Visualizing the Evolution of Community Structures in Dynamic Social Networks

Abstract

Social network analysis is the study of patterns of interaction between social entities. The field is attracting increasing attention from diverse disciplines including sociology, epidemiology, and behavioral ecology. An important sociological phenomenon that draws the attention of analysts is the emergence of communities, which tend to form, evolve, and dissolve gradually over a period of time. Understanding this evolution is crucial to sociologists and domain scientists, and often leads to a better appreciation of the social system under study. Therefore, it is imperative that social network visualization tools support this task. While graph-based representations are well suited for investigating structural properties of networks at a single point in time, they appear to be significantly less useful when used to analyze gradual structural changes over a period of time. In this paper, we present an interactive visualization methodology for dynamic social networks. Our technique focuses on revealing the community structure implied by the evolving interaction patterns between individuals. We apply our visualization to analyze the community structure in the US House of Representatives. We also report on a user study conducted with the participation of behavioral ecologists working with social network datasets that depict interactions between wild animals. Findings from the user study confirm that the visualization was helpful in providing answers to sociological questions as well as eliciting new observations on the social organization of the population under study.