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Conference Paper: Hybrid centrality measures for binary and weighted networks

TitleHybrid centrality measures for binary and weighted networks
Authors
Issue Date2013
PublisherSpringer Verlag.
Citation
The 3rd Workshop on Complex Networks (CompleNet 2012), Melbourne, Florida, USA, 7-9 March 2012. In Studies in Computational Intelligence, 2013, v. 424, p. 1-7 How to Cite?
AbstractExisting centrality measures for social network analysis suggest the importance of an actor and give consideration to actor's given structural position in a network. These existing measures suggest specific attribute of an actor (i.e., popularity, accessibility, and brokerage behavior). In this study, we propose new hybrid centrality measures (i.e., Degree-Degree, Degree-Closeness and Degree-Betweenness), by combining existing measures (i.e., degree, closeness and betweenness) with a proposition to better understand the importance of actors in a given network. Generalized set of measures are also proposed for weighted networks. Our analysis of co-authorship networks dataset suggests significant correlation of our proposed new centrality measures (especially weighted networks) than traditional centrality measures with performance of the scholars. Thus, they are useful measures which can be used instead of traditional measures to show prominence of the actors in a network. © 2013 Springer-Verlag Berlin Heidelberg.
DescriptionTechnical Session 1: Network Metrics And Models
Studies in Computational Intelligence, Vol 424 entitled: Complex Networks
Persistent Identifierhttp://hdl.handle.net/10722/194465
ISBN
ISSN
2015 SCImago Journal Rankings: 0.187

 

DC FieldValueLanguage
dc.contributor.authorAbbasi, A-
dc.contributor.authorHossain, L-
dc.date.accessioned2014-01-30T03:32:37Z-
dc.date.available2014-01-30T03:32:37Z-
dc.date.issued2013-
dc.identifier.citationThe 3rd Workshop on Complex Networks (CompleNet 2012), Melbourne, Florida, USA, 7-9 March 2012. In Studies in Computational Intelligence, 2013, v. 424, p. 1-7-
dc.identifier.isbn9783642302862-
dc.identifier.issn1860-949X-
dc.identifier.urihttp://hdl.handle.net/10722/194465-
dc.descriptionTechnical Session 1: Network Metrics And Models-
dc.descriptionStudies in Computational Intelligence, Vol 424 entitled: Complex Networks-
dc.description.abstractExisting centrality measures for social network analysis suggest the importance of an actor and give consideration to actor's given structural position in a network. These existing measures suggest specific attribute of an actor (i.e., popularity, accessibility, and brokerage behavior). In this study, we propose new hybrid centrality measures (i.e., Degree-Degree, Degree-Closeness and Degree-Betweenness), by combining existing measures (i.e., degree, closeness and betweenness) with a proposition to better understand the importance of actors in a given network. Generalized set of measures are also proposed for weighted networks. Our analysis of co-authorship networks dataset suggests significant correlation of our proposed new centrality measures (especially weighted networks) than traditional centrality measures with performance of the scholars. Thus, they are useful measures which can be used instead of traditional measures to show prominence of the actors in a network. © 2013 Springer-Verlag Berlin Heidelberg.-
dc.languageeng-
dc.publisherSpringer Verlag.-
dc.relation.ispartofStudies in Computational Intelligence-
dc.rightsThe original publication is available at www.springerlink.com-
dc.titleHybrid centrality measures for binary and weighted networks-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/978-3-642-30287-9_1-
dc.identifier.scopuseid_2-s2.0-84867456284-
dc.identifier.hkuros240195-
dc.identifier.volume424-
dc.identifier.spage1-
dc.identifier.epage7-
dc.publisher.placeGermany-

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