Characterizing dissimilarity of weighted networks

Abstract Measuring the dissimilarities between networks is a basic problem and wildly used in many fields. Based on method of the D-measure which is suggested for unweighted networks, we propose a quantitative dissimilarity metric of weighted network (WD-metric). Crucially, we construct a distance p...

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Autores principales:	Yuanxiang Jiang, Meng Li, Ying Fan, Zengru Di
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8d3e923e8ebc4bb49167b230577523b5
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spelling	oai:doaj.org-article:8d3e923e8ebc4bb49167b230577523b52021-12-02T15:53:46ZCharacterizing dissimilarity of weighted networks10.1038/s41598-021-85175-92045-2322https://doaj.org/article/8d3e923e8ebc4bb49167b230577523b52021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-85175-9https://doaj.org/toc/2045-2322Abstract Measuring the dissimilarities between networks is a basic problem and wildly used in many fields. Based on method of the D-measure which is suggested for unweighted networks, we propose a quantitative dissimilarity metric of weighted network (WD-metric). Crucially, we construct a distance probability matrix of weighted network, which can capture the comprehensive information of weighted network. Moreover, we define the complementary graph and alpha centrality of weighted network. Correspondingly, several synthetic and real-world networks are used to verify the effectiveness of the WD-metric. Experimental results show that WD-metric can effectively capture the influence of weight on the network structure and quantitatively measure the dissimilarity of weighted networks. It can also be used as a criterion for backbone extraction algorithms of complex network.Yuanxiang JiangMeng LiYing FanZengru DiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yuanxiang Jiang Meng Li Ying Fan Zengru Di Characterizing dissimilarity of weighted networks
description	Abstract Measuring the dissimilarities between networks is a basic problem and wildly used in many fields. Based on method of the D-measure which is suggested for unweighted networks, we propose a quantitative dissimilarity metric of weighted network (WD-metric). Crucially, we construct a distance probability matrix of weighted network, which can capture the comprehensive information of weighted network. Moreover, we define the complementary graph and alpha centrality of weighted network. Correspondingly, several synthetic and real-world networks are used to verify the effectiveness of the WD-metric. Experimental results show that WD-metric can effectively capture the influence of weight on the network structure and quantitatively measure the dissimilarity of weighted networks. It can also be used as a criterion for backbone extraction algorithms of complex network.
format	article
author	Yuanxiang Jiang Meng Li Ying Fan Zengru Di
author_facet	Yuanxiang Jiang Meng Li Ying Fan Zengru Di
author_sort	Yuanxiang Jiang
title	Characterizing dissimilarity of weighted networks
title_short	Characterizing dissimilarity of weighted networks
title_full	Characterizing dissimilarity of weighted networks
title_fullStr	Characterizing dissimilarity of weighted networks
title_full_unstemmed	Characterizing dissimilarity of weighted networks
title_sort	characterizing dissimilarity of weighted networks
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/8d3e923e8ebc4bb49167b230577523b5
work_keys_str_mv	AT yuanxiangjiang characterizingdissimilarityofweightednetworks AT mengli characterizingdissimilarityofweightednetworks AT yingfan characterizingdissimilarityofweightednetworks AT zengrudi characterizingdissimilarityofweightednetworks
_version_	1718385520854171648

Characterizing dissimilarity of weighted networks

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