Complementary Dynamics of Global and Local Centrality for Influence Maximization in Social Networks

With the growing interest and ease of communication, the number of people using various social networks to disseminate information daily has increased exponentially. Social networking platforms have experienced exponential growth in recent years. To maximize the reach of information, there is a need to effectively identify influential nodes in social networks. Still, existing centrality measures and other methods have drawbacks relating to generalizing across networks and providing consistently accurate identification. In this study, a novel method is proposed by using the Global Local Influence (GLI) approach in particular combining closeness centrality and eigenvector centrality respectively. This aims to get more accurate identification of influential nodes across various networks. To evaluate the performance of the proposed work, experiments were conducted on synthetic and real-world datasets. The results were evaluated with the help of the diffusion models, namely Susceptible-Infected-Recovered (SIR) model and Node Removal Procedure (NRP). The results showed that the proposed algorithm outperforms the existing centrality measures by over 9% across the datasets supported by both the evaluation metrics used.