Community structure is one of the most important topological properties in complex networks. The network clustering problem (NCP) refers to the detection of network community structures, and many practical problems can be modeled as NCPs. So far, lots of network clustering algorithms have been proposed. However, further improvements in the clustering accuracy, especially when discovering reasonable community structure without prior knowledge, still constitute an open problem. Building on Markov random walks, the paper addresses this problem with a novel ant colony optimization strategy, named as RWACO, which improves prior results on the NCPs and does not require knowledge of the number of communities present on a given network. The framework of ant colony optimization is taken as the basic framework in the RWACO algorithm. In each iteration, a Markov random walk model is taken as heuristic rule. All of the ants’ local solutions are aggregated to a global one through 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 performance of algorithm RWACO was tested against a set of benchmark computer-generated networks, and as well on real-world network data sets. Experimental results confirm the validity and improvements of this approach.