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. © 2014 Costantini, Perugini.

Costantini, G., Perugini, M. (2014). Generalization of clustering coefficients to signed correlation networks. PLOS ONE, 9(2) [10.1371/journal.pone.0088669].

Generalization of clustering coefficients to signed correlation networks

COSTANTINI, GIULIO
Primo
;
PERUGINI, MARCO
Ultimo
2014

Abstract

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. © 2014 Costantini, Perugini.
Articolo in rivista - Articolo scientifico
Cluster Analysis; Computer Simulation; Humans; Psychopathology; Algorithms; Models, Theoretical; Personality; Agricultural and Biological Sciences (all); Biochemistry, Genetics and Molecular Biology (all); Medicine (all)
English
2014
9
2
e88669
open
Costantini, G., Perugini, M. (2014). Generalization of clustering coefficients to signed correlation networks. PLOS ONE, 9(2) [10.1371/journal.pone.0088669].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/60113
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