dc.contributor.department Department of Computer Science en
dc.subject.keyword Clustering methods
dc.subject.keyword Convergence
dc.subject.keyword Laplace equations
dc.subject.keyword Message passing
dc.subject.keyword Minimization
dc.subject.keyword Optimization
dc.subject.keyword TV
dc.identifier.urn URN:NBN:fi:aalto-202108048179
dc.identifier.doi 10.1109/LSP.2020.3045832
dc.type.version publishedVersion

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