Research On Co-evolution Spreading And Immunity Strategies Based On Complex Networks

The research on the spreading dynamics and immunization strategies based on complex networks has always been the focus of scholars from various fields and a hot issue of competitive research.In recent years,numerous scholars have pushed the modeling of epidemic transmission and the research on immunization strategies to a new height.In recent years,the research on network spreading dynamics and immunization strategies mainly focuses on the construction of spreading models and the research on relevant theoretical analysis methods,which are mainly highly abstract to the real spreading processes,and the impact of real social factors on the spreading processes is still lacking in systematic research.Therefore,with coevolutionary spreading on complex networks as the center,this thesis focuses on the impact of real network structure,individual consciousness and behavior,social resources and other factors on information and epidemic spreading and immunization,and study the spreading of information and epidemic and immunization strategies on complex networks in the following aspects:(1)Firstly,based on the complex types of connections in real social networks,this thesis studies the influence of multiple types of connections on information spreading.Therefore,this thesis constructs a two-layer network structure based on friendly and hostile relations,and simulates the real information spreading process to build an information competition spreading model based on the two relations.Then,an analytical framework based on edge division theory is constructed to analyze the range of information spreading and explosion threshold with two kinds of relations.Finally,the dynamic properties of information propagation on multi-relationship networks with different network structures are systematically studied by means of theoretical analysis and computer numerical simulation.The results show that the multi-relationship network has an inhibitory effect on information transmission.In addition,on a multi-relationship network,the information burst threshold also changes.Specifically,the threshold is only relevant to the network topology of the friend network.The extended edge division theory proposed in this thesis predicts the above phenomenon well.(2)Secondly,the collaborative transmission mechanism of multiple epidemics and the corresponding immunization strategies were studied in this thesis.Firstly,the effect of edge synergism on epidemic spreading was studied.Therefore,this thesis constructs a collaborative transmission model of two epidemics with edge synergy effect based on SIR Model,and establishes a mathematical analytic framework based on edge division theory,and obtains the expression of epidemic outbreak fronting point.Then,the spreading characteristics of different network structures are explored by means of numerical simulation and theoretical analysis.The research findings indicate that for ER networks,the type of phase transition in the co-infection scale primarily depends on the strength of the edge synergistic effect.Furthermore,by increasing the intensity of the edge synergistic effect,it was observed that the phase transition type transitions from continuous to discontinuous.However,for SF networks,the phase transition type remains consistently as a continuous phase transition.Drawing on the foundation of research on synergistic propagation dynamics,this article conducts a systematic study on the immunization issues of two synergistic viruses.Firstly,this thesis proposes an immunity rule based on preference rule,and adopts SIR Model to describe the transmission process of two kinds of co-transmitted epidemics.Then,based on the network percolation theory,a theoretical analysis method based on edge percolation was constructed,and the epidemic outbreak threshold and spread range under different immunization strategies were analyzed theoretically.Finally,numerical analysis and theoretical analysis were used to systematically analyze the effects of immunization strategies on epidemic transmission under different model parameters and network structures.The results showed that,compared with the nodes with low immunity,the nodes with high immunity could effectively inhibit the spread of the epidemic in the case of low transmission rate,while the opposite was true in the case of high transmission rate.Regarding the system’s immunization cost,the study reveals that immunizing hub nodes would increase the overall immunization cost of the system.(3)Based on the aforementioned research foundation,this article delves into the issues of epidemic propagation driven by information and the allocation of immunization resources.The study primarily focuses on the coupling of individual consciousness behavior,resource allocation,and the co-evolutionary process of epidemic propagation.Furthermore,it analyzes various immunization resource allocation strategies under different conditions.Specifically,the article first examines the characteristics of informationdriven resource-epidemic co-evolutionary propagation dynamics on social-contact duallayer networks,and proposes a coupled model of information-driven consciousness-resourcedisease propagation on such networks.Subsequently,a theoretical analysis of the coupled dynamic process is conducted using the micro-Markov method,forecasting and analyzing the epidemic spread range and outbreak threshold under different resource allocation methods.Finally,a systematic study is conducted on the impact of information-driven consciousness behavior and network structure on epidemic propagation through a combination of theoretical analysis and numerical simulations.Furthermore,this article explores the influence of different types of information on the coupled dynamics of resource-epidemic propagation on multilayer networks,employing a resource-based SIS model to model the coupled dynamic process.Extensive Monte Carlo simulations are then used to study the effects of network structure.The research presented in this article helps reveal the macroscopic and microscopic mechanisms of information and epidemic propagation under complex factors,enhancing the accuracy of predictions for the spread,evolutionary patterns,and outbreak thresholds of information and epidemics in complex social scenarios.The findings contribute to a deeper understanding of real-world information and epidemic propagation mechanisms,enabling targeted control measures.The theoretical analysis methods proposed in this article provide a valuable contribution to the study of propagation dynamics on complex networks.