Research On Positive And Negative Information Dissemination Model And Regulation Method In Mobile Social Network

With the increasing development of wireless communication technologies and intelligent terminal devices,mobile social networks(MSNs)bring great convenience for the information spread and sharing.MSNs have broad application prospects in many crucial fields such as mobile social networking,knowledge sharing,vehicle networks and Internet of things(IOTs).Due to the intermittent links between terminal devices,the dynamic variability of network topology,the sociality and uncertainty of nodes and other factors,how to design an efficient and reliable information transmission method has become a challenging issue in MSNs research.Furthermore,while people enjoy the convenient and fast services brought by MSNs,the autonomy and openness of the MSNs enable network users to freely spread both the positive information beneficial to users and the negative information unfavorable to users,such as rumors,public opinions and anti-government speeches.The spread of the negative information has caused serious public panic and information misleading.Besides,owing to the influence of user's social relationships,emotions and other factors,the simultaneous spread of the positive and negative information in MSNs exhibits a complex dynamic coupling process.Therefore,how to describe this dynamic spread process and control the influence caused by the negative information spread effectively has become an urgent issue to be solved in MSNs basic research.Based on the above background,this work studies the reliable information transmission method based on node's location,the modeling and the optimal control of the positive and negative information spread process.The specific contents are as follows:(1)Aiming at the problem that node's location information in MSNs affects the transmission efficiency,this work proposes an efficient locationaware information transmission method based on Markov process.Specifically,this work proposes a location-aware prediction model.The core of this model is to establish a second-order Markov transition probability matrix and then predict the next location of the node according to the historical information.In addition,a decision-making scheme of information forwarding is devised to select desired relay nodes according to node's delivery probability.Furthermore,in order to ensure that important information is received and forwarded preferentially,this work introduces a prioritybased buffer management strategy.Compared to the existing works,this work verifies the effectiveness of the prediction model and information transmission method.(2)Aiming at the problem that the simultaneous spread of the positive and negative information exhibits complex evolution process,this work divides the states presented by different network users into four types:susceptible state,positive information-spreading state,negative informationspreading state and dual information-chaotic state.Then,according to user's transfer relationships among four states,a positive and negative information spread model is established based on the differential equations.Furthermore,this work utilizes the theory of differential equations to obtain the critical condition for the continuous spread or disappearance of the negative information in the network,that is,the zero equilibrium and non-zero equilibrium of the model.Besides,this work analyzes the existence of the zero and non-zero equilibrium and proves their stability.The experimental results demonstrate that the critical condition can reveal the dynamic evolution process of the positive and negative information spread in MSNs.(3)Aiming at the problem that the negative information spread brings serious impact to the network and users,this work proposes a collaborative control mechanism to adjust the spread process of the positive and negative information and inhibit the spread of the negative information.Specifically,this mechanism mainly implements a control strategy for users in negative information-spreading state and dual information-chaotic state respectively and simultaneously.Then,the minimization of the expenses in the process of implementing the control strategy is regarded as the optimization objective.Based on the optimal control theory,we obtain the dynamic distribution of two optimal control strategies over time and prove the existence and uniqueness of the optimal control strategies,thus providing the optimal control decision and theoretical basis for the regulation of the positive and negative information spread.Finally,this work verifies the efficiency of the collaborative control mechanism based on two real social networks.