Detecting Overlapping Communities in Directed Networks Based on Link Similarity

Identifying overlapping communities in networks has attracted increasing attention recently, but the most common approach to this problem has been to ignore the edge direction and apply the methods in undirected networks. In this paper, an overlapping communities detecting algorithm in directed networks is proposed whose partition communities as groups of links. The transcriptional regulatory network (TRN) of E. coli are used to evaluate the algorithm. Experimental results demonstrate that the algorithm proposed is efficient for detecting overlapping communities in directed networks.