Published Paper Details:

Graph theory provides a robust framework for analyzing and understanding complex social networks, where nodes represent individuals and edges denote their interactions. This paper explores various applications of graph theory in social network analysis , focusing on network structure , community detection, influence modeling, and recommendation systems. We begin by discussing fundamental concepts such as centrality and network metrics, which help identify influential nodes and measure their impact. Next, we delve into algorithms for detecting communities within networks, enabling a deeper understanding of group dynamics and structures. Additionally, we examine how graphtheoretic approaches model the spread of information, behaviors, and trends, offering insights into viral phenomena and marketing strategies. Finally, the paper addresses the use of graph theory in enhancing recommendation systems through personalized content and friend suggestions. By integrating theoretical concepts with practical applications, this study demonstrates the value of graph theory in analyzing and optimizing social networks, ultimately contributing to more effective and insightful network-based solutions.