Research On Issues Of Information Diffusion Based On Game Theory

Online social network analysis,a new interdisciplinary subject,has attracted many researchers' attention since its birth.The research of information dissemination in online social networks is an intersecting field of online social network analysis and complex network research.As the study on information propagation possesses great application value,it has increasingly become one of the focuses of current research.From the perspective of information propagation dynamics and based on evolutionary games,in this thesis,the impacts of different dynamic factors on information diffusion in complex social networks is studied.The principal contributions of this thesis can be summarized as follows:1.In this thesis,user multidimensional attributes and evolutionary games are combined with the traditional susceptible-infected-recovered(SIR)epidemic model,which is used to quantify the impact of external and internal driving factors on group state transitions during hotspot propagation.First,the network structure attributes and historical attributes of social users are extracted,and user multidimensional attribute driven mechanism is constructed by multivariate linear regression.This is used to analyze the internal driving factors that affect users participating in hotspots.Second,taking into account multi-source information dissemination and the complexity of user interaction behaviors,the concept,perceived popularity,is defined,and the dynamic evolution strategy of user behaviors based on evolutionary game theory is proposed.This reveals that external driving factors affect the behaviors of social network users.Finally,considering the external and internal driving factors that affect hot topic information dissemination,we obtain the hotspot propagation model based on user multidimensional attributes and evolutionary games,combined with the traditional SIR epidemic model.Experiments show that the model can effectively reveal the impact of different driving factors on information dissemination,and depict the trend of information dissemination in social networks.2.Taking into account the real topological relations among the participants and the psychological characteristics of the users,in this thesis,a hotspot propagation model based on heterogeneous mean field and evolutionary games is proposed.First,in real social networks,the changes of hotspot's trend could lead to the dynamic changes of users' willingness to participate in the hot topic.This effect is reflected in the dynamic behaviors among the users.In this work,based on the evolutionary games,a dynamic evolution mechanism for users' willingness to participate in hotspot is constructed and dynamically adjusts the infection rate of information dissemination model.Second,in view of the heterogeneity of the real network structure and the complexity of the heterogeneous mean field,graphical evolutionary game is introduced to improve the heterogeneous mean field.Thus,a new dynamics model of information dissemination is constructed based on graphical evolutionary game.Finally,considering the dynamic behavior among the nodes and the heterogeneity of real social networks,we obtain a hotspot propagation model based on dynamic evolution mechanism and improved heterogeneous mean field.To verify the proposed model,we perform simulations over synthetic networks and real network.Experiments show that the model effectively reveal the impact of different driving factors on information dissemination,and predict the trend of information dissemination in social networks.