Estimating user influence in social networks under independent cascade model

Abstract The rapid increase in social applications emphasizes the importance of estimating user influence. Heuristic methods like degree and betweenness centralities usually differ from the actual propagation process and yield unsatisfactory results. Traditional methods like Monte Carlo simulation are time-consuming. We modify the duplicate forwarding model to analyze the propagation process, which is proved to be close to the independent cascade model. We calculate the influence of a given source on a target. This approach allows for relatively accurate user influence estimation. Although this method is more efficient than traditional methods, it still requires traversing all users. Therefore, we introduce a virtual user who is connected to all users. By estimating the influence of any user on the virtual user, we can approximate the user influence efficiently. Experiments on real-world networks demonstrate that our method achieves not only better accuracy in user influence ranking but also lower computational cost.