Today I read a paper titled “Finding statistically significant communities in networks”
The abstract is:
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 (this http URL), and we believe it will be a valuable tool in the analysis of networks