Ranking Influential Nodes in Social Networks Based on the Game of Thieves Algorithm

With advancements in technology, the number of people using various social networks to share information daily has increased exponentially. To spread information to maximum users, there is a need to effectively identify highly influential nodes in social networks. Still, existing centrality measures and methods have drawbacks relating to computational complexity and accuracy. The proposed method considers the Game of Thieves algorithm to rank influential nodes in social networks effectively with lower computational time that outperforms existing closeness centrality, eigenvector, Game Theory, and VoteRank⁺⁺ algorithms. To evaluate the performance of the proposed work, experiments were conducted on synthetic and real-world network datasets. The results were evaluated with the help of the Susceptible-Infected-Recovered (SIR) model and Node Removal Procedure (NRP) evaluation metrics. These results showed that GOT outperforms the existing measures by 12% on average while giving more accurate results than the standard algorithms for large-scale networks.