| Summary: | With the increasing diversity of social media, the demand for real-time analysis of social networks continues to increase, and research on dynamic community detection has received extensive attention. Existing community detection reviews mostly focus on static community detection and the discussion of related methods, so network evolution analysis cannot be carried out. Besides, the entity data in the community are cross-substitutional and sequential. Therefore, the research status of dynamic community detection is studied, reviewed, and analyzed. First, based on the research background of complex networks, a general research framework for dynamic community detection is proposed. Then, the relevant definitions of dynamic community detection are formally expressed, and the evolution of dynamic communities at the network level and node level is analyzed in detail. According to the difference in architecture and technology, the dynamic community detection methods are summarized and classified. Combined with commonly used data sets, the static community detection algorithm is analyzed qualitatively and quantitatively with evaluation criteria. Finally, the typical application scenarios of community detection are introduced, the main challenges faced by current dynamic community detection research are discussed, and relevant solutions are put forward for dynamic communities, which outlines a clearer and comprehensive research direction for dynamic community detection research field.
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