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Erschienen in: Social Network Analysis and Mining 1/2020

01.12.2020 | Original Article

Centrality-based epidemic control in complex social networks

verfasst von: Mohammadreza Doostmohammadian, Hamid R. Rabiee, Usman A. Khan

Erschienen in: Social Network Analysis and Mining | Ausgabe 1/2020

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Abstract

Recent progress in the areas of network science and control has shown a significant promise in understanding and analyzing epidemic processes. A well-known model to study epidemics processes used by both control and epidemiological research communities is the susceptible–infected–susceptible (SIS) dynamics to model the spread of disease/viruses over contact networks of infected and susceptible individuals. The SIS model has two metastable equilibria: one is called the endemic equilibrium and the other is known as the disease-free or healthy-state equilibrium. Control theory provides the tools to design control actions (allocating curing or vaccination resources) in order to achieve and stabilize the disease-free equilibrium. However, the control actions are often designed for the entire community. Based on the ideas developed in graph-theoretic control, this paper aims to study allocating curing resources to a target group instead of the entire community. This target group is selected based on centrality rank in different types of random networks. Our results show that specific graph properties are involved in the epidemic control. In particular, we show that (1) the clustering-coefficient and (2) the degree distribution of the network are effective in the selection of these target groups for epidemic control.

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Metadaten
Titel
Centrality-based epidemic control in complex social networks
verfasst von
Mohammadreza Doostmohammadian
Hamid R. Rabiee
Usman A. Khan
Publikationsdatum
01.12.2020
Verlag
Springer Vienna
Erschienen in
Social Network Analysis and Mining / Ausgabe 1/2020
Print ISSN: 1869-5450
Elektronische ISSN: 1869-5469
DOI
https://doi.org/10.1007/s13278-020-00638-7

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