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Network immunization and virus propagation in email networks: experimental evaluation and analysis

Network immunization strategies have emerged as possible solutions to the challenges of virus propagation. In this paper, an existing interactive model is introduced and then improved in order to better characterize the way a virus spreads in email networks with different topologies. The model is us...

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Detalles Bibliográficos
Autores principales: Gao, Chao, Liu, Jiming, Zhong, Ning
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer-Verlag 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7088328/
https://www.ncbi.nlm.nih.gov/pubmed/32214580
http://dx.doi.org/10.1007/s10115-010-0321-0
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author Gao, Chao
Liu, Jiming
Zhong, Ning
author_facet Gao, Chao
Liu, Jiming
Zhong, Ning
author_sort Gao, Chao
collection PubMed
description Network immunization strategies have emerged as possible solutions to the challenges of virus propagation. In this paper, an existing interactive model is introduced and then improved in order to better characterize the way a virus spreads in email networks with different topologies. The model is used to demonstrate the effects of a number of key factors, notably nodes’ degree and betweenness. Experiments are then performed to examine how the structure of a network and human dynamics affects virus propagation. The experimental results have revealed that a virus spreads in two distinct phases and shown that the most efficient immunization strategy is the node-betweenness strategy. Moreover, those results have also explained why old virus can survive in networks nowadays from the aspects of human dynamics.
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spelling pubmed-70883282020-03-23 Network immunization and virus propagation in email networks: experimental evaluation and analysis Gao, Chao Liu, Jiming Zhong, Ning Knowl Inf Syst Regular Paper Network immunization strategies have emerged as possible solutions to the challenges of virus propagation. In this paper, an existing interactive model is introduced and then improved in order to better characterize the way a virus spreads in email networks with different topologies. The model is used to demonstrate the effects of a number of key factors, notably nodes’ degree and betweenness. Experiments are then performed to examine how the structure of a network and human dynamics affects virus propagation. The experimental results have revealed that a virus spreads in two distinct phases and shown that the most efficient immunization strategy is the node-betweenness strategy. Moreover, those results have also explained why old virus can survive in networks nowadays from the aspects of human dynamics. Springer-Verlag 2010-07-14 2011 /pmc/articles/PMC7088328/ /pubmed/32214580 http://dx.doi.org/10.1007/s10115-010-0321-0 Text en © Springer-Verlag London Limited 2010 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Regular Paper
Gao, Chao
Liu, Jiming
Zhong, Ning
Network immunization and virus propagation in email networks: experimental evaluation and analysis
title Network immunization and virus propagation in email networks: experimental evaluation and analysis
title_full Network immunization and virus propagation in email networks: experimental evaluation and analysis
title_fullStr Network immunization and virus propagation in email networks: experimental evaluation and analysis
title_full_unstemmed Network immunization and virus propagation in email networks: experimental evaluation and analysis
title_short Network immunization and virus propagation in email networks: experimental evaluation and analysis
title_sort network immunization and virus propagation in email networks: experimental evaluation and analysis
topic Regular Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7088328/
https://www.ncbi.nlm.nih.gov/pubmed/32214580
http://dx.doi.org/10.1007/s10115-010-0321-0
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