Social Network Analysis and Mining: Review Methods for Analyzing and Understanding Social Network Structures and Behavior

In recent decades, the proliferation of social media and online communication platforms has necessitated advanced methodologies for analyzing social network structures and user behavior. Social Network Analysis (SNA) emerges as a critical interdisciplinary approach integrating sociol- ogy, network theory, and data science to unravel the complexities inherent in social networks. This review elucidates fundamental methods of SNA, encompassing both classical techniques and modern algorithmic advancements. We discuss network metrics such as centrality, modularity, and clustering, which provide insights into network topology and the roles of individual nodes. Furthermore, we explore dynamic network analysis, predictive modeling, and visualization techniques that facilitate understand- ing of temporal network evolution and user interaction patterns. The challenges and limitations of existing methodologies are also addressed, highlighting opportunities for future research in enhancing the precision and scalability of social network analyses. Visual examples and case studies illustrate the application of these methods, underscoring their practicality in deciphering social phenomena and guiding technological innovations.