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Evolutionary clustering – clustering in the presence of dynamic shifts of data's topological structure – has recently drawn remarkable attention wherein several algorithms are developed in the study of complex real networks. Despite the growing interests, all of the algorithms are designed based on seemingly the same principle. The primary principle in these evolutionary clustering frameworks is guided by decomposing the problem into two individual criteria, snapshot quality and temporal smoothness. Snapshot quality should properly cluster individuals of a network into interconnected communities. Temporal smoothness, on the other hand, should capture well the dynamic shift of the interconnected clusters from one time step to another. Thus, in the absence of any dynamic behavior, an evolutionary clustering model should be no more than a community detection one in a static network. Unfortunately, all of the …