In recent years, there is an increasing interest in the research community in finding community structure in complex networks. The networks are usually represented as graphs, and the task is usually cast as a graph clustering problem. Traditional clustering algorithms and graph partitioning algorithms have been applied to this problem. New graph clustering algorithms have also been proposed. Random walk based clustering, in which the similarities between pairs of nodes in a graph are usually estimated using random walk with restart (RWR) algorithm, is one of the most popular graph clustering methods. Most of these clustering algorithms only find disjoint partitions in networks; however, communities in many real-world networks often overlap to some degree. In this paper, we propose an efficient clustering method based on random walks for discovering communities in graphs. The proposed method makes use of network topology and edge weights, and is able to discover overlapping communities. We analyze the effect of parameters in the proposed method on clustering results. We evaluate the proposed method on real world social networks that are well documented in the literature, using both topological-based and knowledge-based evaluation methods. We compare the proposed method to other clustering methods including recently published Repeated Random Walks, and find that the proposed method achieves better precision and accuracy values in terms of six statistical measurements including both data-driven and knowledge-driven evaluation metrics.
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