Generalization of clustering coefficients to signed correlation networks.

The recent interest in network analysis applications in personality psychology and psychopathology has put forward new methodological challenges. Personality and psychopathology networks are typically based on correlation matrices and therefore include both positive and negative edge signs. However,...

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Autores principales:	Giulio Costantini, Marco Perugini
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5da1566a8a4a44789a1593fc7d4f0071
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spelling	oai:doaj.org-article:5da1566a8a4a44789a1593fc7d4f00712021-11-18T08:31:37ZGeneralization of clustering coefficients to signed correlation networks.1932-620310.1371/journal.pone.0088669https://doaj.org/article/5da1566a8a4a44789a1593fc7d4f00712014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24586367/?tool=EBIhttps://doaj.org/toc/1932-6203The recent interest in network analysis applications in personality psychology and psychopathology has put forward new methodological challenges. Personality and psychopathology networks are typically based on correlation matrices and therefore include both positive and negative edge signs. However, some applications of network analysis disregard negative edges, such as computing clustering coefficients. In this contribution, we illustrate the importance of the distinction between positive and negative edges in networks based on correlation matrices. The clustering coefficient is generalized to signed correlation networks: three new indices are introduced that take edge signs into account, each derived from an existing and widely used formula. The performances of the new indices are illustrated and compared with the performances of the unsigned indices, both on a signed simulated network and on a signed network based on actual personality psychology data. The results show that the new indices are more resistant to sample variations in correlation networks and therefore have higher convergence compared with the unsigned indices both in simulated networks and with real data.Giulio CostantiniMarco PeruginiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 2, p e88669 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Giulio Costantini Marco Perugini Generalization of clustering coefficients to signed correlation networks.
description	The recent interest in network analysis applications in personality psychology and psychopathology has put forward new methodological challenges. Personality and psychopathology networks are typically based on correlation matrices and therefore include both positive and negative edge signs. However, some applications of network analysis disregard negative edges, such as computing clustering coefficients. In this contribution, we illustrate the importance of the distinction between positive and negative edges in networks based on correlation matrices. The clustering coefficient is generalized to signed correlation networks: three new indices are introduced that take edge signs into account, each derived from an existing and widely used formula. The performances of the new indices are illustrated and compared with the performances of the unsigned indices, both on a signed simulated network and on a signed network based on actual personality psychology data. The results show that the new indices are more resistant to sample variations in correlation networks and therefore have higher convergence compared with the unsigned indices both in simulated networks and with real data.
format	article
author	Giulio Costantini Marco Perugini
author_facet	Giulio Costantini Marco Perugini
author_sort	Giulio Costantini
title	Generalization of clustering coefficients to signed correlation networks.
title_short	Generalization of clustering coefficients to signed correlation networks.
title_full	Generalization of clustering coefficients to signed correlation networks.
title_fullStr	Generalization of clustering coefficients to signed correlation networks.
title_full_unstemmed	Generalization of clustering coefficients to signed correlation networks.
title_sort	generalization of clustering coefficients to signed correlation networks.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/5da1566a8a4a44789a1593fc7d4f0071
work_keys_str_mv	AT giuliocostantini generalizationofclusteringcoefficientstosignedcorrelationnetworks AT marcoperugini generalizationofclusteringcoefficientstosignedcorrelationnetworks
_version_	1718421684808056832

Generalization of clustering coefficients to signed correlation networks.

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