Automatic detection of cohesive subgroups within social hypertext: A heuristic approach

Authors: Chin, Alvin¹; Chignell, Mark²

Source: New Review in Hypermedia and Multimedia, Volume 14, Number 1, January 2008 , pp. 121-143(23)

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Abstract:

The problem of identifying cohesive subgroups in social hypertext is reviewed. A computationally efficient three-step framework for identifying cohesive subgroups is proposed, referred to as the Social Cohesion Analysis of Networks (SCAN) method. In the first step of this method (Select), people within a social network are screened using a level of network centrality to select possible subgroup members. In the second step (Collect), the people selected in the first step are collected into subgroups identified at each point in time using hierarchical cluster analysis. In the third step (Choose), similarity modeling is used to choose cohesive subgroups based on the similarity of subgroups when compared across different points in time. The application of this SCAN method is then demonstrated in a case study where a subgroup is automatically extracted from a social network formed based on the online interactions of a group of about 150 people that occurred over a two-year period. In addition, this paper also demonstrates that similarity-based cohesion can provide a different, and in this case more compelling, subgroup representation than a method based on splitting a hierarchical clustering dendrogram using an optimality criterion.

Keywords: Social Hypertext; Social Networks; Cluster Analysis; Cohesive Subgroups; Similarity Measurement; Online Community; Network Centrality; Subgroup Evolution

Document Type: Research article

DOI: http://dx.doi.org/10.1080/13614560802357180

Affiliations: 1: Department of Computer Science, University of Toronto, Toronto, Ontario, Canada 2: Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada

Publication date: 2008-01-01