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Article: Percolation Centrality: Quantifying Graph-Theoretic Impact of Nodes during Percolation in Networks

TitlePercolation Centrality: Quantifying Graph-Theoretic Impact of Nodes during Percolation in Networks
Authors
Issue Date2013
PublisherPublic Library of Science. The Journal's web site is located at http://www.plosone.org/home.action
Citation
PLoS ONE, 2013, v. 8 n. 1, article no. e53095 How to Cite?
AbstractA number of centrality measures are available to determine the relative importance of a node in a complex network, and betweenness is prominent among them. However, the existing centrality measures are not adequate in network percolation scenarios (such as during infection transmission in a social network of individuals, spreading of computer viruses on computer networks, or transmission of disease over a network of towns) because they do not account for the changing percolation states of individual nodes. We propose a new measure, percolation centrality, that quantifies relative impact of nodes based on their topological connectivity, as well as their percolation states. The measure can be extended to include random walk based definitions, and its computational complexity is shown to be of the same order as that of betweenness centrality. We demonstrate the usage of percolation centrality by applying it to a canonical network as well as simulated and real world scale-free and random networks. © 2013 Piraveenan et al.
Persistent Identifierhttp://hdl.handle.net/10722/194492
ISSN
2015 Impact Factor: 3.057
2015 SCImago Journal Rankings: 1.395
PubMed Central ID
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorPiraveenan, M-
dc.contributor.authorProkopenko, M-
dc.contributor.authorHossain, L-
dc.date.accessioned2014-01-30T03:32:39Z-
dc.date.available2014-01-30T03:32:39Z-
dc.date.issued2013-
dc.identifier.citationPLoS ONE, 2013, v. 8 n. 1, article no. e53095-
dc.identifier.issn1932-6203-
dc.identifier.urihttp://hdl.handle.net/10722/194492-
dc.description.abstractA number of centrality measures are available to determine the relative importance of a node in a complex network, and betweenness is prominent among them. However, the existing centrality measures are not adequate in network percolation scenarios (such as during infection transmission in a social network of individuals, spreading of computer viruses on computer networks, or transmission of disease over a network of towns) because they do not account for the changing percolation states of individual nodes. We propose a new measure, percolation centrality, that quantifies relative impact of nodes based on their topological connectivity, as well as their percolation states. The measure can be extended to include random walk based definitions, and its computational complexity is shown to be of the same order as that of betweenness centrality. We demonstrate the usage of percolation centrality by applying it to a canonical network as well as simulated and real world scale-free and random networks. © 2013 Piraveenan et al.-
dc.languageeng-
dc.publisherPublic Library of Science. The Journal's web site is located at http://www.plosone.org/home.action-
dc.relation.ispartofPLoS ONE-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titlePercolation Centrality: Quantifying Graph-Theoretic Impact of Nodes during Percolation in Networks-
dc.typeArticle-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1371/journal.pone.0053095-
dc.identifier.pmid23349699-
dc.identifier.pmcidPMC3551907-
dc.identifier.scopuseid_2-s2.0-84872804191-
dc.identifier.hkuros240213-
dc.identifier.volume8-
dc.identifier.issue1-
dc.identifier.isiWOS:000314019100015-

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