Abstract: Existing network information propagation models that suppose all infected nodes are capable of infecting susceptible nodes,it cannot reflect objectively the fact that information propagation is time-efficient.For this problem,based on mean field theory,from the macro perspective of information dissemination,a novel dynamical model of network media information dissemination is proposed in this paper.According to real situation,the model assumes that only newly infected nodes will infect susceptible nodes in the social network,and there are two ways for susceptible nodes to become infected nodes,one is the spread of infection in social networks,the other is random browsing.Furthermore,based on graph theory,the states of nodes in the model and their transition relations are abstracted as weighted directed graphs.Based on Bayes' theorem,the state transitions between nodes are expressed as probability events,and the probability expression of event occurrence is given.Then the state transformation relationship matrix is determined.Finally,the model is solved using Gauss-Seidel iterative method.Numerical simulation results show that the dissemination of online media information is time-sensitive.Hot events will reach a peak of diffusion in a day,and then the range of diffusion will decline rapidly.Then the statistical data of the hot event from Baidu index is used to verify the validity of the model.The results show that the proposed model can reflect the spreading trend of network information more accurately than existing models.

Key words: Dynamic quarantine, Dynamical model, Network security, Network virus propagation