Studies On Spreading Dynamics Modeling And Intervention In Complex Networks

Many spreading phenomena in the real world can be simplified and abstracted into the spreading process on complex networks,for instance,the information spreading on social networks,the computer virus transmission on computer networks,the disease spreading on contact networks,the crisis diffusion on financial networks and so on.The study of modeling and intervention of the spreading dynamics on complex networks can provide decision-making basis for rumor and disease prevention,product marketing and trend guidance,etc.,which has important practical significance.However,at present,there are still many difficulties and challenges in the study of spreading dynamics on complex networks.On the one hand,it is a very challenging problem to combine the information of network structure and spreading dynamics to model and intervene the network–based spreading process because of the diverse characteristics of network structure and the complex characteristics of spreading dynamics.On the other hand,considering that many spreading phenomena in reality do not exist independently,but interact and co-evolve with each other,it is also difficult to model and analyze these complex cooperative spreading phenomena.To address the above issues,this dissertation will start from the study of the spreading of single information,conducting the research of information spreading based on the dynamic state of edges.On this basis,the cooperative spreading dynamics on complex networks is further discussed,the research of information–information cooperative spreading based on the dynamic state of edges and the research on the individual–awareness–based resource–disease cooperative spreading dynamics are carried out.The specific contents and innovations of this dissertation can be summarized as follows:(1)The research of information spreading based on the dynamic state of edges.Information spreading is ubiquitous in human society.Modeling and effective intervention of some typical information spreading in the real world can promote the benign development of human society.However,at present,most of the theoretical analysis methods on information spreading in complex networks take nodes as the research object,and the theoretical research based on network edges is still relatively lacking.Moreover,most of the existing information spreading intervention strategies only rely on the structural characteristics of the network,and ignore the characteristic information of the spreading dynamics itself.To solve the above problems,this paper takes the network edge connection as the analysis object,and develops a set of information spreading analysis methods based on the dynamic state of the edges,namely the edge state equation analysis method of the SIR model.This method can quickly and accurately predict the final state probability of nodes and edges,so as to accurately predict the final spreading range of information in the network.On this basis,this dissertation then proposes an index to measure the influence of potential edges in promoting information spreading dynamics.In the general parameter condition,this index can effectively identify the optimal potential edges which can promote the information spreading in the complex network.Finally,this dissertation proposes an intervention strategy for information spreading based on the optimal potential edge.This strategy combines the characteristics of network structure with the characteristics of spreading dynamics itself,and it can better intervene the information spreading than the previous heuristic strategies which only rely on the centrality indexes of network topology.The proposal of this strategy makes up for the shortcomings of previous research on intervention strategies of information spreading.This part of the study solves the problem of how to effectively combine the structural characteristics of the network topology with the characteristics of the spreading dynamics to intervene the spreading process on the complex networks.The theoretical achievement of this part of the study lays a certain foundation for the subsequent research on cooperative spreading dynamics.(2)The research of information–information cooperative spreading based on the dynamic state of edges.Many information spreading phenomena in the real network do not exist completely independently,but interact and co-evolve with each other.The process of information–information cooperative spreading is more complex and changeable than that of single information spreading.The theoretical research on information-information cooperative spreading is still scarce,and it is more challenging to analyze and intervene the information-information cooperative spreading by combining the characteristics of network structure and the characteristics of spreading dynamics itself.In view of this,this dissertation first expands the co-SIR model to study the information-information cooperative spreading dynamics.Then,based on the co-SIR model,an index is proposed to quantitatively describe the influence of potential edges on promoting the information–information cooperative dynamics in the case of dynamic symmetric spreading.Combined with the structural characteristics of the network and the characteristics of the spreading dynamics,this index can effectively identify the optimal potential edge that can promote the information–information spreading in the co–SIR dynamic system.Finally,this dissertation also proposes an intervention strategy based on the optimal potential edge for the information–information cooperative spreading dynamics.In the general parameter condition,this strategy can more effectively promote information–information cooperative spreading dynamics compared with the comparison strategy that only relies on the centrality indexes of network topology.This part of the study can help to understand the information–information cooperative spreading on real networks,and provide a reliable decision-making basis for the prediction and control of information spreading in practice.Meanwhile,the research approach and methods also provide inspirations for the subsequent research of cooperative dynamics between different types of spreading process.(3)The research on individual–awareness–based resource–disease cooperative spreading dynamics.The cooperative spreading of different types of spreading processes has more intricate evolutionary mechanisms,but can generate more abundant dynamic behaviors,compared with the cooperative spreading of the same type of spreading process.This dissertation studies a typical kind of cooperative spreading dynamics of different types of spreading processes,namely the individual–awareness–based resource–disease cooperative spreading dynamics.At present,the research on the resource–disease cooperative spreading has attracted the attention of many researchers,but few of them have considered the individual awareness into the co-evolution mechanism of the resource and disease dynamics.In view of this,this dissertation first proposes an individual–awareness–based resource–disease cooperative spreading model.The model includes two different types of dynamic processes: individual–awareness–based disease spreading and resource allocation.They interact and co-evolve with each other.Then,on the basis of this model,this dissertation systematically investigates the influence of individual awareness and network heterogeneity in the resource–disease cooperative spreading dynamics system.The results showed that the influence of basic individual awareness on the cooperative spreading dynamics can be divided into three stages: in stage I,the basic individual awareness is weak and can not effectively inhibit the spreading of disease;in stage II,the basic individual awareness is strong,and the outbreak size of the disease is significantly reduced;in stage III,the basic individual awareness is large enough that the disease does not erupt.In addition,this dissertation also found that the network heterogeneity plays the role of”double-edged sword” on the cooperative spreading dynamics.Specifically,when the basic transmission probability is large,or the basic individual awareness is small,the enhancement of network heterogeneity can inhibit the spreading of disease;on the contrary,it can promote the spreading of disease.This part of the study can provide reliable decisionmaking basis for resource allocation and epidemic prevention strategy formulation during the actual epidemic period.