Best influential spreaders identification using network global structural properties

Abstract Influential spreaders are the crucial nodes in a complex network that can act as a controller or a maximizer of a spreading process. For example, we can control the virus propagation in an epidemiological network by controlling the behavior of such influential nodes, and amplify the informa...

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Autores principales:	Amrita Namtirtha, Animesh Dutta, Biswanath Dutta, Amritha Sundararajan, Yogesh Simmhan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d5a11baf127b44659ccccbcf0688440e
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spelling	oai:doaj.org-article:d5a11baf127b44659ccccbcf0688440e2021-12-02T14:16:16ZBest influential spreaders identification using network global structural properties10.1038/s41598-021-81614-92045-2322https://doaj.org/article/d5a11baf127b44659ccccbcf0688440e2021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-81614-9https://doaj.org/toc/2045-2322Abstract Influential spreaders are the crucial nodes in a complex network that can act as a controller or a maximizer of a spreading process. For example, we can control the virus propagation in an epidemiological network by controlling the behavior of such influential nodes, and amplify the information propagation in a social network by using them as a maximizer. Many indexing methods have been proposed in the literature to identify the influential spreaders in a network. Nevertheless, we have notice that each individual network holds different connectivity structures that we classify as complete, incomplete, or in-between based on their components and density. These affect the accuracy of existing indexing methods in the identification of the best influential spreaders. Thus, no single indexing strategy is sufficient from all varieties of network connectivity structures. This article proposes a new indexing method Network Global Structure-based Centrality (ngsc) which intelligently combines existing kshell and sum of neighbors’ degree methods with knowledge of the network’s global structural properties, such as the giant component, average degree, and percolation threshold. The experimental results show that our proposed method yields a better spreading performance of the seed spreaders over a large variety of network connectivity structures, and correlates well with ranking based on an SIR model used as ground truth. It also out-performs contemporary techniques and is competitive with more sophisticated approaches that are computationally cost.Amrita NamtirthaAnimesh DuttaBiswanath DuttaAmritha SundararajanYogesh SimmhanNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Amrita Namtirtha Animesh Dutta Biswanath Dutta Amritha Sundararajan Yogesh Simmhan Best influential spreaders identification using network global structural properties
description	Abstract Influential spreaders are the crucial nodes in a complex network that can act as a controller or a maximizer of a spreading process. For example, we can control the virus propagation in an epidemiological network by controlling the behavior of such influential nodes, and amplify the information propagation in a social network by using them as a maximizer. Many indexing methods have been proposed in the literature to identify the influential spreaders in a network. Nevertheless, we have notice that each individual network holds different connectivity structures that we classify as complete, incomplete, or in-between based on their components and density. These affect the accuracy of existing indexing methods in the identification of the best influential spreaders. Thus, no single indexing strategy is sufficient from all varieties of network connectivity structures. This article proposes a new indexing method Network Global Structure-based Centrality (ngsc) which intelligently combines existing kshell and sum of neighbors’ degree methods with knowledge of the network’s global structural properties, such as the giant component, average degree, and percolation threshold. The experimental results show that our proposed method yields a better spreading performance of the seed spreaders over a large variety of network connectivity structures, and correlates well with ranking based on an SIR model used as ground truth. It also out-performs contemporary techniques and is competitive with more sophisticated approaches that are computationally cost.
format	article
author	Amrita Namtirtha Animesh Dutta Biswanath Dutta Amritha Sundararajan Yogesh Simmhan
author_facet	Amrita Namtirtha Animesh Dutta Biswanath Dutta Amritha Sundararajan Yogesh Simmhan
author_sort	Amrita Namtirtha
title	Best influential spreaders identification using network global structural properties
title_short	Best influential spreaders identification using network global structural properties
title_full	Best influential spreaders identification using network global structural properties
title_fullStr	Best influential spreaders identification using network global structural properties
title_full_unstemmed	Best influential spreaders identification using network global structural properties
title_sort	best influential spreaders identification using network global structural properties
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/d5a11baf127b44659ccccbcf0688440e
work_keys_str_mv	AT amritanamtirtha bestinfluentialspreadersidentificationusingnetworkglobalstructuralproperties AT animeshdutta bestinfluentialspreadersidentificationusingnetworkglobalstructuralproperties AT biswanathdutta bestinfluentialspreadersidentificationusingnetworkglobalstructuralproperties AT amrithasundararajan bestinfluentialspreadersidentificationusingnetworkglobalstructuralproperties AT yogeshsimmhan bestinfluentialspreadersidentificationusingnetworkglobalstructuralproperties
_version_	1718391695497756672

Best influential spreaders identification using network global structural properties

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