A social community detection algorithm based on parallel grey label propagation

Community detection is one of the important methods for understanding the mechanism behind the function of social networks. The recently developed label propagation algorithm (LPA) has been gaining increasing attention because of its excellent characteristics, such as a succinct framework, linear time and space complexity, easy parallelization, etc. However, several limitations of the LPA algorithm, including random label initialization and greedy label updating, hinder its application to complex networks. A new parallel LPA is proposed in this study. First, grey relational analysis is integrated into the label updating process, which is based on vertex similarity. Second, parallel propagation steps are comprehensively studied to utilize parallel computation power efficiently. Third, randomness in label updating is significantly reduced via automatic label selection and label weight thresholding. Experiments conducted on artificial and real social networks demonstrate that the proposed algorithm is scalable and exhibits high clustering accuracy.

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