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The community detection algorithms based on label propagation (LPA) receive broad attention for the advantages of near-linear complexity and no prerequisite for any object function or cluster number. However, the propagation of labels contains uncertainty and randomness, which affects the accuracy and stability of the LPA algorithm. In this study, we propose an efficient detection method based on COPRA with Time-sequence (COPRA_TS). Firstly, the labels are sorted according to a new label importance measure. Then, the label of each vertex is updated according to time-sequence topology measure. The experiments on both the artificial datasets and the real-world datasets demonstrate that the quality of communities discovered by COPRA_TS algorithm is improved with a better stability. At last some future research topics are given.