Spreading Dynamics On Co-evolutionary Network And Its Control

Complex networks research focuses on the macro phenomenon resulting from the micro interactions among agents,especially various dynamics on complex networks.Epidemic spreading research is one of these hotspots.It aims at applying theoretical results to the modelling,simulation and prediction of real epidemic cases and to the further designing of control strategy and group incentive mechanism.Traditional epidemic spreading research is mainly about the influence of static network structure on epidemics.Although some realistic phenomena can be well explained,current researches consider too few factors and is too idealized.In order to reproduce real epidemic spreading process,it is necessary to integrate more real factors into the modelling of epidemic spreading.Researches of real epidemics,like AIDS and Ebola,show that behavioural responses can greatly affect epidemics.Behavioural response is that agents change their behaviours according to epidemic state,and these changes could alter link relationship of agents to some extent.Link structure changes in turn affect epidemic spreading.This makes the epidemics occur on co-evolutionary networks,but not on static networks.As current modelling is oversimplified,this dissertation focuses its study on epidemic spreading on co-evolutionary networks and its control,which mainly includes three parts as follows.(1)We study epidemic spreading and its control on homogeneous co-evolutionary network where all agents are identical.As present studies concentrate on properties of steady state and neglect transient characteristics,we first analyse transient spreading process on homogeneous co-evolutionary network and find the emergence of community structure in transient process.Then,we separately put forward community-based immunization and quarantine strategies and find that both strategies are more efficient than the corresponding random control strategies.What's more,results show that the timing of epidemic control is of great importance,and the critical time is the formation time of strong community structure.These results deepen our understanding of epidemic spreading,and give us a new perspective in epidemic prediction and control.(2)We study epidemic spreading and its control on heterogeneous co-evolutionary network where agents are of different intrinsic attributes.Considering that differences exist in the susceptibility of agents,we put forward a heterogeneous co-evolutionary epidemic spreading model.First,we study epidemic threshold and structural properties in steady state.Results show that heterogeneity of susceptibility can increase epidemic threshold,and in the process of adapting to epidemics,network gradually self-organizes into a topology which is with heterogeneous connectivity but more robust.Then,we analyse the phase transition point of the threshold-increasing phenomenon and find that threshold-increasing is derived from a different kind of spreading behaviour in the model,which does not exist in homogeneous co-evolutionary network.Further analysis shows that this spreading behaviour is in a region bordered by a local transcritical bifurcation point and a global heteroclinic bifurcation point.Finally,we continue to study the formation process and topological feature of the robust topology by transient process analysis.We find that network resists epidemic spreading by self-organizing into core-periphery structure.According to this,we propose a preferential rewiring strategy.This strategy can inhibit epidemic spreading more effectively by promoting the formation of coreperiphery structure,which verifies our transient analysis results.These results embody the importance of research on the combined effects of multiple factors and provide a reference for the designing of control measures.(3)We study the information-epidemic spreading on coupled network.Behavioural response depends on the obtained information,but the network of information spreading is absolutely different from that of epidemic spreading.Given this,we investigate the interplay between information and epidemic spreading on coupled network.On the one hand,we study the information-epidemic spreading in static coupled network,where agents who get information take vaccination with certain probability.We find that epidemic spreading can trigger the spreading of information,and information can promote the adoption of vaccination and inhibit epidemic spreading.On the other hand,as real network is of time-varying feature,we continue to study their interplay on time-varying coupled network,where agents with information take measures to avoid being infected.Similar interaction mode as in static coupled network is observed.Network properties have great influence on epidemic spreading and the inhibiting effect of information on epidemics.From the above results,adaptive change strategy of agent activity is proposed to inhibit epidemic spreading.These results provide a basis for stimulating people to take self-protective measures by making effective use of information.