GRAPH NEURAL NETWORKS FOR SOCIAL NETWORK ANALYSIS: RECENT ADVANCES, APPLICATIONS, AND RESEARCH CHALLENGES

The rapid expansion of online social platforms has generated vast amounts of interconnected data, making traditional machine learning techniques insufficient for capturing complex relationships among users and communities. Graph Neural Networks (GNNs) have emerged as a powerful deep learning paradigm capable of learning from graph-structured data by leveraging node features, edge relationships, and network topology. This paper presents a comprehensive review of Graph Neural Networks for social network analysis, covering their architecture, learning mechanisms, major algorithms, practical applications, advantages, challenges, and future research directions. The paper also explores the integration of GNNs with explainable artificial intelligence, federated learning, and large language models for advanced graph analytics. The findings demonstrate that Graph Neural Networks significantly improve prediction accuracy, community detection, recommendation systems, misinformation detection, and user behavior analysis while opening new opportunities for intelligent social computing.