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工程科学与技术:2016,48(4):150-157
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基于隐朴素贝叶斯模型的链路预测算法
(1.重庆邮电大学;2.重庆邮电大学13983132374邮箱1183566132@qq.com)
The Links Prediction Based on Hidden Naive Bayes Model
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投稿时间:2015-06-30    修订日期:2016-01-06
中文摘要: 针对目前基于共邻节点及其改进的链接预测模型中存在对共邻节点间的依赖关系考虑不足,不能完全利用网络的拓扑结构信息的问题,本文提出了基于隐朴素贝叶斯模型和双隐朴素贝叶斯模型的链接预测方法。算法考虑共邻节点间互相依赖关系及其依赖关系的不同,通过隐朴素贝叶斯分类模型计算节点之间的相似性,利用条件互信息来衡量节点间的依赖程度,提高链接预测的准确率。采用网络DBLP和Email的真实数据作为实验数据集,使用AUC和Precision方法来评价本文的预测模型,实验结果表明,本文方法比目前主流方法的预测效果更好,验证了方法的准确性。
Abstract:In order to solve the problem that the existing link prediction models based on local information between nodes considered the dependent relationships between common neighbor nodes insufficiently and failed to fully make use of the network topology information, meanwhile improve the accuracy of links prediction, this paper put forward the link prediction method based on hidden naive Bayes model. The algorithm fully considered the interdependence between common neighbor nodes and difference between interdependence. Then the similarity 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 show that the predictive effect of proposed algorithm is better than that of the mainstream method which effectively verified the accuracy of the method.
文章编号:201500638     中图分类号:    文献标志码:
基金项目:国家自然科学基金:吴大鹏,项目编号:61371097,项目名称:社会化泛在无线网络节点关系感知与可信协同机理研究。国家自然科学基金:张红升,项目编号:61401051,项目名称:基于DAB的自组织数字广播通信网关键技术研究与实现。
Author NameAffiliationE-mail
HuangHongCheng  huanghc@cqupt.edu.cn 
WeiQing   
HuMin   
FengYuBin   
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引用文本:
黄宏程,魏青,胡敏,冯榆斌.基于隐朴素贝叶斯模型的链路预测算法[J].工程科学与技术,2016,48(4):150-157.
HuangHongCheng,WeiQing,HuMin,FengYuBin.The Links Prediction Based on Hidden Naive Bayes Model[J].Advanced Engineering Sciences,2016,48(4):150-157.