  Detecting communities from complex networks has recently triggered great
interest. Aiming at this problem, a new ant colony optimization strategy
building on the Markov random walks theory, which is named as MACO, is proposed
in this paper. The framework of ant colony optimization is taken as the basic
framework in this algorithm. In each iteration, a Markov random walk model is
employed as heuristic rule; all of the ants local solutions are aggregated to a
global one through an idea of clustering ensemble, which then will be used to
update a pheromone matrix. The strategy relies on the progressive strengthening
of within-community links and the weakening of between-community links.
Gradually this converges to a solution where the underlying community structure
of the complex network will become clearly visible. The proposed MACO has been
evaluated both on synthetic benchmarks and on some real-world networks, and
compared with some present competing algorithms. Experimental result has shown
that MACO is highly effective for discovering communities.
