Identifying Influential Users in Social Networks with an Extended Hybrid Global Structure Model

With the widespread use of social media, communication, and news dissemination have become faster and more extensive. Therefore, identifying key individuals in controlling rumors is of great importance. Researchers have employed various methods to identify these individuals, each with its advantages and disadvantages. Some methods lack accuracy and do not adequately identify the influence of each individual. Others have low resolution, meaning they consider many individuals to be equally influential and do not differentiate between them. In this paper, we propose a comprehensive method based on the Degree and K-shell, which improves upon the Hybrid global structure model (HGSM) method. According to the evaluations performed on five social networks, the proposed method has higher accuracy and resolution compared to recent methods, while also having a suitable time complexity. The real influence of each individual is calculated using the Susceptible-Infected-Recovered (SIR) model and the evaluation criteria are Kendall’s Tau, Monotonicity index, and Time complexity.