文章基本信息

标题：Uncertainty Community Detection in Social Networks
本地全文：下载
作者：Liu, Hongwei ; Chen, Li ; Zhu, Hui 等
期刊名称：Journal of Software
印刷版ISSN：1796-217X
出版年度：2014
卷号：9
期号：4
页码：1045-1049
DOI：10.4304/jsw.9.4.1045-1049
语种：English
出版社：Academy Publisher
摘要：Community detection in social networks has a great uncertainty and it has commercial value for business. Many of these communities are the target markets to business. Research found that some factors played important roles for social community mining, such as user profile, information of out-degree and in-degree. This paper takes full advantage of user information to model Hybrid Bayesian networks which has strong ability to deal with uncertain event. We begin mining data from users’ profile in social networks and process these data into binary logic data and discrete ones. Combining these two kinds of data, we build Hybrid Bayesian networks, then use graph theory to simplify the process of calculation. Finally, we find that this Hybrid Bayesian networks can provide more accurate and intelligent community detection and it can be applied to different target users and target network communities. Then firms can provide better services for target market.
关键词：Bayesian Network;Graph theory;Community detection;Social network