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The Impact of the Network Topology on the Viral Prevalence: A Node-Based Approach

This paper addresses the impact of the structure of the viral propagation network on the viral prevalence. For that purpose, a new epidemic model of computer virus, known as the node-based SLBS model, is proposed. Our analysis shows that the maximum eigenvalue of the underlying network is a key fact...

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Detalles Bibliográficos
Autores principales: Yang, Lu-Xing, Draief, Moez, Yang, Xiaofan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4519130/
https://www.ncbi.nlm.nih.gov/pubmed/26222539
http://dx.doi.org/10.1371/journal.pone.0134507
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author Yang, Lu-Xing
Draief, Moez
Yang, Xiaofan
author_facet Yang, Lu-Xing
Draief, Moez
Yang, Xiaofan
author_sort Yang, Lu-Xing
collection PubMed
description This paper addresses the impact of the structure of the viral propagation network on the viral prevalence. For that purpose, a new epidemic model of computer virus, known as the node-based SLBS model, is proposed. Our analysis shows that the maximum eigenvalue of the underlying network is a key factor determining the viral prevalence. Specifically, the value range of the maximum eigenvalue is partitioned into three subintervals: viruses tend to extinction very quickly or approach extinction or persist depending on into which subinterval the maximum eigenvalue of the propagation network falls. Consequently, computer virus can be contained by adjusting the propagation network so that its maximum eigenvalue falls into the desired subinterval.
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spelling pubmed-45191302015-07-31 The Impact of the Network Topology on the Viral Prevalence: A Node-Based Approach Yang, Lu-Xing Draief, Moez Yang, Xiaofan PLoS One Research Article This paper addresses the impact of the structure of the viral propagation network on the viral prevalence. For that purpose, a new epidemic model of computer virus, known as the node-based SLBS model, is proposed. Our analysis shows that the maximum eigenvalue of the underlying network is a key factor determining the viral prevalence. Specifically, the value range of the maximum eigenvalue is partitioned into three subintervals: viruses tend to extinction very quickly or approach extinction or persist depending on into which subinterval the maximum eigenvalue of the propagation network falls. Consequently, computer virus can be contained by adjusting the propagation network so that its maximum eigenvalue falls into the desired subinterval. Public Library of Science 2015-07-29 /pmc/articles/PMC4519130/ /pubmed/26222539 http://dx.doi.org/10.1371/journal.pone.0134507 Text en © 2015 Yang et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Yang, Lu-Xing
Draief, Moez
Yang, Xiaofan
The Impact of the Network Topology on the Viral Prevalence: A Node-Based Approach
title The Impact of the Network Topology on the Viral Prevalence: A Node-Based Approach
title_full The Impact of the Network Topology on the Viral Prevalence: A Node-Based Approach
title_fullStr The Impact of the Network Topology on the Viral Prevalence: A Node-Based Approach
title_full_unstemmed The Impact of the Network Topology on the Viral Prevalence: A Node-Based Approach
title_short The Impact of the Network Topology on the Viral Prevalence: A Node-Based Approach
title_sort impact of the network topology on the viral prevalence: a node-based approach
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4519130/
https://www.ncbi.nlm.nih.gov/pubmed/26222539
http://dx.doi.org/10.1371/journal.pone.0134507
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