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Community structures are an important feature of many social, biological andtechnological networks. Here we study a variation on the method for detectingsuch communities proposed by Girvan and Newman and based on the idea of usingcentrality measures to define the community boundaries (M. Girvan and M. E. J.Newman, Community structure in social and biological networks Proc. Natl. Acad.Sci. USA 99, 7821-7826 (2002)). We develop an algorithm of hierarchicalclustering that consists in finding and removing iteratively the edge with thehighest information centrality. We test the algorithm on computer generated andreal-world networks whose community structure is already known or has beenstudied by means of other methods. We show that our algorithm, although it runsto completion in a time O(n^4), is very effective especially when thecommunities are very mixed and hardly detectable by the other methods.Comment: 13 pages, 13 figures. Final version accepted for publication in  Physical Review

Method to find community structures based on information centrality