Finding statistically significant communities in networks

TL;DR

OSLOM is a versatile community detection method that identifies statistically significant clusters in networks, accounting for various complexities like direction, weights, overlaps, and hierarchies, suitable for large and real-world networks.

Contribution

This paper introduces OSLOM, the first community detection algorithm that considers multiple network features and statistical significance, improving flexibility and accuracy over existing methods.

Findings

OSLOM performs comparably to top algorithms on benchmark graphs.

It effectively detects communities in real-world networks.

The method handles overlapping, directed, weighted, and hierarchical communities.

Abstract

Community structure is one of the main structural features of networks, revealing both their internal organization and the similarity of their elementary units. Despite the large variety of methods proposed to detect communities in graphs, there is a big need for multi-purpose techniques, able to handle different types of datasets and the subtleties of community structure. In this paper we present OSLOM (Order Statistics Local Optimization Method), the first method capable to detect clusters in networks accounting for edge directions, edge weights, overlapping communities, hierarchies and community dynamics. It is based on the local optimization of a fitness function expressing the statistical significance of clusters with respect to random fluctuations, which is estimated with tools of Extreme and Order Statistics. OSLOM can be used alone or as a refinement procedure of partitions/covers delivered by other techniques. We have also implemented sequential algorithms combining OSLOM with other fast techniques, so that the community structure of very large networks can be uncovered. Our method has a comparable performance as the best existing algorithms on artificial benchmark graphs. Several applications on real networks are shown as well. OSLOM is implemented in a freely available software (http://www.oslom.org), and we believe it will be a valuable tool in the analysis of networks.