Density-Based Clustering of Social Networks
Giovanna Menardi, Domenico De Stefano
Abstract
Abstract The idea of the modal formulation of density-based clustering is to associate groups with the regions around the modes of the probability density function underlying the data. The correspondence between clusters and dense regions in the sample space is here exploited to discuss an extension of this approach to the analysis of social networks. Conceptually, the notion of high-density cluster fits well the one of community in a network, regarded to as a collection of individuals with dense local ties in its neighbourhood. The lack of a probabilistic notion of density in networks is turned into a strength of the proposed method, where node-wise measures that quantify the role of actors are used to derive different community configurations. The approach allows for the identification of a hierarchical structure of clusters, which may catch different degrees of resolution of the clustering structure. This feature well fits the nature of social networks, disentangling different involvements of individuals in aggregations.