  A community within a network is a group of vertices densely connected to each
other but less connected to the vertices outside. The problem of detecting
communities in large networks plays a key role in a wide range of research
areas, e.g. Computer Science, Biology and Sociology. Most of the existing
algorithms to find communities count on the topological features of the network
and often do not scale well on large, real-life instances.
  In this article we propose a strategy to enhance existing community detection
algorithms by adding a pre-processing step in which edges are weighted
according to their centrality w.r.t. the network topology. In our approach, the
centrality of an edge reflects its contribute to making arbitrary graph
tranversals, i.e., spreading messages over the network, as short as possible.
Our strategy is able to effectively complements information about network
topology and it can be used as an additional tool to enhance community
detection. The computation of edge centralities is carried out by performing
multiple random walks of bounded length on the network. Our method makes the
computation of edge centralities feasible also on large-scale networks. It has
been tested in conjunction with three state-of-the-art community detection
algorithms, namely the Louvain method, COPRA and OSLOM. Experimental results
show that our method raises the accuracy of existing algorithms both on
synthetic and real-life datasets.
