Multi-objective optimization approach to detecting extremal patterns in social networks

This paper introduces the use of extremal patterns as a way to characterize social networks. The concepts of Pareto-dominance, multi-objective optimization, and estimation of distribution algorithms are integrated in a general strategy to compute the multiple extremal patterns. The algorithm is applied to the identification of sets of subjects that have the broadest direct network reachability in a social network extracted from the Reality mining dataset.