Najva Hafizi - Department of Algorithms and Computation, Faculty of Engineering Science, College of Engineering, University of Tehran, Tehran, Iran.
Mojtaba Mazoochi - ICT Research Institute (ITRC), Tehran, Iran.
Ali moeini - Department of Algorithms and Computation, Faculty of Engineering Science, College of Engineering, University of Tehran, Tehran, Iran.
Leila Rabiei - Innovation and Development Center of Artificial Intelligence, ICT Research Institute (ITRC), Tehran, Iran.
Seyed Mohammadreza Ghaffariannia - Department of Algorithms and Computation, Faculty of Engineering Science, College of Engineering, University of Tehran, Tehran, Iran.
Farzaneh Rahmani - Innovation and Development Center of Artificial Intelligence, ICT Research Institute (ITRC), Tehran, Iran.

Online social networks (OSNs) such as Facebook, Twitter, Instagram, etc. have attracted many users all around the world. Based on the centrality concept, many methods are proposed in order to find influential users in an online social network. However, the performance of these methods is not always acceptable. In this paper, we proposed a new improvement on centrality measures called P-centrality measure in which the effects of node predecessors are considered. In an extended measure called EP-centrality, the effect of the preceding predecessors of node predecessors are also considered. We also defined a combination of two centrality measures called NodePower (NP) to improve the effectiveness of the proposed metrics. The performance of utilizing our proposed centrality metrics in comparison with the conventional centrality measures is evaluated by Susceptible-Infected-Recovered (SIR) model. The results show that the proposed metrics display better performance finding influential users than normal ones due to Kendall’s τ coefficient metric.

online social networks, Centrality measures, Influential users, Susceptible-Infected-Recovered model