Identifying influential individuals in social networks: An example of a location-based online social network

The landscape of information access has evolved significantly over time, with the advent of search engines, social media platforms, and the widespread use of the internet. These developments have fostered a global communication network, resulting in intricate connections between individuals. Online social networks have emerged as key facilitators of social interaction, expediting the exchange of information and playing a pivotal role in content dissemination. Within these networks, certain individuals, termed as Key Players, wield considerable influence, profoundly impacting information diffusion. Thus, the identification of the most influential individuals within complex network structures stands as a crucial challenge. In this study, we employ modularity and eigenvector centrality metrics to designate nodes for initial activation, aiming at influence maximization in social networks. Visualization and analysis of the dataset are conducted using Gephi software, providing insights into the dynamics of the social network structure and facilitating the identification of key players.