Clearing the FOG: Fuzzy, overlapping groups for social networks

George B. Davis, Kathleen M. Carley
2008 Social Networks  
Humans are well known to belong to many associative groups simultaneously, with various levels of affiliation. However, most group detection algorithms for social networks impose a strict partitioning on nodes, forcing entities to belong to a single group. Link analysis research has produced several methods which detect multiple memberships but force equal membership. This paper extends these approaches by introducing the FOG framework, a stochastic model and group detection algorithm for
more » ... overlapping groups. We apply our algorithm to both link data and network data, where we use a random walk approach to generate rich links from networks. The results demonstrate that not only can fuzzy groups be located, but also that the strength of membership in a group and the fraction of individuals with exclusive membership are highly informative of emerging group dynamics.
doi:10.1016/j.socnet.2008.03.001 fatcat:pxvayv2vp5glbfx56bjw7kkom4