In the present article we study social network modelling  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. Using these functions, we develop a new community detection algorithm, the URLia 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, and 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.
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 URLia 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.
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