Influences Of Information Dissemination From Topology Structure On Social Network Based On Improved SEIR Model

With the development and popularization of the Internet and mobile devices,convenient social networks have built a bridge of information sharing and network communication among the crowd,providing people with contactless social communication greatly.Social network platform has attracted users from all walks of life by virtue of its characteristics of mobile socialization and information update in time.Embracing the rapid development of social network services,the social effects are emerging constantly.how to improve the effectiveness of positive information dissemination,or curb the spread of negative information has become research hotspots.Based on the improved model of information transmission of social networks,this paper studies the influence of different centrality indexes in the topology of social networks on information transmission.Firstly,complex network technology is used to analyze the topology structure of social networks.User relationship data from two social network platforms Advogato and Anybeat were selected to preprocess the data and build a visual network respectively.Label Propagation Algorithm(LPA)algorithm and greedy algorithm are used for community detection of social networks after that,and the overall topological characteristics of the above two networks are analyzed from the overall structure of the network and node centrality index,so as to further understand the degree of closeness among users.From the point of view for node centrality,distribution of central metrics is studied in different networks,and Spearman correlation coefficient is used to measure the correlation between indicators.Furthermore,adopting the immunization rate of exposed persons on the traditional infectious disease model,an improved SEIR model based on social network was proposed,which describes the information transmission model of social network.The influence of initial transmission nodes is studied respectively on the speed and scope of information transmission over the SEIR and the improved SEIR models.Finally,considering the differences in user attributes over different social platforms,this paper attempts to simulate the information transmission process by changing the immunization rate of exposed persons in the model for the different networks and compares the changing trend of population density in the four states.The research are shown as the following.firstly,the degree distribution of the nodes for the two networks selected in this paper obey the power rate distribution,and the difference of betweenness centrality and the eigenvector centrality is more significant among different nodes.There is a strong correlation between the centrality indicators.The correlation between degree centrality and betweenness centrality,and between closeness centrality and eigenvector centrality is stronger.At the same time,the correlation between each centrality index varies with the specific network.Secondly,users represented by nodes with high centrality and feature vector centrality show strong guidance ability in information release,information transmission and public opinion guidance,playing a key role in network communication.Thirdly,the improved SEIR model can be more consistent with the information transmission process of the current network social platforms.It is demonstrated that increasing of the user immunization rate appropriately can control the scope of information transmission effectively,but it will lead to longer duration of information in the network.