Double-Layer Network Negative Public Opinion Information Propagation Modeling Based on Continuous-Time Markov Chain. (9th May 2020)

• Record Type: Journal Article
• Authors: Liu, Xiaoyang; Tang, Ting; He, Daobing
• Is Part Of: Computer journal. Volume 64:Number 9(2021)
• Page Start: 1315
• Page End: 1325
• Publication Date: 2020-05-09
• Subjects: double-layer network -- continuous-time Markov chain -- negative public opinion propagation -- basic reproductive number; Computers -- Periodicals; 005.1
• DOI: 10.1093/comjnl/bxaa038
• Languages: English
• ISSNs: 0010-4620

Abstract

Abstract: In view of the fact that the existing public opinion propagation aspects are mostly based on single-layer propagation network, these works rarely consider the double-layer network structure and the negative opinion evolution. This paper proposes a new susceptible-infected-vaccinated-susceptible negative opinion information propagation model with preventive vaccination by constructing double-layer network topology. Firstly, the continuous-time Markov chain is used to simulate the negative public opinion information propagation process and the nonlinear dynamic equation of the model is derived; secondly, the steady state condition of the virus propagation in the model is proposed and mathematically proved; finally, Monte Carlo method is applied in the proposed model. The parameters of simulation model have an effect on negative public opinion information propagation, the derivation results are verified by computer simulation. The simulation results show that the proposed model has a larger threshold of public opinion information propagation and has more effective control of the scale of negative public opinion; it also can reduce the density of negative public opinion information propagation and suppress negative public opinion information compared with the traditional susceptible infected susceptible model. It also can provide the scientific method and research approach based on probability statistics for the study of negative public opinion information propagation in complex networks.