Using epidemic betweenness to measure the influence of users in complex networks

Wen, S; Jiang, J; Liu, B; Xiang, Y; Zhou, W

Using epidemic betweenness to measure the influence of users in complex networks

Wen, S Jiang, J Liu, B

Xiang, Y Zhou, W

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Journal of Network and Computer Applications, 2017, 78, pp. 288-299
Issue Date:: 2017-01-15

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Field	Value	Language
dc.contributor.author	Wen, S
dc.contributor.author	Jiang, J
dc.contributor.author	Liu, B https://orcid.org/0000-0002-3603-6617
dc.contributor.author	Xiang, Y
dc.contributor.author	Zhou, W https://orcid.org/0000-0002-1680-2521
dc.date.accessioned	2022-08-09T04:58:47Z
dc.date.available	2022-08-09T04:58:47Z
dc.date.issued	2017-01-15
dc.identifier.citation	Journal of Network and Computer Applications, 2017, 78, pp. 288-299
dc.identifier.issn	1084-8045
dc.identifier.issn	1095-8592
dc.identifier.uri	http://hdl.handle.net/10453/159803
dc.description.abstract	Betweenness is a measure of the centrality of a node in a network, and is normally calculated as the fraction of shortest paths, random walk paths or flow units between node pairs that pass through the node of interest. Betweenness is, in some sense, a measure of the influence a node possesses over the spread of information in the network. However, the traditional betweenness is based on the information dissemination from one node to another. This is conceptually not suitable for the epidemics in which information is disseminated from one node to multiple neighboring nodes and destinations. To address this problem, we propose a novel betweenness measure based on epidemics. The epidemic betweenness counts the average number of the following nodes influenced by the node of interest after it becomes the epidemic source or an intermediary. This measure reflects the potential influence of a node to any epidemic in complex networks. To justify this measure, we introduce real complex networks and estimate the average influential scale of each node in epidemics through a large number of simulations. We compare the simulation results to those of the epidemic betweenness and another seven classic measures, such as Eigenvector and Katz. We further provide correlation studies to expose the differences of the epidemic betweenness in capturing influential nodes. We find that the epidemic betweenness is exclusively the measure that accurately present the potential influence of each node in epidemics. Finally, as an example of application, the epidemic betweenness measure explains the finding in recent research that unpopular users (nodes with small degree) could also lead to large cascades of epidemics.
dc.language	English
dc.publisher	Elsevier
dc.relation.ispartof	Journal of Network and Computer Applications
dc.relation.isbasedon	10.1016/j.jnca.2016.10.018
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 1005 Communications Technologies
dc.subject.classification	Networking & Telecommunications
dc.title	Using epidemic betweenness to measure the influence of users in complex networks
dc.type	Journal Article
utslib.citation.volume	78
utslib.for	0805 Distributed Computing
utslib.for	1005 Communications Technologies
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-08-09T04:58:45Z
pubs.publication-status	Published
pubs.volume	78

Abstract:

Betweenness is a measure of the centrality of a node in a network, and is normally calculated as the fraction of shortest paths, random walk paths or flow units between node pairs that pass through the node of interest. Betweenness is, in some sense, a measure of the influence a node possesses over the spread of information in the network. However, the traditional betweenness is based on the information dissemination from one node to another. This is conceptually not suitable for the epidemics in which information is disseminated from one node to multiple neighboring nodes and destinations. To address this problem, we propose a novel betweenness measure based on epidemics. The epidemic betweenness counts the average number of the following nodes influenced by the node of interest after it becomes the epidemic source or an intermediary. This measure reflects the potential influence of a node to any epidemic in complex networks. To justify this measure, we introduce real complex networks and estimate the average influential scale of each node in epidemics through a large number of simulations. We compare the simulation results to those of the epidemic betweenness and another seven classic measures, such as Eigenvector and Katz. We further provide correlation studies to expose the differences of the epidemic betweenness in capturing influential nodes. We find that the epidemic betweenness is exclusively the measure that accurately present the potential influence of each node in epidemics. Finally, as an example of application, the epidemic betweenness measure explains the finding in recent research that unpopular users (nodes with small degree) could also lead to large cascades of epidemics.

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http://hdl.handle.net/10453/159803