In social communities the composition of thematic groups varies over time due to changes occurring in users' behaviors. To study the time evolution of such a process, we design a conceptual framework exploiting a distributed algorithm driving 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 time-stable, 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
Messina, Fabrizio;
2017-01-01
Abstract
In social communities the composition of thematic groups varies over time due to changes occurring in users' behaviors. To study the time evolution of such a process, we design a conceptual framework exploiting a distributed algorithm driving 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 time-stable, 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.File in questo prodotto:
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