Rumor Propagation And Source Detection Based On Extended Epidemic Model

With the rapid development of social networks,people can get information more quickly and widely.The Internet has brought great convenience to people's lives,but it also brings many security issues.Rumors that are one of fake information can mislead users of network and destroy the harmony of the Internet.therefore,it is important to study the propagation and source detection of rumor in order to control the risk which comes from rumorsThis dissertation studies the rumor propagation and source detection based on extended epidemic model.The main contributions are presented as follows:(1)On the basis of the classic infectious disease model SIR,we consider the ability of social networking platforms to isolate nodes that spread rumors and introduce a saturation function to describe the level of system detection capabilities,thereby establishing the SIOR model.According to the stability theory of differential equations,we prove that there is an equilibrium point in the spread of rumors and the local stability when the spreading process reaches equilibrium.During the experiment,we verify the stability of the spread of rumors through numerical simulations.Subsequently,we simulate the rumor propagation in a complex network and the results also shows that the propagation will eventually converge to a stable point.On the other hand,we consider the impact of rumor propagation under different parameter values of saturate function,the results shows that the detection and banning ability of system can control the convergence speed of rumor spreading and the peak of number of infected nodes when the model reaches a stable point.The above conclusions can provide theoretical guidance for suppressing the spread of rumors.(2)On the basis of the SIOR model,we study the problem of detecting the source of rumors from a single source.According to the state of the network node provided by the sample of a single network snapshot,we can infer the source of the rumor.Firstly,we obtain the source estimator through the optimal information propagation process and verify the estimated value that is similar to the Jordan Infection Center in the network topology based on the SIOR model.Then we propose a reverse infection propagation algorithm for the SIOR model,which can identify the Jordan infection center in the network topology.Finally we compare with other centrality detection algorithms through simulation experiments to verify the superiority of the estimator.