Embedding the intrinsic relevance of vertices in network analysis: the case of centrality metrics

Abstract Complex network theory (CNT) is gaining a lot of attention in the scientific community, due to its capability to model and interpret an impressive number of natural and anthropic phenomena. One of the most active CNT field concerns the evaluation of the centrality of vertices and edges in t...

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Autores principales:	Orazio Giustolisi, Luca Ridolfi, Antonietta Simone
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/fe8b34d8b70b4c51be0383d735791ba6
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spelling	oai:doaj.org-article:fe8b34d8b70b4c51be0383d735791ba62021-12-02T16:23:09ZEmbedding the intrinsic relevance of vertices in network analysis: the case of centrality metrics10.1038/s41598-020-60151-x2045-2322https://doaj.org/article/fe8b34d8b70b4c51be0383d735791ba62020-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-60151-xhttps://doaj.org/toc/2045-2322Abstract Complex network theory (CNT) is gaining a lot of attention in the scientific community, due to its capability to model and interpret an impressive number of natural and anthropic phenomena. One of the most active CNT field concerns the evaluation of the centrality of vertices and edges in the network. Several metrics have been proposed, but all of them share a topological point of view, namely centrality descends from the local or global connectivity structure of the network. However, vertices can exhibit their own intrinsic relevance independent from topology; e.g., vertices representing strategic locations (e.g., hospitals, water and energy sources, etc.) or institutional roles (e.g., presidents, agencies, etc.). In these cases, the connectivity network structure and vertex intrinsic relevance mutually concur to define the centrality of vertices and edges. The purpose of this work is to embed the information about the intrinsic relevance of vertices into CNT tools to enhance the network analysis. We focus on the degree, closeness and betweenness metrics, being among the most used. Two examples, concerning a social (the historical Florence family’s marriage network) and an infrastructure (a water supply system) network, demonstrate the effectiveness of the proposed relevance-embedding extension of the centrality metrics.Orazio GiustolisiLuca RidolfiAntonietta SimoneNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-11 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Orazio Giustolisi Luca Ridolfi Antonietta Simone Embedding the intrinsic relevance of vertices in network analysis: the case of centrality metrics
description	Abstract Complex network theory (CNT) is gaining a lot of attention in the scientific community, due to its capability to model and interpret an impressive number of natural and anthropic phenomena. One of the most active CNT field concerns the evaluation of the centrality of vertices and edges in the network. Several metrics have been proposed, but all of them share a topological point of view, namely centrality descends from the local or global connectivity structure of the network. However, vertices can exhibit their own intrinsic relevance independent from topology; e.g., vertices representing strategic locations (e.g., hospitals, water and energy sources, etc.) or institutional roles (e.g., presidents, agencies, etc.). In these cases, the connectivity network structure and vertex intrinsic relevance mutually concur to define the centrality of vertices and edges. The purpose of this work is to embed the information about the intrinsic relevance of vertices into CNT tools to enhance the network analysis. We focus on the degree, closeness and betweenness metrics, being among the most used. Two examples, concerning a social (the historical Florence family’s marriage network) and an infrastructure (a water supply system) network, demonstrate the effectiveness of the proposed relevance-embedding extension of the centrality metrics.
format	article
author	Orazio Giustolisi Luca Ridolfi Antonietta Simone
author_facet	Orazio Giustolisi Luca Ridolfi Antonietta Simone
author_sort	Orazio Giustolisi
title	Embedding the intrinsic relevance of vertices in network analysis: the case of centrality metrics
title_short	Embedding the intrinsic relevance of vertices in network analysis: the case of centrality metrics
title_full	Embedding the intrinsic relevance of vertices in network analysis: the case of centrality metrics
title_fullStr	Embedding the intrinsic relevance of vertices in network analysis: the case of centrality metrics
title_full_unstemmed	Embedding the intrinsic relevance of vertices in network analysis: the case of centrality metrics
title_sort	embedding the intrinsic relevance of vertices in network analysis: the case of centrality metrics
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/fe8b34d8b70b4c51be0383d735791ba6
work_keys_str_mv	AT oraziogiustolisi embeddingtheintrinsicrelevanceofverticesinnetworkanalysisthecaseofcentralitymetrics AT lucaridolfi embeddingtheintrinsicrelevanceofverticesinnetworkanalysisthecaseofcentralitymetrics AT antoniettasimone embeddingtheintrinsicrelevanceofverticesinnetworkanalysisthecaseofcentralitymetrics
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Embedding the intrinsic relevance of vertices in network analysis: the case of centrality metrics

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