Applications of Graph-Based AI Models in Social Network Analysis: Uncovering Hidden Relationships

Abstract

Social networks are intricate webs of relationships that encapsulate interactions among individuals, organizations, and entities. The exponential growth of social media and online platforms has created vast datasets that present both opportunities and challenges for analysis. Graph-based AI models have emerged as powerful tools for navigating this complexity, allowing researchers to uncover hidden relationships, predict behavior, and enhance user experiences. This paper explores the applications of graph-based AI models in social network analysis, focusing on their capabilities to reveal hidden relationships, identify influential nodes, and support targeted interventions. We present a review of existing methodologies, highlight case studies, and discuss future directions for research in this domain.