Community detection and Social Network analysis based on the Italian wars of the 15th century
arXiv:2007.02641 · doi:10.1016/j.future.2020.06.030
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.
Corrections in: Revamped affinity section, conclusions and minor changes in the introduction. Also, the dynamic delta section is expanded a bit
References in corpus (7)
- Fast unfolding of communities in large networks
- Modularity and community structure in networks
- Uncovering the overlapping community structure of complex networks in nature and society
- Finding community structure in networks using the eigenvectors of matrices
- Cooperative Game Theory Approaches for Network Partitioning
- Near linear time algorithm to detect community structures in large-scale networks
- Comparing community structure identification