Release Time:2019-03-11  Hits:

Indexed by:Conference Paper

Date of Publication:2014-08-24

Included Journals:Scopus、CPCI-S、EI

Page Number:361-366

Key Words:iDBMM; Dynamic Network Models; Dynamic Roles; Social Network Analysis

Summary:In the dynamic social network, how to use data mining tools to find the hidden dynamic knowledge in the social network has become the focus of the study. It can be applied to a wide range of areas with good practical value and application significance. We propose a novel algorithm called iDBMM based on the improvement of DBMM algorithm. At first, iDBMM algorithm classifies the training set to obtain the basic characteristics of each role. Then it scores the test set relative to each role and distribute the role of the highest score to the corresponding node. Finally, the transition model is obtained by the statistical method. Experimental results show that new method determines the distribution of the roles of the nodes effectively to make up for the shortcoming of non-negative matrix factorization and improve the prediction accuracy.