Research On Mobile Social Network Mining Algorithm And Its Statistical Properties

In recent years,due to the popularity of mobile devices,mobile social network has become a popular topic.Mobile social network is relatively dense area of mobile Internet.There is a close connection between the internal entities,and less connection between the mobile social networks.Therefore,this studies have an important academic value on mobile social network technique and application value on mobile social network marketing.Mobile call and message data are the most common information during the period of mobile internet era.To deeply understand the behavior mode of the mobile phone network,mobile call and message records from over 1 million individuals about 2.64 million samples are analyzed by using statistical methods.The sample data contains some variables,such as user ID,opposite user ID,call and message time and call duration.Using real call and message detail records,this thesis studies community discovery algorithm and statistical properties of mobile social network.The main research achievement and innovations in this thesis are listed below:The research on mobile social network discovery and key user discriminant techniques not only promote the development of communication community theory,but also improve the methodology of social network and social computing.This thesis proposed mobile social network discovery modelling based on Potts Spin-glass(PSG)technique,and Hamiltonian module evaluation function is powerful for classify mobile social network.Moreover,key user discriminant modelling is proposed based on Jaccards coefficient,and the proposed modelling is an effective method for evaluating the relatedness between users.Finally,the statistical analysis revealed that the 80/20 rule is also exited in the mobile social network,such as more than 90% communications including SMS and calls are from about 10% of the mobile users.In this thesis,the eight variables are extracted from sample data set,and the mobile social network statistical properties is categorized into four groups.Furthermore,a mobile social network statistical properties predication modelling is proposed based on discriminant coordinates analysis.In the approach,the high dimension data is reduced to two dimensions,and a general predication steps of mobile social network statistical properties is described.An empirical research shows that the proposed modelling is feasible and effective,and the modelling has a forecast accuracy of 95%.