DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Jungeun | ko |
dc.contributor.author | Lim, Sungsu | ko |
dc.contributor.author | Lee, Jae-Gil | ko |
dc.contributor.author | Lee, Byung Suk | ko |
dc.date.accessioned | 2019-12-17T03:20:15Z | - |
dc.date.available | 2019-12-17T03:20:15Z | - |
dc.date.created | 2018-12-10 | - |
dc.date.issued | 2019-11 | - |
dc.identifier.citation | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, v.31, no.11, pp.2138 - 2150 | - |
dc.identifier.issn | 1041-4347 | - |
dc.identifier.uri | http://hdl.handle.net/10203/269780 | - |
dc.description.abstract | This 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.language | English | - |
dc.publisher | IEEE COMPUTER SOC | - |
dc.title | LinkBlackHole*: Robust Overlapping Community Detection Using Link Embedding | - |
dc.type | Article | - |
dc.identifier.wosid | 000500304600008 | - |
dc.identifier.scopusid | 2-s2.0-85054517072 | - |
dc.type.rims | ART | - |
dc.citation.volume | 31 | - |
dc.citation.issue | 11 | - |
dc.citation.beginningpage | 2138 | - |
dc.citation.endingpage | 2150 | - |
dc.citation.publicationname | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING | - |
dc.identifier.doi | 10.1109/TKDE.2018.2873750 | - |
dc.contributor.localauthor | Lee, Jae-Gil | - |
dc.contributor.nonIdAuthor | Lee, Byung Suk | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Community detection | - |
dc.subject.keywordAuthor | graph clustering | - |
dc.subject.keywordAuthor | overlapping communities | - |
dc.subject.keywordAuthor | link clustering | - |
dc.subject.keywordAuthor | graph drawing | - |
dc.subject.keywordAuthor | link embedding | - |
dc.subject.keywordPlus | COMPLEXITY | - |
dc.subject.keywordPlus | NETWORKS | - |
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