Identifying influential nodes: A new method based on dynamic propagation probability model

Identifying influential nodes in complex networks persists as a crucial issue due to its practical applications in the real world. The propagation model is a special method for identifying influential nodes based on propagation dynamics. However, most of propagation-based methods have not delved deeply into the impact of network topology on the propagation process. In this paper, we propose a method based on the dynamic propagation probability model, called DPP. The main idea of this method is to characterize the impact of a node on the basis of its propagation capacity during propagation process by using dynamic propagation probability within its three level neighborhood. This new metric redefines the propagation probability of neighbors by refining the propagation process, which allows the propagation probability to be transmitted in accordance with the network structure. To validate the performance of the proposed method, we compare with eight different methods from four aspects in 11 real-world networks. The experimental results demonstrate that the DPP method has good performance in most cases.