  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).
