An overlapping community detection algorithm based on Lévy flight

Abstract

Considering the phenomenon that most intelligent optimization algorithms easily plunge into local optimum when solving the complex problem of overlapping community detection, LFOCDA, an overlapping community detection algorithm based on Lévy flight, is proposed in this paper. An extended modularity function has been used as the quality measurement standard of overlapping community division. During the iterative renewed process, Lévy flight has been introduced owing to the variable-step random walk characteristic, so that the algorithm has a chance to jump out of local optimum, thus expanding the search range and guiding the search closer to global optimal solution. Enormous experimental results have verified the effectiveness and adaptability of the algorithm.