Ensembles based on the Rich-Club and how to use them to build soft-communities

TL;DR

This paper introduces an efficient maximal entropy algorithm to generate network ensembles based on degree sequences and rich-club coefficients, facilitating the identification of soft communities in real networks.

Contribution

It presents a novel method for creating network null-models using rich-club properties, aiding in community detection.

Findings

The algorithm efficiently generates network ensembles with specified properties.

Ensembles can distinguish between correlated and uncorrelated network structures.

Application to real networks demonstrates utility in soft-community detection.

Abstract

Ensembles of networks are used as null-models to discriminate network structures. We present an efficient algorithm, based on the maximal entropy method to generate network ensembles defined by the degree sequence and the rich-club coefficient. The method is applicable for unweighted, undirected networks. The ensembles are used to generate correlated and uncorrelated null--models of a real networks. These ensembles can be used to define the partition of a network into soft communities.