Modeling And Analysis Of The Spreading Dynamics In Two-Layer Multiplex Networks

Spreading behavior is a common phenomenon,and the spreading of epi-demic diseases and rumors all affect human health and social development.Studying the mechanisms behind the spreading can bring great significance to the prevention and control of epidemic diseases,as well as the promotion or suppression of the informat,ion diffusion.However,as many propagation be-haviors are affected by the interaction between layers,it is difficult to describe such a complexity by a single network.Therefore,t,his dissertation studies the propagation process on two different models based on the two-layer multiplex networks.Firstly,we consider a novel susceptible-infect,ed-susceptible-type propa-gation process model on multiplex networks,where we assume that the pre-requisite of one susceptible node to be infected is that there is at least one infectious neighbor in each layer.Using stochastic simulations and mean-field theory methods,investigated the effect of overlap among layers on the spread-ing dynamics.Through our research,we find that when all the edges are completely overlapped on the two-layer networks,it undergoes a continuous phase transition from an healthy phase to an endemic spread phase.But when the edges are not completely overlapped,a discontinuous phase transition is observed.Using high and low two different initial densities of infected individ-uals,the model shows two distinct transition pathways,and a bistable region is formed.As the degree of overlapping edges decreases,the bistable region is enlarged.In order to further study the interaction of spreading dynamics between different layers,such as the interplay of information diffusion and epidemic spreading which take place on different layers,so we also considered the in-teraction model where the one layer satisfies the dynamics of unaware-aware-unaware processes and the other layer satisfies the dynamics of susceptible-asymptomatic-infected-susceptible spreading processes.Combing Monte Car-lo simulations with Markov chain theory methods,we study the effects of the asymptomatic state and the information transmission rate on the spreading of awareness and epidemic.The study find that reducing the asymptomatic pe-riod or increasing the transmission rate of information diffusion can accelerate the diffusion of information,leading to the low risk of epidemic.