LinkBlackHole*: Robust Overlapping Community Detection Using Link Embedding

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dc.contributor.authorKim, Jungeunko
dc.contributor.authorLim, Sungsuko
dc.contributor.authorLee, Jae-Gilko
dc.contributor.authorLee, Byung Sukko
dc.date.accessioned2019-12-17T03:20:15Z-
dc.date.available2019-12-17T03:20:15Z-
dc.date.created2018-12-10-
dc.date.issued2019-11-
dc.identifier.citationIEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, v.31, no.11, pp.2138 - 2150-
dc.identifier.issn1041-4347-
dc.identifier.urihttp://hdl.handle.net/10203/269780-
dc.description.abstractThis paper proposes LinkBlackHole*, a novel algorithm for finding communities that are (i) overlapping in nodes and (ii) mixing (not separating clearly) in links. There has been a small body of work in each category, but this paper is the first one that addresses both. LinkBlackHole* is a merger of our earlier two algorithms, LinkSCAN* and BlackHole, inheriting their advantages in support of highly-mixed overlapping communities. The former is used to handle overlapping nodes, and the latter to handle mixing links in finding communities. Like LinkSCAN and its more efficient variant LinkSCAN*, this paper presents LinkBlackHole and its more efficient variant LinkBlackHole*, which reduces the number of links through random sampling. Thorough experiments show superior quality of the communities detected by LinkBlackHole* and LinkBlackHole to those detected by other state-of-the-art algorithms. In addition, LinkBlackHole* shows high resilience to the link sampling effect, and its running time scales up almost linearly with the number of links in a network.-
dc.languageEnglish-
dc.publisherIEEE COMPUTER SOC-
dc.titleLinkBlackHole*: Robust Overlapping Community Detection Using Link Embedding-
dc.typeArticle-
dc.identifier.wosid000500304600008-
dc.identifier.scopusid2-s2.0-85054517072-
dc.type.rimsART-
dc.citation.volume31-
dc.citation.issue11-
dc.citation.beginningpage2138-
dc.citation.endingpage2150-
dc.citation.publicationnameIEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING-
dc.identifier.doi10.1109/TKDE.2018.2873750-
dc.contributor.localauthorLee, Jae-Gil-
dc.contributor.nonIdAuthorLee, Byung Suk-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorCommunity detection-
dc.subject.keywordAuthorgraph clustering-
dc.subject.keywordAuthoroverlapping communities-
dc.subject.keywordAuthorlink clustering-
dc.subject.keywordAuthorgraph drawing-
dc.subject.keywordAuthorlink embedding-
dc.subject.keywordPlusCOMPLEXITY-
dc.subject.keywordPlusNETWORKS-
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