Finding instabilities in the community structure of complex networks

TL;DR

This paper introduces a novel method to detect nodes between clusters and assess cluster stability in complex networks by adding noise to edge weights, applicable with various clustering algorithms.

Contribution

It presents a new approach to identify boundary nodes and measure cluster stability, extending existing clustering techniques to weighted networks.

Findings

Method successfully identifies boundary nodes in real-world networks.

Provides a general measure of cluster stability.

Applicable with any clustering algorithm that handles weighted networks.

Abstract

The problem of finding clusters in complex networks has been extensively studied by mathematicians, computer scientists and, more recently, by physicists. Many of the existing algorithms partition a network into clear clusters, without overlap. We here introduce a method to identify the nodes lying ``between clusters'' and that allows for a general measure of the stability of the clusters. This is done by adding noise over the weights of the edges of the network. Our method can in principle be applied with any clustering algorithm, provided that it works on weighted networks. We present several applications on real-world networks using the Markov Clustering Algorithm (MCL).