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.

中文翻译：

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基于 15 世纪意大利战争的社区检测和社交网络分析

在这个贡献中，我们通过使用人类交互作为基础来研究社交网络建模。为此，我们提出了一组新的函数，亲和力，旨在捕捉网络中每对参与者之间局部交互的性质。通过使用这些函数，我们开发了一种新的社区检测算法 Borgia Clustering，其中社区自然地从网络中的多代理交互中产生。我们还讨论了在这种情况下社区规模和规模的影响，以及我们如何应对大型社区出现时出现的额外复杂性。最后，我们将我们的社区检测解决方案与其他有代表性的算法进行比较，找到了有利的结果。