Research On The Impact Of Community Structure To Spreading Models On Complex Networks

The issue of communication on complex networks has always been a hot topic.With the further development of theoretical research,all kinds of propagation can be further explored by relying on the complex network model,such as SARS,influenza and other infectious diseases in the crowd;the spread of all kinds of computer viruses in the Internet;the proliferation of rumors and false news in social networks.In order to deal with the problems and capture the opportunities about the communication,people constantly put forward various models to simulate and predict the propagation behavior.Many models,such as SIS and SIR,which originated from the spread of diseases,are also used to simulate information dissemination or rumor control.Many of these models use the nodes and edges in the topological structure of complex networks as the carrier of propagation behavior.The model of epidemic spreading with information dissemination is the focus of this study.There are great differences between these two spreading models,such as propagation speed,reaction timeliness and life cycle.But in the models based on complex networks,these two spreading issues use link propagation to update the state of nodes dynamically.More importantly,in real life,these two kinds of communication usually occur at the same time and interact with each other.This thesis focuses on studying the interactions between epidemic spreading and information dissemination,and exploring the influence of the network community to the spreading process.The main work are summarized as follows:?1?Because of the close relationship between epidemic spreading and information dissemination,we propose a double-layer complex network structure to serve as the carrier of propagation.At the same time,we propose model SIR-A to simulate the relationship of the spreading process and the contact among two layers of networks.The epidemic layer uses the SIR model and information dissemination uses the awareness model.In the SIR-A model,we consider the influence of community structure on information dissemination.The individual alertness is also updated along with communication and disperse with time.Moreover,considering the defeat of using the infection ratio i?t?as the evaluation standard of the disease transmission process,we add the source location information to i?t?and propose a new index propagation risk R_sp.Simulation experiments on different network data confirm that our model can well integrate the above factors.?2?The community size of network affects the sensitivity of the individuals to information and the responses to diseases.We propose a multi-objective optimization algorithm MA_RC-CD,which combines the maximum complete sub-graph discovery algorithm from graph theory and the memetic algorithm to detect the community structure of the network.The idea of this algorithm is:first we use RC algorithm to pre-divide the nodes,then we use the short-code memetic algorithm to further optimize.Experimental results on different network data show that the MA_RC-CD can achieve satisfactory results.?3?With the rapid dissemination of information and short reaction cycle,people can use the low-cost information dissemination behavior to control the spread of disease.On the basis of SIR-A model,we propose four kinds of information dissemination strategies:random dissemination,target dissemination,dissemination strategy based on path dissemination and dissemination strategy based on location information of infection source.These strategies disseminate active information by selecting a certain number of nodes in the information layer as information sources,thereby minimizing the peak value of epidemic spreading risk R_sp and delaying the arrival of peak periods.Experimental results show that these four strategies can achieve the desired goal.The memetic algorithm is used to optimize the target selection criteria function.The strategy based on the location information of the infection source perform better.