Abstract

Abstract — The formation and evolution of interest groups in Online Social Networks is driven by both the users ’ preferences and the choices of the groups ’ administrators. In this context, the notion of homogeneity of a social group is crucial: it accounts for determining the mutual similarity among the members of a group and it’s often regarded as fundamental to determine the satisfaction of group members. In this paper we propose a group homogeneity measure that takes into account behavioral information of users, and an algorithm to optimize such a measure in a social network scenario by matching users and groups profiles. We provide an advantageous formulation of such framework by means of a fully-distributed multi-agent system. Experiments on simulated social network data clearly highlight the performance improvement brought by our approach.