  The idea underlying 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. This 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. Such extension seems particularly
appealing: 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 major strength of the proposed method,
where node-wise measures that quantify the role and position of actors may be
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 a different involvement of
individuals in social aggregations.
