Community mining using three closely joint techniques based on community mutual membership and refinement strategy

Community structure has become one of the central studies of the topological structure of complex networks in the past decades. Although many advanced approaches have been proposed to identify community structure, those state-of-the-art methods still lack efficiency in terms of a balance between stability, accuracy and computation time. Here, we propose an algorithm with different stages, called TJA-net, to efficiently identify communities in a large network with a good balance between accuracy, stability and computation time. First, we propose an initial labeling algorithm, called ILPA, combining K-nearest neighbor (KNN) and label propagation algorithm (LPA). To produce a number of sub-communities automatically, ILPA iteratively labels a node in a network using the labels of its adjacent nodes and their index of closeness. Next, we merge sub-communities using the mutual membership of two communities. Finally, a refinement strategy is designed for modifying the label of the wrongly clustered nodes at boundaries. In our approach, we propose and use modularity density as the objective function rather than the commonly used modularity. This can deal with the issue of the resolution limit for different network structures enhancing the result precision. We present a series of experiments with artificial and real data set and compare the results obtained by our proposed algorithm with the ones obtained by the state-of-the-art algorithms, which shows the effectiveness of our proposed approach. The experimental results on large-scale artificial networks and real networks illustrate the superiority of our algorithm.