Community detection and Social Network analysis based on the Italian wars of the 15th century

TL;DR

This paper introduces a novel community detection algorithm called Borgia Clustering, based on new affinity functions that model local interactions in social networks, applied to the context of 15th-century Italian wars.

Contribution

It presents a new set of affinity functions and the Borgia Clustering algorithm for community detection, specifically tailored to social networks derived from historical human interactions.

Findings

Favorable comparison with existing algorithms

Effective detection of communities in historical social networks

Insights into community size and scale effects

Abstract

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.