Novel model of social networks with tunable clustering coefficient
نویسندگان
چکیده
We propose a novel method to generate scale-free networks with discretely tunable clustering coefficient in order to model real social networks. Recently several methods were introduced to generate networks with power law degree distribution. Most of them are based on preferential attachment, but in these networks the average clustering coefficient is low opposite to the real social networks. Beside the attractive popularity our model is based on the fact that if a person knows somebody, probably knows several people from his/her acquaintanceship as well. The topological properties of these networks were studied and it was found that in these networks the cliques are relevant independently from the system size.
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