Social Network Information Dissemination Models And Control Methods Based On Pareto Principle And Node Status

With the development of Internet technology and the rise of online social platforms such as Sina Weibo,Facebook,Zhihu,etc.,more and more people are posting messages on social platforms,expressing opinions,sharing life,and participating in various hot topics in real time.Accept all kinds of real-time news.When hot news appeared on the Internet,people on social platforms gradually participated,forwarded comments and shared,and a small group of people participated in a large number of people,and one online community spread to multiple online communities,thus generating information spread.Information has two sides.When negative information erupts,it may have a huge negative impact if it is not controlled.How to control the development trend of negative information is a hot topic of current research.The outbreak of online information and the outbreak of epidemics are very similar in their wide spreading range,fast spreading speed,and strong destructive power.Therefore,this article will apply the infectious disease model to the field of information dissemination,but the traditional infectious disease model is not suitable for the real network environment,so the traditional infectious disease model needs to be improved.There are a huge number of online users on social platforms,and there are large and small communities in the network,but only a small number of users can influence the trend of information dissemination.High-influence users,structural hole users,users who have a special "status" in the network structure,have an important influence on the process of information dissemination.How to identify high-influence users in different communities in the network,structural holes users,and propose effective control strategies to make the research goals of this article.Therefore,this article can be divided into the following research contents:(1)Propose the SEI2 R information dissemination model and its control strategy based on the Pareto principle.(1)According to the special status of nodes in the network,the communication nodes in the process of network information dissemination are divided into two types: high-influence nodes and ordinary nodes,and it is assumed that the proportion of these two types of nodes in the network conforms to the Pareto principle.(2)Construct the SEI2 R information dissemination model,and use the Lyapunov equilibrium analysis method to prove the local stability and global stability of the proposed system.(3)Quantify the influencing factors in the process of information dissemination: user characteristics,information timeliness,social strengthening effect and other characteristics,and analyze the parameters of the impact of different influencing factors on the SEI2 R model,and propose corresponding control strategies to reduce the scale of information dissemination.Controlling network information dissemination is of great significance.(2)Propose SEI3 R information dissemination model and control algorithm based on node status.(1)Network nodes have a community phenomenon in the network,and structural hole nodes are also a kind of nodes that have an important influence on the process of information dissemination.(2)The communication state nodes in the process of network information dissemination are divided into high-impact nodes,ordinary nodes and structural hole nodes.According to the community discovery algorithm,structural hole discovery algorithm,and high-impact discovery algorithm,the SEI3 R model is proposed.(3)Three control strategies are proposed for the three types of nodes,and different control strategies are implemented for different control purposes.Use the real mailbox network and Facebook network to prove the effectiveness of the proposed control strategy.Based on the Pareto principle,node status,network topology structure characteristics,user characteristics,social reinforcement characteristics,and information timeliness,the thesis proposes SEI2 R propagation model and SEI3 R propagation model,and proposes corresponding control strategies.Finally,simulation experiments are carried out on two different scale data sets.The experimental results prove that the proposed control strategy has good results.