Exploring Socially Cohesive Person-based Communities

The last years, there has been a great interest in detecting overlapping communities in complex networks, which is understood as dense groups of nodes featuring a low outbound density. To date, most methods used to uncover overlapping communities stem from the field of disjoint community detection by attempting to decompose the whole network into several possibly overlapping groups of nodes. In this article we take an orthogonal approach by introducing a novel point of view to the problem of overlapping communities, namely the concept of egomunities, which are subjective communities centered around a given node, more precisely inside its neighborhood. In order to construct those egomunities, we propose a general metric on graphs, the cohesion, inspired by sociological considerations. The cohesion quantifies the community-ness of one given set of nodes, based on the notions of weak ties and triangles – triplets of pairwise connected nodes, instead of the classical view using only edge density. A set of nodes has a high cohesion if it features a high density of triangles and intersects few triangles with the rest of the network. We build upon the cohesion to construct a heuristic algorithm which uncovers egomunities of a given node by attempting to maximize their cohesion. We illustrate the pertinence of our method with a detailed description of one person’s egomunities among Facebook friends and promising results from an ongoing large scale Facebook experiment. We finally conclude by describing promising applications of egomunities such as information inference and interest recommendations, and present a possible extension to cohesion in the case of weighted networks. Key-words: social networks, complex networks, real-world graphs, community detection, overlapping communities, data mining, modelisation in ria -0 05 65 33 6, v er si on 2 19 F eb 2 01 1