Group-based susceptible-infectious-susceptible model in large-scale directed networks

Wang, X; Song, B; Ni, W; Liu, RP; Guo, YJ; Niu, X; Zheng, K

Group-based susceptible-infectious-susceptible model in large-scale directed networks

Wang, X

Song, B Ni, W

Liu, RP

Guo, YJ

Niu, X Zheng, K

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Publication Type:: Journal Article
Citation:: Security and Communication Networks, 2019, 2019
Issue Date:: 2019-01-01

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Field	Value	Language
dc.contributor.author	Wang, X https://orcid.org/0000-0001-9439-6437	en_US
dc.contributor.author	Song, B	en_US
dc.contributor.author	Ni, W https://orcid.org/0000-0002-4933-594X	en_US
dc.contributor.author	Liu, RP https://orcid.org/0000-0001-7001-6305	en_US
dc.contributor.author	Guo, YJ https://orcid.org/0000-0001-6008-9682	en_US
dc.contributor.author	Niu, X	en_US
dc.contributor.author	Zheng, K	en_US
dc.date.issued	2019-01-01	en_US
dc.identifier.citation	Security and Communication Networks, 2019, 2019	en_US
dc.identifier.issn	1939-0114	en_US
dc.identifier.uri	http://hdl.handle.net/10453/130113
dc.description.abstract	© 2019 Xu Wang et al. Epidemic models trade the modeling accuracy for complexity reduction. This paper proposes to group vertices in directed graphs based on connectivity and carries out epidemic spread analysis on the group basis, thereby substantially reducing the modeling complexity while preserving the modeling accuracy. A group-based continuous-time Markov SIS model is developed. The adjacency matrix of the network is also collapsed according to the grouping, to evaluate the Jacobian matrix of the group-based continuous-time Markov model. By adopting the mean-field approximation on the groups of nodes and links, the model complexity is significantly reduced as compared with previous topological epidemic models. An epidemic threshold is deduced based on the spectral radius of the collapsed adjacency matrix. The epidemic threshold is proved to be dependent on network structure and interdependent of the network scale. Simulation results validate the analytical epidemic threshold and confirm the asymptotical accuracy of the proposed epidemic model.	en_US
dc.relation.ispartof	Security and Communication Networks	en_US
dc.relation.isbasedon	10.1155/2019/1657164	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Group-based susceptible-infectious-susceptible model in large-scale directed networks	en_US
dc.type	Journal Article
utslib.citation.volume	2019	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	1117 Public Health and Health Services	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0802 Computation Theory and Mathematics	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	0899 Other Information and Computing Sciences	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/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	2019	en_US

Abstract:

© 2019 Xu Wang et al. Epidemic models trade the modeling accuracy for complexity reduction. This paper proposes to group vertices in directed graphs based on connectivity and carries out epidemic spread analysis on the group basis, thereby substantially reducing the modeling complexity while preserving the modeling accuracy. A group-based continuous-time Markov SIS model is developed. The adjacency matrix of the network is also collapsed according to the grouping, to evaluate the Jacobian matrix of the group-based continuous-time Markov model. By adopting the mean-field approximation on the groups of nodes and links, the model complexity is significantly reduced as compared with previous topological epidemic models. An epidemic threshold is deduced based on the spectral radius of the collapsed adjacency matrix. The epidemic threshold is proved to be dependent on network structure and interdependent of the network scale. Simulation results validate the analytical epidemic threshold and confirm the asymptotical accuracy of the proposed epidemic model.

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http://hdl.handle.net/10453/130113