Betweenness centrality for temporal multiplexes

Abstract Betweenness centrality quantifies the importance of a vertex for the information flow in a network. The standard betweenness centrality applies to static single-layer networks, but many real world networks are both dynamic and made of several layers. We propose a definition of betweenness c...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Silvia Zaoli, Piero Mazzarisi, Fabrizio Lillo
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/810a351d61c640f09bc972b76771c23f
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:810a351d61c640f09bc972b76771c23f
record_format	dspace
spelling	oai:doaj.org-article:810a351d61c640f09bc972b76771c23f2021-12-02T13:20:02ZBetweenness centrality for temporal multiplexes10.1038/s41598-021-84418-z2045-2322https://doaj.org/article/810a351d61c640f09bc972b76771c23f2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-84418-zhttps://doaj.org/toc/2045-2322Abstract Betweenness centrality quantifies the importance of a vertex for the information flow in a network. The standard betweenness centrality applies to static single-layer networks, but many real world networks are both dynamic and made of several layers. We propose a definition of betweenness centrality for temporal multiplexes. This definition accounts for the topological and temporal structure and for the duration of paths in the determination of the shortest paths. We propose an algorithm to compute the new metric using a mapping to a static graph. We apply the metric to a dataset of $$\sim 20$$ ∼ 20 k European flights and compare the results with those obtained with static or single-layer metrics. The differences in the airports rankings highlight the importance of considering the temporal multiplex structure and an appropriate distance metric.Silvia ZaoliPiero MazzarisiFabrizio LilloNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Silvia Zaoli Piero Mazzarisi Fabrizio Lillo Betweenness centrality for temporal multiplexes
description	Abstract Betweenness centrality quantifies the importance of a vertex for the information flow in a network. The standard betweenness centrality applies to static single-layer networks, but many real world networks are both dynamic and made of several layers. We propose a definition of betweenness centrality for temporal multiplexes. This definition accounts for the topological and temporal structure and for the duration of paths in the determination of the shortest paths. We propose an algorithm to compute the new metric using a mapping to a static graph. We apply the metric to a dataset of $$\sim 20$$ ∼ 20 k European flights and compare the results with those obtained with static or single-layer metrics. The differences in the airports rankings highlight the importance of considering the temporal multiplex structure and an appropriate distance metric.
format	article
author	Silvia Zaoli Piero Mazzarisi Fabrizio Lillo
author_facet	Silvia Zaoli Piero Mazzarisi Fabrizio Lillo
author_sort	Silvia Zaoli
title	Betweenness centrality for temporal multiplexes
title_short	Betweenness centrality for temporal multiplexes
title_full	Betweenness centrality for temporal multiplexes
title_fullStr	Betweenness centrality for temporal multiplexes
title_full_unstemmed	Betweenness centrality for temporal multiplexes
title_sort	betweenness centrality for temporal multiplexes
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/810a351d61c640f09bc972b76771c23f
work_keys_str_mv	AT silviazaoli betweennesscentralityfortemporalmultiplexes AT pieromazzarisi betweennesscentralityfortemporalmultiplexes AT fabriziolillo betweennesscentralityfortemporalmultiplexes
_version_	1718393220519428096

Betweenness centrality for temporal multiplexes

Ejemplares similares