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A community within a network is a group of vertices densely connected to eachother but less connected to the vertices outside. The problem of detectingcommunities in large networks plays a key role in a wide range of researchareas, e.g. Computer Science, Biology and Sociology. Most of the existingalgorithms to find communities count on the topological features of the networkand often do not scale well on large, real-life instances.  In this article we propose a strategy to enhance existing community detectionalgorithms by adding a pre-processing step in which edges are weightedaccording to their centrality w.r.t. the network topology. In our approach, thecentrality of an edge reflects its contribute to making arbitrary graphtranversals, i.e., spreading messages over the network, as short as possible.Our strategy is able to effectively complements information about networktopology and it can be used as an additional tool to enhance communitydetection. The computation of edge centralities is carried out by performingmultiple random walks of bounded length on the network. Our method makes thecomputation of edge centralities feasible also on large-scale networks. It hasbeen tested in conjunction with three state-of-the-art community detectionalgorithms, namely the Louvain method, COPRA and OSLOM. Experimental resultsshow that our method raises the accuracy of existing algorithms both onsynthetic and real-life datasets.

Enhancing community detection using a network weighting strategy