Community detection and Social Network analysis based on the Italian wars of the 15th century
This work addresses community detection in social networks, specifically applied to historical data from the Italian wars of the 15th century, representing a domain-specific incremental advancement.
The authors tackled community detection in social networks by developing a new algorithm called Borgia Clustering, which uses novel affinity functions to model local interactions between actors, and found favorable results compared to other algorithms.
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.