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.

在这项贡献中，我们以人际互动为基础研究了社交网络建模。为此，我们提出了一组新的功能，关联性，旨在捕获网络中每对参与者之间本地交互的性质。通过使用这些功能，我们开发了一种新的社区检测算法，即Borgia聚类，该社区自然是由网络中的多主体交互产生的。我们还将讨论这种情况下社区规模和规模的影响，以及当大型社区出现时我们如何应对当前存在的额外复杂性。最后，我们将我们的社区检测解决方案与其他代表性算法进行了比较，找到了令人满意的结果。