Abstract
Graph databases, designed to store and query data in the form of interconnected nodes and edges, offer a powerful solution for managing complex relationships found in social networks. Unlike traditional relational databases, which struggle with the inherent complexity of interconnected data, graph databases provide an intuitive and efficient way to model, analyze, and query social network data. This paper explores the role of graph databases in social networks, discussing their architecture, querying capabilities, and key advantages over other data models. We also delve into real-world applications in social media, recommendation systems, fraud detection, and community detection, emphasizing how graph-based structures enhance data insights and decision-making
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