Detecting the overlapping and hierarchical community structure of complex networks

TL;DR

This paper introduces a novel algorithm that detects overlapping and hierarchical community structures in complex networks by optimizing a fitness function, revealing multi-level organization with high accuracy.

Contribution

The paper presents the first algorithm capable of simultaneously identifying overlapping communities and hierarchical structures in complex networks.

Findings

Effective detection of overlapping communities.

Ability to explore different hierarchical levels.

Excellent results on real and artificial networks.

Abstract

Many networks in nature, society and technology are characterized by a mesoscopic level of organization, with groups of nodes forming tightly connected units, called communities or modules, that are only weakly linked to each other. Uncovering this community structure is one of the most important problems in the field of complex networks. Networks often show a hierarchical organization, with communities embedded within other communities; moreover, nodes can be shared between different communities. Here we present the first algorithm that finds both overlapping communities and the hierarchical structure. The method is based on the local optimization of a fitness function. Community structure is revealed by peaks in the fitness histogram. The resolution can be tuned by a parameter enabling to investigate different hierarchical levels of organization. Tests on real and artificial networks give excellent results.