Core Trends in Complex Networks for Social Network Analysis

Complex network analysis has profoundly impacted various disciplines, influencing areas such as consumer behavior analysis in business and the foundational dynamics of social network formation. This study conducts a comprehensive review of the contemporary literature concerning clustering techniques within the realm of complex network analysis. We propose a systematic taxonomy that categorizes existing research into five distinct classes: modularity models, label propagation models, clique models, stochastic block models, and neighbor influence models. Through this framework, we perform a comparative analysis of these models, highlighting their performance metrics and inherent limitations in terms of time and space complexities. The manuscript concludes by addressing the limitations of current methodologies and outlining potential avenues for future research aimed at bridging these gaps.