Ant colony optimization for community detection in large-scale complex networks

In this paper we present a new ant colony optimization for community detection in large networks, which takes modularity Q as objective function. An important difference that distinguishes our algorithm from the former ant algorithms is the manner in which the ants are used in the algorithm. Unlike those existing methods in which each ant searches for a candidate solution, each ant in our algorithm only decides whether its current vertex joins the community of its previous vertex with the aid of a simulated annealing idea, whose purpose is to locally optimize function Q. In each iteration, the ants work collectively so as to uncover the community structure of the network. Moreover, we introduce a thought of “layer and rule” into this method for further improving its performance. Our algorithm doesn't employ the pheromone, which reduces its running time and makes it well suitable for large-scale networks. Meanwhile, it still performs very well on both computer-generated benchmark and some widely used real-world networks compared with a set of competing algorithm in terms of clustering quality.