On community structure validation in real networks

Computational Statistics, vol. 37, pp. 1165–1183

Abstract

Abstract Community structure is a commonly observed feature of real networks. The term refers to the presence in a network of groups of nodes (communities) that feature high internal connectivity, but are poorly connected between each other. Whereas the issue of community detection has been addressed in several works, the problem of validating a partition of nodes as a good community structure for a real network has received considerably less attention and remains an open issue. We propose a set of indices for community structure validation of network partitions that are based on an hypothesis testing procedure that assesses the distribution of links between and within communities. Using both simulations and real data, we illustrate how the proposed indices can be employed to compare the adequacy of different partitions of nodes as community structures in a given network, to assess whether two networks share the same or similar community structures, and to evaluate the performance of different network clustering algorithms.

Authors 2

1. Mirko Signorelli corresponding Aachen Mathematical Institute

   Leiden University

   Affiliation as printed

   Mathematical Institute, Leiden University, Leiden, The Netherlands

   Mathematical Institute, Leiden University, Leiden, the Netherlands

2. Luisa Cutillo corresponding

   University of Leeds

   Affiliation as printed

   School of Mathematics, University of Leeds, Leeds, UK

   School of Mathematics, University of Leeds, Leeds, UK#TAB#