  In this contribution we study social network modelling by using human
interaction as a basis. To do so, we propose a new set of functions,
affinities, designed to capture the nature of the local interactions among each
pair of actors in a network. By using these functions, we develop a new
community detection algorithm, the Borgia Clustering, where communities
naturally arise from the multi-agent interaction in the network. We also
discuss the effects of size and scale for communities regarding this case, as
well as how we cope with the additional complexity present when big communities
arise. Finally, we compare our community detection solution with other
representative algorithms, finding favourable results.
