Density-based clustering of social networks

TL;DR

This paper extends density-based clustering methods to social networks by using node-wise measures to identify communities, capturing hierarchical structures and different levels of social involvement without relying on probabilistic density.

Contribution

It introduces a novel approach that adapts density-based clustering to social networks, leveraging node measures to detect communities and hierarchical structures.

Findings

Identifies communities as dense regions in social networks.

Reveals hierarchical clustering structures.

Allows for multiple resolutions of social groupings.

Abstract

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.