Graph-Based AI Algorithms for Social Network Analysis in Big Data

Abstract

The explosive growth of digital data in online social networks has paved the way for a new era of insights and understanding through the lens of graph-based Artificial Intelligence (AI) algorithms. This paper explores the convergence of graph theory and AI techniques in the context of Big Data for Social Network Analysis (SNA). By leveraging graph representations, this research delves into the complexities of analyzing large-scale social networks, offering an overview of cutting-edge AI methodologies tailored to decipher intricate relationships and patterns within these networks. Addressing scalability and efficiency challenges, it demonstrates the potential for graph-based AI algorithms in diverse applications, from targeted marketing to fraud detection. Furthermore, this paper outlines the current challenges and prospects in this domain, illustrating the pivotal role that these algorithms play in extracting valuable insights from the vast and complex landscape of social network data in the Big Data era.