DC FieldValueLanguage
dc.contributor.authorTaheri, Seyed Mohammad-
dc.contributor.authorMahyar, Hamidreza-
dc.contributor.authorFirouzi, Mohammad-
dc.contributor.authorGhalebi K., Elahe-
dc.contributor.authorGrosu, Radu-
dc.contributor.authorMovaghar, Ali-
dc.date.accessioned2020-06-27T18:32:54Z-
dc.date.issued2017-
dc.identifier.issn1869-5469-
dc.identifier.urihttps://resolver.obvsg.at/urn:nbn:at:at-ubtuw:3-4235-
dc.identifier.urihttp://hdl.handle.net/20.500.12708/947-
dc.description.abstractMeasuring centrality in a social network, especially in bipartite mode, poses many challenges, for example, the requirement of full knowledge of the network topology, and the lack of properly detecting top-k behavioral representative users. To overcome the above mentioned challenges, we propose HellRank, an accurate centrality measure for identifying central nodes in bipartite social networks. HellRank is based on the Hellinger distance between two nodes on the same side of a bipartite network. We theoretically analyze the impact of this distance on a bipartite network and find upper and lower bounds for it. The computation of the HellRank centrality measure can be distributed, by letting each node uses local information only on its immediate neighbors. Consequently, one does not need a central entity that has full knowledge of the network topological structure. We experimentally evaluate the performance of the HellRank measure in correlation with other centrality measures on real-world networks. The results show partial ranking similarity between the HellRank and the other conventional metrics according to the Kendall and Spearman rank correlation coefficient.en
dc.languageEnglish-
dc.language.isoen-
dc.publisherSpringer Vienna-
dc.relation.ispartofSocial Network Analysis and Mining-
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/deed.de-
dc.subjectBipartite social networksen
dc.subjectTop-k central nodesen
dc.subjectHellinger distanceen
dc.subjectRecommender systemsen
dc.titleHellRank: a Hellinger-based centrality measure for bipartite social networksen
dc.typeArticleen
dc.typeArtikelde
dc.rights.holderThe Author(s) 2017-
dc.type.categoryResearch Articleen
dc.type.categoryForschungsartikelde
tuw.versionvor-
dcterms.isPartOf.titleSocial Network Analysis and Mining-
tuw.publication.orgunitE191 - Institut für Computer Engineering-
tuw.publisher.doi10.1007/s13278-017-0440-7-
dc.identifier.libraryidAC15188640-
dc.identifier.urnurn:nbn:at:at-ubtuw:3-4235-
dc.rights.identifierCC BY 4.0-
item.fulltextwith Fulltext-
item.openairetypeArticle-
item.openairetypeArtikel-
item.cerifentitytypePublications-
item.cerifentitytypePublications-
item.languageiso639-1en-
item.grantfulltextopen-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
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