Examining the interaction between agents in a well-mixed population has been a prevalent area of research. Previous studies have emphasized the evolution of the impact of network architecture and payoff differences on agents? behavior in various games. There has been a recent surge in interest in incorporating popularity among researchers in this field. In this study, we employ a game theoretic approach to gain insight into the strategic behavior and decision-making processes of individuals in a network and how these decisions impact the diffusion of information when an individual?s popularity is considered. The Fermi function is used to model the probability of information diffusion and the spread of influence within the network. We introduce a simulation module that models the dynamic process of evolutionary game theory in both synthetic and real-world networks, leveraging the Fermi update rule as a critical component. Furthermore, the implementation is modified to analyze the influence of each individual by incorporating metrics that account for individual popularity and its effect on the overall network dynamics. This modification allows for a more granular understanding of how individual agents contribute to the propagation of information and the development of cooperative behaviors. The simulation results provide valuable insight into the evolution of cooperative behavior in complex networks and hold potential for further exploration into various aspects of evolutionary game theory.