Detecting Clusters/Communities in Social Networks. (2nd January 2018)

• Record Type: Journal Article
• Authors: Hoffman, Michaela; Steinley, Douglas; Gates, Kathleen M.; Prinstein, Mitchell J.; Brusco, Michael J.
• Is Part Of: Multivariate behavioral research. Volume 53:Number 1(2018)
• Page Start: 57
• Page End: 73
• Publication Date: 2018-01-02
• Subjects: Network analysis -- cluster analysis -- community detection -- Cohen's kappa; Psychometrics -- Periodicals; Psychology, Experimental -- Periodicals; Psychology, Experimental; Psychometrics; Periodicals; 150.15195
• DOI: 10.1080/00273171.2017.1391682
• Languages: English
• ISSNs: 0027-3171

Abstract

ABSTRACT: Cohen's κ, a similarity measure for categorical data, has since been applied to problems in the data mining field such as cluster analysis and network link prediction. In this paper, a new application is examined: community detection in networks. A new algorithm is proposed that uses Cohen's κ as a similarity measure for each pair of nodes; subsequently, the κ values are then clustered to detect the communities. This paper defines and tests this method on a variety of simulated and real networks. The results are compared with those from eight other community detection algorithms. Results show this new algorithm is consistently among the top performers in classifying data points both on simulated and real networks. Additionally, this is one of the broadest comparative simulations for comparing community detection algorithms to date.