Identification of nodes influence based on global structure model in complex networks

Abstract Identification of Influential nodes in complex networks is challenging due to the largely scaled data and network sizes, and frequently changing behaviors of the current topologies. Various application scenarios like disease transmission and immunization, software virus infection and disinf...

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Autores principales:	Aman Ullah, Bin Wang, JinFang Sheng, Jun Long, Nasrullah Khan, ZeJun Sun
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/444fd16775aa48629b8cea50ff4561e9
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spelling	oai:doaj.org-article:444fd16775aa48629b8cea50ff4561e92021-12-02T13:18:08ZIdentification of nodes influence based on global structure model in complex networks10.1038/s41598-021-84684-x2045-2322https://doaj.org/article/444fd16775aa48629b8cea50ff4561e92021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-84684-xhttps://doaj.org/toc/2045-2322Abstract Identification of Influential nodes in complex networks is challenging due to the largely scaled data and network sizes, and frequently changing behaviors of the current topologies. Various application scenarios like disease transmission and immunization, software virus infection and disinfection, increased product exposure and rumor suppression, etc., are applicable domains in the corresponding networks where identification of influential nodes is crucial. Though a lot of approaches are proposed to address the challenges, most of the relevant research concentrates only on single and limited aspects of the problem. Therefore, we propose Global Structure Model (GSM) for influential nodes identification that considers self-influence as well as emphasizes on global influence of the node in the network. We applied GSM and utilized Susceptible Infected Recovered model to evaluate its efficiency. Moreover, various standard algorithms such as Betweenness Centrality, Profit Leader, H-Index, Closeness Centrality, Hyperlink Induced Topic Search, Improved K-shell Hybrid, Density Centrality, Extended Cluster Coefficient Ranking Measure, and Gravity Index Centrality are employed as baseline benchmarks to evaluate the performance of GSM. Similarly, we used seven real-world and two synthetic multi-typed complex networks along-with different well-known datasets for experiments. Results analysis indicates that GSM outperformed the baseline algorithms in identification of influential node(s).Aman UllahBin WangJinFang ShengJun LongNasrullah KhanZeJun SunNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Aman Ullah Bin Wang JinFang Sheng Jun Long Nasrullah Khan ZeJun Sun Identification of nodes influence based on global structure model in complex networks
description	Abstract Identification of Influential nodes in complex networks is challenging due to the largely scaled data and network sizes, and frequently changing behaviors of the current topologies. Various application scenarios like disease transmission and immunization, software virus infection and disinfection, increased product exposure and rumor suppression, etc., are applicable domains in the corresponding networks where identification of influential nodes is crucial. Though a lot of approaches are proposed to address the challenges, most of the relevant research concentrates only on single and limited aspects of the problem. Therefore, we propose Global Structure Model (GSM) for influential nodes identification that considers self-influence as well as emphasizes on global influence of the node in the network. We applied GSM and utilized Susceptible Infected Recovered model to evaluate its efficiency. Moreover, various standard algorithms such as Betweenness Centrality, Profit Leader, H-Index, Closeness Centrality, Hyperlink Induced Topic Search, Improved K-shell Hybrid, Density Centrality, Extended Cluster Coefficient Ranking Measure, and Gravity Index Centrality are employed as baseline benchmarks to evaluate the performance of GSM. Similarly, we used seven real-world and two synthetic multi-typed complex networks along-with different well-known datasets for experiments. Results analysis indicates that GSM outperformed the baseline algorithms in identification of influential node(s).
format	article
author	Aman Ullah Bin Wang JinFang Sheng Jun Long Nasrullah Khan ZeJun Sun
author_facet	Aman Ullah Bin Wang JinFang Sheng Jun Long Nasrullah Khan ZeJun Sun
author_sort	Aman Ullah
title	Identification of nodes influence based on global structure model in complex networks
title_short	Identification of nodes influence based on global structure model in complex networks
title_full	Identification of nodes influence based on global structure model in complex networks
title_fullStr	Identification of nodes influence based on global structure model in complex networks
title_full_unstemmed	Identification of nodes influence based on global structure model in complex networks
title_sort	identification of nodes influence based on global structure model in complex networks
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/444fd16775aa48629b8cea50ff4561e9
work_keys_str_mv	AT amanullah identificationofnodesinfluencebasedonglobalstructuremodelincomplexnetworks AT binwang identificationofnodesinfluencebasedonglobalstructuremodelincomplexnetworks AT jinfangsheng identificationofnodesinfluencebasedonglobalstructuremodelincomplexnetworks AT junlong identificationofnodesinfluencebasedonglobalstructuremodelincomplexnetworks AT nasrullahkhan identificationofnodesinfluencebasedonglobalstructuremodelincomplexnetworks AT zejunsun identificationofnodesinfluencebasedonglobalstructuremodelincomplexnetworks
_version_	1718393268214956032

Identification of nodes influence based on global structure model in complex networks

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