Percolation centrality: quantifying graph-theoretic impact of nodes during percolation in networks.

A number of centrality measures are available to determine the relative importance of a node in a complex network, and betweenness is prominent among them. However, the existing centrality measures are not adequate in network percolation scenarios (such as during infection transmission in a social n...

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Autores principales:	Mahendra Piraveenan, Mikhail Prokopenko, Liaquat Hossain
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/33e01b9f4d5a4b28a605df401ff68378
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spelling	oai:doaj.org-article:33e01b9f4d5a4b28a605df401ff683782021-11-18T08:00:41ZPercolation centrality: quantifying graph-theoretic impact of nodes during percolation in networks.1932-620310.1371/journal.pone.0053095https://doaj.org/article/33e01b9f4d5a4b28a605df401ff683782013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23349699/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203A number of centrality measures are available to determine the relative importance of a node in a complex network, and betweenness is prominent among them. However, the existing centrality measures are not adequate in network percolation scenarios (such as during infection transmission in a social network of individuals, spreading of computer viruses on computer networks, or transmission of disease over a network of towns) because they do not account for the changing percolation states of individual nodes. We propose a new measure, percolation centrality, that quantifies relative impact of nodes based on their topological connectivity, as well as their percolation states. The measure can be extended to include random walk based definitions, and its computational complexity is shown to be of the same order as that of betweenness centrality. We demonstrate the usage of percolation centrality by applying it to a canonical network as well as simulated and real world scale-free and random networks.Mahendra PiraveenanMikhail ProkopenkoLiaquat HossainPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 1, p e53095 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mahendra Piraveenan Mikhail Prokopenko Liaquat Hossain Percolation centrality: quantifying graph-theoretic impact of nodes during percolation in networks.
description	A number of centrality measures are available to determine the relative importance of a node in a complex network, and betweenness is prominent among them. However, the existing centrality measures are not adequate in network percolation scenarios (such as during infection transmission in a social network of individuals, spreading of computer viruses on computer networks, or transmission of disease over a network of towns) because they do not account for the changing percolation states of individual nodes. We propose a new measure, percolation centrality, that quantifies relative impact of nodes based on their topological connectivity, as well as their percolation states. The measure can be extended to include random walk based definitions, and its computational complexity is shown to be of the same order as that of betweenness centrality. We demonstrate the usage of percolation centrality by applying it to a canonical network as well as simulated and real world scale-free and random networks.
format	article
author	Mahendra Piraveenan Mikhail Prokopenko Liaquat Hossain
author_facet	Mahendra Piraveenan Mikhail Prokopenko Liaquat Hossain
author_sort	Mahendra Piraveenan
title	Percolation centrality: quantifying graph-theoretic impact of nodes during percolation in networks.
title_short	Percolation centrality: quantifying graph-theoretic impact of nodes during percolation in networks.
title_full	Percolation centrality: quantifying graph-theoretic impact of nodes during percolation in networks.
title_fullStr	Percolation centrality: quantifying graph-theoretic impact of nodes during percolation in networks.
title_full_unstemmed	Percolation centrality: quantifying graph-theoretic impact of nodes during percolation in networks.
title_sort	percolation centrality: quantifying graph-theoretic impact of nodes during percolation in networks.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/33e01b9f4d5a4b28a605df401ff68378
work_keys_str_mv	AT mahendrapiraveenan percolationcentralityquantifyinggraphtheoreticimpactofnodesduringpercolationinnetworks AT mikhailprokopenko percolationcentralityquantifyinggraphtheoreticimpactofnodesduringpercolationinnetworks AT liaquathossain percolationcentralityquantifyinggraphtheoreticimpactofnodesduringpercolationinnetworks
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Percolation centrality: quantifying graph-theoretic impact of nodes during percolation in networks.

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