A New Betweenness Centrality Algorithm with Local Search for Community Detection in Complex Network

Abstract

Community structure identification in complex networks has been an important research topic in recent years. In this paper, a new between-ness centrality algorithm with local search called BCALS in short, is proposed as an effective optimization technique to solve the community detection problem with the advantage that the number of communities is automatically determined in the process. BCALS selects at first, leaders according to their measure of between-ness centrality, then it selects randomly a node and calculates its local function for all communities and assigns it to the community that optimizes its local function. Experiments show that BCALS gets effective results compared to other detection community algorithms found in the literature.