Study On Propagation And Refutation Of Rumours In Complex Networks

Rumors are basic elements of human interaction. They could be gossips, advanced propaganda, and innovative ideas. Rumors can cause panic in a society during wars and epidemics outbreaks,and may also cause damage to personal reputations. Understanding how rumors spread may help us to predict and reduce the harm caused by rumor spreading.In order to simulate the complicated social relationship, scientists begin to study the graph theory while Erdos and Renyi established stochastic networks in 1960s. In 1998, Watts and Strogatz proposed small world networks based on randomized reconnection. In addition, Barabasi and Albert established BA scale-free network model in 1999. New network models are becoming more close to the practical networks. In this paper,we investigate the rumor spreading in mentioned networks based on previous network theory.We study the rumor spreading based on the evolution of SIR model,which is the classical epidemic spreading model on complex networks.From 1940s, scientists paid more attention on the differential form of epidemic models. SI, SIS, SIR model were put forward gradually to simulate different types of diseases, which still are very important academic models by now.In the typical spreading models, the mean-field equations for the dynamics of rumor spreading is a common way to use. But it is not very accurate to simulate the real process. So the complex network is introduced in rumor spreading, which form the studying basis of the spreading model.In this paper, we introduce the basic knowledge of complex networks and several classic network models in the first chapter. In chapter 2, the three basic differential equations of epidemic spreading model and the rumor spreading model are introduced. By incorporating the concept of degree distribution in complex network to traditional spreading model,rumor spreading model is built on the complex network eventually.In chapter 3, we put forward a new kind of rumor spreading model where stiflers will be replaced by refuters, and spreaders can be converted to the refuters by refuters. Considering the difference between rumor spreading and rumor refuting, we build a two-layer network to simulate the process. We find that spreaders couldn't disappear totally as the traditional model in end. Because of the special network structure, some spreaders would eventually survive. We find that the probability of agent i survive with state spreader is negatively correlated with the sum of degrees in the rumor spreading layer of the agent i's nearest neighbors in the refuting layer.In chapter 4, we summarize the development of rumor spreading model and some important conclusion of our model. Furthermore, we analyze the advantages and disadvantages of our model, and we forecast the development direction of rumor spreading model in the future finally.