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Conference Paper: Modeling and detecting community hierarchies

TitleModeling and detecting community hierarchies
Authors
Keywordscommunity detection
hierarchical structure
Issue Date2013
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013, v. 7953 LNCS, p. 160-175 How to Cite?
AbstractCommunity detection has in recent years emerged as an invaluable tool for describing and quantifying interactions in networks. In this paper we propose a theoretical model that explicitly formalizes both the tight connections within each community and the hierarchical nature of the communities. We further present an efficient algorithm that provably detects all the communities in our model. Experiments demonstrate that our definition successfully models real world communities, and our algorithm compares favorably with existing approaches. © 2013 Springer-Verlag.
Persistent Identifierhttp://hdl.handle.net/10722/341119
ISSN
2023 SCImago Journal Rankings: 0.606

 

DC FieldValueLanguage
dc.contributor.authorBalcan, Maria Florina-
dc.contributor.authorLiang, Yingyu-
dc.date.accessioned2024-03-13T08:40:18Z-
dc.date.available2024-03-13T08:40:18Z-
dc.date.issued2013-
dc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013, v. 7953 LNCS, p. 160-175-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10722/341119-
dc.description.abstractCommunity detection has in recent years emerged as an invaluable tool for describing and quantifying interactions in networks. In this paper we propose a theoretical model that explicitly formalizes both the tight connections within each community and the hierarchical nature of the communities. We further present an efficient algorithm that provably detects all the communities in our model. Experiments demonstrate that our definition successfully models real world communities, and our algorithm compares favorably with existing approaches. © 2013 Springer-Verlag.-
dc.languageeng-
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
dc.subjectcommunity detection-
dc.subjecthierarchical structure-
dc.titleModeling and detecting community hierarchies-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/978-3-642-39140-8_11-
dc.identifier.scopuseid_2-s2.0-84879849524-
dc.identifier.volume7953 LNCS-
dc.identifier.spage160-
dc.identifier.epage175-
dc.identifier.eissn1611-3349-

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