Finding instabilities in the community structure of complex networks.
The problem of finding clusters in complex networks has been studied by mathematicians, computer scientists, and, more recently, by physicists. Many of the existing algorithms partition a network into clear clusters without overlap. Here we introduce a method to identify the nodes lying "between clusters," allowing for a general measure of the stability of the clusters. This is done by adding noise over the edge weights. Our method can in principle be used with almost any clustering algorithm able to deal with weighted networks. We present several applications on real-world networks using two different clustering algorithms.
Gfeller
David
D
Laboratoire de Biophysique Statistique, SB/ITP, Ecole Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland.
Chappelier
Jean-Cédric
JC
De Los Rios
Paolo
P