INVESTIGATION OF GRAPH THEORY FOR ANALYSIS OF SOCIAL NETWORKS

In recent years, graph theory has attracted a lot of attention for its use in the study of social networks. It has become an effective tool for deconstructing complicated systems. Social networks are complex webs of connected people or things, where interactions and relationships are very important in determining how people behave, how information spreads, and how the network as a whole functions. This study examines the applicability of graph theory to the study of social networks and offers insights into the basic theories and methods used in this area. We offer several graph metrics to quantify key aspects of social networks, such as node importance, information flow, and network cohesion. Examples include degree centrality, between ness centrality, and clustering coefficient. The section that follows examines several graph theory-based algorithms and techniques that help us comprehend social network dynamics. In order to reveal interaction patterns and community structures, network community identification techniques locate clusters or groups inside a network. In order to foresee relationships and the spread of information in social networks, link prediction techniques make use of graph features to predict future connections between nodes.