Temporal Degree-Degree and Closeness-Closeness: A New Centrality Metrics for Social Network Analysis

In the area of network analysis, centrality metrics play an important role in defining the “most important” actors in a social network. However, nowadays, most types of networks are dynamic, meaning their topology changes over time. The connection weights and the strengths of social links between no...

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Autores principales:	Mahmoud Elmezain, Ebtesam A. Othman, Hani M. Ibrahim
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	social network analysis time-ordered weighted graph centrality measures temporal degree centrality temporal closeness centrality temporal degree-degree Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/b1a1d550d24c4191bb2c2c3569366d71
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id	oai:doaj.org-article:b1a1d550d24c4191bb2c2c3569366d71
record_format	dspace
spelling	oai:doaj.org-article:b1a1d550d24c4191bb2c2c3569366d712021-11-25T18:16:33ZTemporal Degree-Degree and Closeness-Closeness: A New Centrality Metrics for Social Network Analysis10.3390/math92228502227-7390https://doaj.org/article/b1a1d550d24c4191bb2c2c3569366d712021-11-01T00:00:00Zhttps://www.mdpi.com/2227-7390/9/22/2850https://doaj.org/toc/2227-7390In the area of network analysis, centrality metrics play an important role in defining the “most important” actors in a social network. However, nowadays, most types of networks are dynamic, meaning their topology changes over time. The connection weights and the strengths of social links between nodes are an important concept in a social network. The new centrality measures are proposed for weighted networks, which relies on a time-ordered weighted graph model, generalized temporal degree and closeness centrality. Furthermore, two measures—Temporal Degree-Degree and Temporal Closeness-Closeness—are employed to better understand the significance of nodes in weighted dynamic networks. Our study is caried out according to real dynamic weighted networks dataset of a university-based karate club. Through extensive experiments and discussions of the proposed metrics, our analysis proves that there is an effectiveness on the impact of each node throughout social networks.Mahmoud ElmezainEbtesam A. OthmanHani M. IbrahimMDPI AGarticlesocial network analysistime-ordered weighted graphcentrality measurestemporal degree centralitytemporal closeness centralitytemporal degree-degreeMathematicsQA1-939ENMathematics, Vol 9, Iss 2850, p 2850 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	social network analysis time-ordered weighted graph centrality measures temporal degree centrality temporal closeness centrality temporal degree-degree Mathematics QA1-939
spellingShingle	social network analysis time-ordered weighted graph centrality measures temporal degree centrality temporal closeness centrality temporal degree-degree Mathematics QA1-939 Mahmoud Elmezain Ebtesam A. Othman Hani M. Ibrahim Temporal Degree-Degree and Closeness-Closeness: A New Centrality Metrics for Social Network Analysis
description	In the area of network analysis, centrality metrics play an important role in defining the “most important” actors in a social network. However, nowadays, most types of networks are dynamic, meaning their topology changes over time. The connection weights and the strengths of social links between nodes are an important concept in a social network. The new centrality measures are proposed for weighted networks, which relies on a time-ordered weighted graph model, generalized temporal degree and closeness centrality. Furthermore, two measures—Temporal Degree-Degree and Temporal Closeness-Closeness—are employed to better understand the significance of nodes in weighted dynamic networks. Our study is caried out according to real dynamic weighted networks dataset of a university-based karate club. Through extensive experiments and discussions of the proposed metrics, our analysis proves that there is an effectiveness on the impact of each node throughout social networks.
format	article
author	Mahmoud Elmezain Ebtesam A. Othman Hani M. Ibrahim
author_facet	Mahmoud Elmezain Ebtesam A. Othman Hani M. Ibrahim
author_sort	Mahmoud Elmezain
title	Temporal Degree-Degree and Closeness-Closeness: A New Centrality Metrics for Social Network Analysis
title_short	Temporal Degree-Degree and Closeness-Closeness: A New Centrality Metrics for Social Network Analysis
title_full	Temporal Degree-Degree and Closeness-Closeness: A New Centrality Metrics for Social Network Analysis
title_fullStr	Temporal Degree-Degree and Closeness-Closeness: A New Centrality Metrics for Social Network Analysis
title_full_unstemmed	Temporal Degree-Degree and Closeness-Closeness: A New Centrality Metrics for Social Network Analysis
title_sort	temporal degree-degree and closeness-closeness: a new centrality metrics for social network analysis
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/b1a1d550d24c4191bb2c2c3569366d71
work_keys_str_mv	AT mahmoudelmezain temporaldegreedegreeandclosenessclosenessanewcentralitymetricsforsocialnetworkanalysis AT ebtesamaothman temporaldegreedegreeandclosenessclosenessanewcentralitymetricsforsocialnetworkanalysis AT hanimibrahim temporaldegreedegreeandclosenessclosenessanewcentralitymetricsforsocialnetworkanalysis
_version_	1718411402534715392

Temporal Degree-Degree and Closeness-Closeness: A New Centrality Metrics for Social Network Analysis

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