The analysis of an SEI2R information propagation model based on Pareto's law in social networks

Abstract

In social network, the information propagation process is very similar to the infectious process of an epidemic. Considering the infected nodes hold different levels of transmission capacity, this paper classifies the infected nodes into two categories: the high influential infected nodes and ordinary influential infected nodes whose percentages are respectively 20% and 80% based on Pareto's law. This paper further proposes a novel information propagation model, named SEI2R model, and obtains disease-free and local disease equilibrium points and their globally asymptotical stabilities. Finally, the simulation experiments on a company's mailbox network verify the stability and correctness of the model in the process of information propagation and analyze the influence of the different parameters on the model.