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Clearing the FOG: Fuzzy, overlapping groups for social networks
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
doi:10.1016/j.socnet.2008.03.001
fatcat:pxvayv2vp5glbfx56bjw7kkom4