Improving Agent Group Homogeneity Over Time

In social communities the agent groups composition could vary over time due to changes occurring in agents’ behaviors. To study the time evolution of such processes, we propose a conceptual framework exploiting a distributed algorithm driving the group formation. The results of tests carried out on real data, extracted by the social network CIAO, show as groups formed by combining similarity and trust measures are i) more timestable, independently by the weight of the trust component, and ii) more time-homogeneous, independently by the presence of uncorrelated random agents’ behaviors affecting the similarity component.