Research On Methods Of Community Detection And Prediction In Microblog

In the past few decades,Internet as a representative of network information technology has been rapid development.The human society has entered a complex network era.As a complex network Microblog has greatly changed the way of human communication and getting information.The humans are enjoying the convenience of the network.At the same time there is the challenge in the safety and reliability of the network.With the increase in the number of using microblog,more and more security issues need to solve.In addition,the large scale,complex connection,multi-content and dynamic change are the characteristics of the microblog network.Because of the existence of these characteristics,the traditional graph-based community detection algorithms are not fully accepted.So the related issues of community detection have been studied in many fields.In order to detect community in microblog,the user behavior characteristics and information dissemination characteristics must be mainly studied.Combining microblog characteristics with the traditional community detection algorithm propose a new community detection method.The results will be more accurate,and the potential communities will be detected.Because network structure and user behavior of microblog will change with time,the community structure will be changed.Therefore,it has important significance and value to analyze and predict the dynamic evolution process of microblog community.Overall,how to detect communities and predict the dynamic evolution process of the community are the research focus in this paper.Specifically,the main researches and contributions of this paper are as follows:1.In this paper,we study the propagation law of microblog information,and propose the microblog information dissemination model based on propagation dynamics.The model improves the SEIR dynamics model in the complex network and redefines the node state.It not only achieve the description of information dissemination,but also the quantitative analysis of spreading stability.Compared with the real network,the experiment shows that the propagation rules of the model are the same as the real situation.2.In this paper,we study the retweeting behavior of microblog users,and propose a retweeting game model.The model improves the inheritance model in sequential game.Using the Markov perfect equilibrium strategy(MPE)estimates the probability of retweeting.It not only achieve quantitative analysis,but also find out the key factors that affect the retweeting behavior.Experiments show that the model can describe the retweeting behavior and verity the authenticity of the model.3.In this paper,we study the structure and characteristics of the community,and propose a community detection method based on activity characteristics.The method uses the large likelihood estimation to extract the retweeting active characteristic set.Then we use the combination of the active characteristic set and Link Optimization Community(LOC)algorithm to solve the problem of unreasonable division.The method improves the accuracy and rationality of the community detection.Therefore,using the proposed method could accurately find the community structure and detect the hidden communities.The accuracy and rationality of the method are verified by experiments on standard test network and real microblog network.4.In this paper,we study the influence of the cooperative behavior among the users in microblog community and propose a dynamic evolution model based on the cooperative behavior.This method constructs a community evolution model based on cooperative behavior.The connection in the community is changed by the influence of user cooperation,which cause the community structure change at every moment.In each evolution process,the user will adjust the strategy.The user builds user relationship and strategy adjustment rules by analyzing the social dilemma income matrix.The user calculates the next moment of user relations and strategies to predict the community evolution.The reliability of the method is verified by simulation experiments.Finally,on the basis of the paper the direction of future research is further discussed.