Work

Detecting Clusters/Communities in Social Networks

Year: 2017

Type: article

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.

Source: Multivariate Behavioral Research

DOI: 10.1080/00273171.2017.1391682

Authors: Michaela Hoffman, Douglas Steinley, Kathleen M. Gates, Mitchell J. Prinstein, Michael J. Brusco

Institutions: University of Missouri, University of North Carolina at Chapel Hill, Florida State University

Language: English

Topic: Complex Network Analysis Techniques

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

Funders: National Institute on Alcohol Abuse and Alcoholism, Eunice Kennedy Shriver National Institute of Child Health and Human Development

Awards: Understanding Peer Influence of Adolescent Health Risk Behaviors, Cost-effective sampling for social network data to minimize measurement error