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,...

Full description

Saved in:
Bibliographic Details
Published in:PloS one 2014-02, Vol.9 (2), p.e88669-e88669
Main Authors: Costantini, Giulio, Perugini, Marco
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Behavior
Cluster Analysis
Clustering
Coefficients
Computer Science
Computer Simulation
Correlation
Gene expression
Humans
Mathematics
Medicine
Models, Theoretical
Neighborhoods
Network analysis
Networks
Personality
Personality psychology
Psychology
Psychopathology
Psychopathology - methods
Self esteem
Social and Behavioral Sciences
Social networks
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0088669