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.
Improving Agent Group Homogeneity Over Time / DE MEO, P; Messina, F; Rosaci, D; Sarne', G. - 1867:(2017), pp. 7.37-7.42. (Intervento presentato al convegno 18th International Workshop "From Objects To Agents" (WOA 2017) tenutosi a Scilla (Reggio Calabria), Italy nel 15-17, June, 2017).
Improving Agent Group Homogeneity Over Time
ROSACI D;SARNE' G
2017-01-01
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
