Modeling and analysis of epidemic spreading on community networks with heterogeneity

Li, C; Jiang, GP; Song, Y; Xia, L; Li, Y; Song, B

Modeling and analysis of epidemic spreading on community networks with heterogeneity

Li, C Jiang, GP Song, Y Xia, L Li, Y Song, B

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Publication Type:: Journal Article
Citation:: Journal of Parallel and Distributed Computing, 2018, 119 pp. 136 - 145
Issue Date:: 2018-09-01

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Field	Value	Language
dc.contributor.author	Li, C	en_US
dc.contributor.author	Jiang, GP	en_US
dc.contributor.author	Song, Y	en_US
dc.contributor.author	Xia, L	en_US
dc.contributor.author	Li, Y	en_US
dc.contributor.author	Song, B	en_US
dc.date.issued	2018-09-01	en_US
dc.identifier.citation	Journal of Parallel and Distributed Computing, 2018, 119 pp. 136 - 145	en_US
dc.identifier.issn	0743-7315	en_US
dc.identifier.uri	http://hdl.handle.net/10453/132222
dc.description.abstract	© 2018 Elsevier Inc. A large number of real world networks exhibit community structure, and different communities may often possess heterogeneity. In this paper, considering the heterogeneity among communities, we construct a new community network model in which the communities show significant differences in average degree. Based on this heterogeneous community network, we propose a novel mathematical epidemic model for each community and study the epidemic dynamics in this network model. We find that the location of the initial infection node only affects the spreading velocity and barely influences the epidemic prevalence. And the epidemic threshold of entire network decreases with the increase of heterogeneity among communities. Moreover, the epidemic prevalence increases with the increase of heterogeneity around the epidemic threshold, while the converse situation holds when the infection rate is much greater than the epidemic threshold.	en_US
dc.relation.ispartof	Journal of Parallel and Distributed Computing	en_US
dc.relation.isbasedon	10.1016/j.jpdc.2018.04.009	en_US
dc.subject.classification	Distributed Computing	en_US
dc.title	Modeling and analysis of epidemic spreading on community networks with heterogeneity	en_US
dc.type	Journal Article
utslib.citation.volume	119	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	0803 Computer Software	en_US
pubs.embargo.period	Not known	en_US
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/Students
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	119	en_US

Abstract:

© 2018 Elsevier Inc. A large number of real world networks exhibit community structure, and different communities may often possess heterogeneity. In this paper, considering the heterogeneity among communities, we construct a new community network model in which the communities show significant differences in average degree. Based on this heterogeneous community network, we propose a novel mathematical epidemic model for each community and study the epidemic dynamics in this network model. We find that the location of the initial infection node only affects the spreading velocity and barely influences the epidemic prevalence. And the epidemic threshold of entire network decreases with the increase of heterogeneity among communities. Moreover, the epidemic prevalence increases with the increase of heterogeneity around the epidemic threshold, while the converse situation holds when the infection rate is much greater than the epidemic threshold.

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http://hdl.handle.net/10453/132222