Spreading predictability in complex networks

Abstract Many state-of-the-art researches focus on predicting infection scale or threshold in infectious diseases or rumor and give the vaccination strategies correspondingly. In these works, most of them assume that the infection probability and initially infected individuals are known at the very...

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Autores principales:	Na Zhao, Jian Wang, Yong Yu, Jun-Yan Zhao, Duan-Bing Chen
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e4ff672e9b7b4fb8b4fb6ec569b66632
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spelling	oai:doaj.org-article:e4ff672e9b7b4fb8b4fb6ec569b666322021-12-02T16:14:04ZSpreading predictability in complex networks10.1038/s41598-021-93611-z2045-2322https://doaj.org/article/e4ff672e9b7b4fb8b4fb6ec569b666322021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93611-zhttps://doaj.org/toc/2045-2322Abstract Many state-of-the-art researches focus on predicting infection scale or threshold in infectious diseases or rumor and give the vaccination strategies correspondingly. In these works, most of them assume that the infection probability and initially infected individuals are known at the very beginning. Generally, infectious diseases or rumor has been spreading for some time when it is noticed. How to predict which individuals will be infected in the future only by knowing the current snapshot becomes a key issue in infectious diseases or rumor control. In this report, a prediction model based on snapshot is presented to predict the potentially infected individuals in the future, not just the macro scale of infection. Experimental results on synthetic and real networks demonstrate that the infected individuals predicted by the model have good consistency with the actual infected ones based on simulations.Na ZhaoJian WangYong YuJun-Yan ZhaoDuan-Bing ChenNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-7 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Na Zhao Jian Wang Yong Yu Jun-Yan Zhao Duan-Bing Chen Spreading predictability in complex networks
description	Abstract Many state-of-the-art researches focus on predicting infection scale or threshold in infectious diseases or rumor and give the vaccination strategies correspondingly. In these works, most of them assume that the infection probability and initially infected individuals are known at the very beginning. Generally, infectious diseases or rumor has been spreading for some time when it is noticed. How to predict which individuals will be infected in the future only by knowing the current snapshot becomes a key issue in infectious diseases or rumor control. In this report, a prediction model based on snapshot is presented to predict the potentially infected individuals in the future, not just the macro scale of infection. Experimental results on synthetic and real networks demonstrate that the infected individuals predicted by the model have good consistency with the actual infected ones based on simulations.
format	article
author	Na Zhao Jian Wang Yong Yu Jun-Yan Zhao Duan-Bing Chen
author_facet	Na Zhao Jian Wang Yong Yu Jun-Yan Zhao Duan-Bing Chen
author_sort	Na Zhao
title	Spreading predictability in complex networks
title_short	Spreading predictability in complex networks
title_full	Spreading predictability in complex networks
title_fullStr	Spreading predictability in complex networks
title_full_unstemmed	Spreading predictability in complex networks
title_sort	spreading predictability in complex networks
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/e4ff672e9b7b4fb8b4fb6ec569b66632
work_keys_str_mv	AT nazhao spreadingpredictabilityincomplexnetworks AT jianwang spreadingpredictabilityincomplexnetworks AT yongyu spreadingpredictabilityincomplexnetworks AT junyanzhao spreadingpredictabilityincomplexnetworks AT duanbingchen spreadingpredictabilityincomplexnetworks
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Spreading predictability in complex networks

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