Tracking cohesive subgroups over time in inferred social networks

Authors:
Alvin Chin;Mark Chignell;Hao Wang
Affiliations:
Mobile Social Networking Group, Nokia Research Center, Beijing, China;Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada;Mobile Social Networking Group, Nokia Research Center, Beijing, China
Venue:
The New Review of Hypermedia and Multimedia - Web Science
Year:
2010

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Abstract

As a first step in the development of community trackers for large-scale online interaction, this paper shows how cohesive subgroup analysis using the Social Cohesion Analysis of Networks (SCAN; Chin and Chignell 2008) and Data-Intensive Socially Similar Evolving Community Tracker (DISSECT; Chin and Chignell 2010) methods can be applied to the problem of identifying cohesive subgroups and tracking them over time. Three case studies are reported, and the findings are used to evaluate how well the SCAN and DISSECT methods work for different types of data. In the largest of the case studies, variations in temporal cohesiveness are identified across a set of subgroups extracted from the inferred social network. Further modifications to the DISSECT methodology are suggested based on the results obtained. The paper concludes with recommendations concerning further research that would be beneficial in addressing the community tracking problem for online data.