Epidemic Spreading on Weighted Co-Evolving Multiplex Networks

Song, B; Wu, H; Song, Y; Wang, X; Jiang, G

Epidemic Spreading on Weighted Co-Evolving Multiplex Networks

Song, B Wu, H Song, Y Wang, X

Jiang, G

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Mathematics, 2023, 11, (14)
Issue Date:: 2023-07-01

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Field	Value	Language
dc.contributor.author	Song, B
dc.contributor.author	Wu, H
dc.contributor.author	Song, Y
dc.contributor.author	Wang, X https://orcid.org/0000-0001-9439-6437
dc.contributor.author	Jiang, G
dc.date.accessioned	2024-04-21T09:17:55Z
dc.date.available	2024-04-21T09:17:55Z
dc.date.issued	2023-07-01
dc.identifier.citation	Mathematics, 2023, 11, (14)
dc.identifier.issn	2227-7390
dc.identifier.issn	2227-7390
dc.identifier.uri	http://hdl.handle.net/10453/178133
dc.description.abstract	The individual behaviors driven by information diffusion show an undeniable impact on the process of epidemic spreading and have been continuously evolving with the dynamic processes. In this paper, a novel weighted co-evolving multiplex network model is proposed to describe the interaction between information diffusion in online social networks and epidemic spreading in adaptive physical contact networks. Considering the difference in the connections between individuals, the heterogeneous rewiring rate, which is proportional to the strength of the connection, is introduced in our model. The simulation results show that the maximum infection scale decreases as the information acceptance probability grows, and the final infection decreases as the rewiring behaviors increase. Interestingly, an infection peak appears in our model due to the interaction between information diffusion and epidemic spread.
dc.language	English
dc.publisher	MDPI
dc.relation.ispartof	Mathematics
dc.relation.isbasedon	10.3390/math11143109
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	49 Mathematical sciences
dc.title	Epidemic Spreading on Weighted Co-Evolving Multiplex Networks
dc.type	Journal Article
utslib.citation.volume	11
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
utslib.copyright.status	open_access	*
dc.date.updated	2024-04-21T09:17:53Z
pubs.issue	14
pubs.publication-status	Published
pubs.volume	11
utslib.citation.issue	14

Abstract:

The individual behaviors driven by information diffusion show an undeniable impact on the process of epidemic spreading and have been continuously evolving with the dynamic processes. In this paper, a novel weighted co-evolving multiplex network model is proposed to describe the interaction between information diffusion in online social networks and epidemic spreading in adaptive physical contact networks. Considering the difference in the connections between individuals, the heterogeneous rewiring rate, which is proportional to the strength of the connection, is introduced in our model. The simulation results show that the maximum infection scale decreases as the information acceptance probability grows, and the final infection decreases as the rewiring behaviors increase. Interestingly, an infection peak appears in our model due to the interaction between information diffusion and epidemic spread.

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http://hdl.handle.net/10453/178133