Research on Social Network Analysis (SNA) focuses on understanding complex relationships, interactions, and structures within social networks by leveraging advanced computational and analytical techniques. Recent studies have introduced AI-driven approaches, including graph neural networks and attention-based graph autoencoders, for anomaly detection and behavioral analysis, while fuzzy centrality measures allow the study of imprecise or uncertain relationships in networks. Methods such as aggregate relational data combined with variational approximations facilitate cost-effective inference of network structures, and hybrid frameworks like CUSA enhance community detection in networks that integrate human and AI entities. Overall, these innovations provide deeper insights into network dynamics, enabling improved analysis, prediction, and interpretation of social interactions across diverse contexts.