Top influencers can be identified universally by combining classical centralities

Abstract Information flow, opinion, and epidemics spread over structured networks. When using node centrality indicators to predict which nodes will be among the top influencers or superspreaders, no single centrality is a consistently good ranker across networks. We show that statistical classifier...

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Autor principal:	Doina Bucur
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/19708cbb8fc8425c8894a545b74efcf3
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spelling	oai:doaj.org-article:19708cbb8fc8425c8894a545b74efcf32021-12-02T15:10:05ZTop influencers can be identified universally by combining classical centralities10.1038/s41598-020-77536-72045-2322https://doaj.org/article/19708cbb8fc8425c8894a545b74efcf32020-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-77536-7https://doaj.org/toc/2045-2322Abstract Information flow, opinion, and epidemics spread over structured networks. When using node centrality indicators to predict which nodes will be among the top influencers or superspreaders, no single centrality is a consistently good ranker across networks. We show that statistical classifiers using two or more centralities are instead consistently predictive over many diverse, static real-world topologies. Certain pairs of centralities cooperate particularly well in drawing the statistical boundary between the superspreaders and the rest: a local centrality measuring the size of a node’s neighbourhood gains from the addition of a global centrality such as the eigenvector centrality, closeness, or the core number. Intuitively, this is because a local centrality may rank highly nodes which are located in locally dense, but globally peripheral regions of the network. The additional global centrality indicator guides the prediction towards more central regions. The superspreaders usually jointly maximise the values of both centralities. As a result of the interplay between centrality indicators, training classifiers with seven classical indicators leads to a nearly maximum average precision function (0.995) across the networks in this study.Doina BucurNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-14 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Doina Bucur Top influencers can be identified universally by combining classical centralities
description	Abstract Information flow, opinion, and epidemics spread over structured networks. When using node centrality indicators to predict which nodes will be among the top influencers or superspreaders, no single centrality is a consistently good ranker across networks. We show that statistical classifiers using two or more centralities are instead consistently predictive over many diverse, static real-world topologies. Certain pairs of centralities cooperate particularly well in drawing the statistical boundary between the superspreaders and the rest: a local centrality measuring the size of a node’s neighbourhood gains from the addition of a global centrality such as the eigenvector centrality, closeness, or the core number. Intuitively, this is because a local centrality may rank highly nodes which are located in locally dense, but globally peripheral regions of the network. The additional global centrality indicator guides the prediction towards more central regions. The superspreaders usually jointly maximise the values of both centralities. As a result of the interplay between centrality indicators, training classifiers with seven classical indicators leads to a nearly maximum average precision function (0.995) across the networks in this study.
format	article
author	Doina Bucur
author_facet	Doina Bucur
author_sort	Doina Bucur
title	Top influencers can be identified universally by combining classical centralities
title_short	Top influencers can be identified universally by combining classical centralities
title_full	Top influencers can be identified universally by combining classical centralities
title_fullStr	Top influencers can be identified universally by combining classical centralities
title_full_unstemmed	Top influencers can be identified universally by combining classical centralities
title_sort	top influencers can be identified universally by combining classical centralities
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/19708cbb8fc8425c8894a545b74efcf3
work_keys_str_mv	AT doinabucur topinfluencerscanbeidentifieduniversallybycombiningclassicalcentralities
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Top influencers can be identified universally by combining classical centralities

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