Links prediction based on hidden naive bayes model

被引：0
|
作者：
Huang H. [1 ,2 ]
Wei Q. [1 ]
Hu M. [1 ]
Feng Y. [1 ]
机构：
[1] School of Communication and Info. Eng., Chongqing Univ. of Posts and Telecommunications, Chongqing
[2] College of Computer Sci., Chongqing Univ., Chongqing
来源：
Sichuan Daxue Xuebao (Gongcheng Kexue Ban)/Journal of Sichuan University (Engineering Science Edition) | 2016年 / 48卷 / 04期
关键词：
Conditional mutual information; Hidden naive Bayes; Link prediction; Similarity;
D O I：
10.15961/j.jsuese.2016.04.021
中图分类号：
学科分类号：
摘要：
In order to solve the problem that the existing link prediction models based on local information between nodes consider the dependent relationships between common neighbor nodes insufficiently and fail to fully make use of the network topology information, the link prediction method based on hidden naive Bayes model was put forward. The algorithm fully considered the interdependence between common neighbor nodes and difference between interdependence. Then the similarities of nodes were computed through hidden naive Bayes classification model and the dependence between nodes were measured by utilizing the conditional mutual information. Through the above methods, the link prediction accuracy was finally improved. In the simulation, DBLP and Email data sets were used as the experimental data and the method of AUC and Precision were used to evaluate the forecasting models. Results showed that the predictive effect of proposed algorithm is better than that of the mainstream method which effectively verified the accuracy of the method. © 2016, Editorial Department of Journal of Sichuan University (Engineering Science Edition). All right reserved.
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页码：150 / 157
页数：7
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