A bio-inspired methodology of identifying influential nodes in complex networks.

How to identify influential nodes is a key issue in complex networks. The degree centrality is simple, but is incapable to reflect the global characteristics of networks. Betweenness centrality and closeness centrality do not consider the location of nodes in the networks, and semi-local centrality,...

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Autores principales:	Cai Gao, Xin Lan, Xiaoge Zhang, Yong Deng
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a44f16e164064c9bbd2f225b08d99b27
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id	oai:doaj.org-article:a44f16e164064c9bbd2f225b08d99b27
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spelling	oai:doaj.org-article:a44f16e164064c9bbd2f225b08d99b272021-11-18T07:41:37ZA bio-inspired methodology of identifying influential nodes in complex networks.1932-620310.1371/journal.pone.0066732https://doaj.org/article/a44f16e164064c9bbd2f225b08d99b272013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23799129/?tool=EBIhttps://doaj.org/toc/1932-6203How to identify influential nodes is a key issue in complex networks. The degree centrality is simple, but is incapable to reflect the global characteristics of networks. Betweenness centrality and closeness centrality do not consider the location of nodes in the networks, and semi-local centrality, leaderRank and pageRank approaches can be only applied in unweighted networks. In this paper, a bio-inspired centrality measure model is proposed, which combines the Physarum centrality with the K-shell index obtained by K-shell decomposition analysis, to identify influential nodes in weighted networks. Then, we use the Susceptible-Infected (SI) model to evaluate the performance. Examples and applications are given to demonstrate the adaptivity and efficiency of the proposed method. In addition, the results are compared with existing methods.Cai GaoXin LanXiaoge ZhangYong DengPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 6, p e66732 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Cai Gao Xin Lan Xiaoge Zhang Yong Deng A bio-inspired methodology of identifying influential nodes in complex networks.
description	How to identify influential nodes is a key issue in complex networks. The degree centrality is simple, but is incapable to reflect the global characteristics of networks. Betweenness centrality and closeness centrality do not consider the location of nodes in the networks, and semi-local centrality, leaderRank and pageRank approaches can be only applied in unweighted networks. In this paper, a bio-inspired centrality measure model is proposed, which combines the Physarum centrality with the K-shell index obtained by K-shell decomposition analysis, to identify influential nodes in weighted networks. Then, we use the Susceptible-Infected (SI) model to evaluate the performance. Examples and applications are given to demonstrate the adaptivity and efficiency of the proposed method. In addition, the results are compared with existing methods.
format	article
author	Cai Gao Xin Lan Xiaoge Zhang Yong Deng
author_facet	Cai Gao Xin Lan Xiaoge Zhang Yong Deng
author_sort	Cai Gao
title	A bio-inspired methodology of identifying influential nodes in complex networks.
title_short	A bio-inspired methodology of identifying influential nodes in complex networks.
title_full	A bio-inspired methodology of identifying influential nodes in complex networks.
title_fullStr	A bio-inspired methodology of identifying influential nodes in complex networks.
title_full_unstemmed	A bio-inspired methodology of identifying influential nodes in complex networks.
title_sort	bio-inspired methodology of identifying influential nodes in complex networks.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/a44f16e164064c9bbd2f225b08d99b27
work_keys_str_mv	AT caigao abioinspiredmethodologyofidentifyinginfluentialnodesincomplexnetworks AT xinlan abioinspiredmethodologyofidentifyinginfluentialnodesincomplexnetworks AT xiaogezhang abioinspiredmethodologyofidentifyinginfluentialnodesincomplexnetworks AT yongdeng abioinspiredmethodologyofidentifyinginfluentialnodesincomplexnetworks AT caigao bioinspiredmethodologyofidentifyinginfluentialnodesincomplexnetworks AT xinlan bioinspiredmethodologyofidentifyinginfluentialnodesincomplexnetworks AT xiaogezhang bioinspiredmethodologyofidentifyinginfluentialnodesincomplexnetworks AT yongdeng bioinspiredmethodologyofidentifyinginfluentialnodesincomplexnetworks
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A bio-inspired methodology of identifying influential nodes in complex networks.

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