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Dynamic community detection is significant for controlling and capturing the temporal features of networks. The evolutionary clustering framework provides a temporal smoothness constraint for simultaneously maximizing the clustering quality at the current time step and minimizing the clustering deviation between two successive time steps. Based on this framework, some existing methods, such as the evolutionary spectral clustering and evolutionary nonnegative matrix factorization, aim to lookdoi:10.1007/s11432-020-2827-9 fatcat:io44rpasn5fypgqebitcmplmda