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- Publisher Website: 10.1007/978-3-642-39140-8_11
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Conference Paper: Modeling and detecting community hierarchies
Title | Modeling and detecting community hierarchies |
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Authors | |
Keywords | community detection hierarchical structure |
Issue Date | 2013 |
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? |
Abstract | Community 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 Identifier | http://hdl.handle.net/10722/341119 |
ISSN | 2023 SCImago Journal Rankings: 0.606 |
DC Field | Value | Language |
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dc.contributor.author | Balcan, Maria Florina | - |
dc.contributor.author | Liang, Yingyu | - |
dc.date.accessioned | 2024-03-13T08:40:18Z | - |
dc.date.available | 2024-03-13T08:40:18Z | - |
dc.date.issued | 2013 | - |
dc.identifier.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 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | http://hdl.handle.net/10722/341119 | - |
dc.description.abstract | Community 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.language | eng | - |
dc.relation.ispartof | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.subject | community detection | - |
dc.subject | hierarchical structure | - |
dc.title | Modeling and detecting community hierarchies | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1007/978-3-642-39140-8_11 | - |
dc.identifier.scopus | eid_2-s2.0-84879849524 | - |
dc.identifier.volume | 7953 LNCS | - |
dc.identifier.spage | 160 | - |
dc.identifier.epage | 175 | - |
dc.identifier.eissn | 1611-3349 | - |