Abstract
Uncovering the dynamics of community structures in complex networks helps us to explore how such community structures change over time. But, understanding these structures is very challenging, especifically in dynamic complex networks where network structure changes frequently and interaction between the individuals changes over time. Recently, many dynamic community detection algorithms have been introduced to capture the dynamics of network community structures. In this paper, we present a detailed analysis of the dynamic community detection algorithms in terms of computation time and accuracy. To provide detailed and extensive analysis, we tested dynamic algorithms on small, medium and large real-world network dataset. Based on the analysis results and network properties, we provide some guidelines that may help to choose the best dynamic community detection algorithms for the given dynamic complex networks.
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