收稿日期: 2014-05-27
修回日期: 2014-07-04
网络出版日期: 2014-08-20
基金资助
本文系国家社会科学基金项目“在线社交网络中基于用户的知识组织模式研究”（项目编号：14BTQ033）和安徽财经大学2014年度校级科研项目“大尺度在线社会网络的社区发现及其应用研究”（项目编号：ACKY1428）研究成果之一。
Community Detection for Large-scale Multi-dimensional Network
Received date: 2014-05-27
Revised date: 2014-07-04
Online published: 2014-08-20
吴小兰 , 章成志 . 面向大规模多维社会网络的社区发现研究[J]. 图书情报工作, 2014 , 58(16) : 122 -130 . DOI: 10.13266/j.issn.0252-3116.2014.16.019
Users in multi-dimensional network usually show a variety of behaviors and interests, so it is hard to find effective communities by using only one dimension. In order to effectively solve the above problem, this paper firstly maps directed networks into undirected weighted networks based on user relationship strength, and then integrates all the networks. Secondly, this paper models hidden community by using SSN-LDA, and calculates the users' similarity by user-community probability distribution matrix. At last, Bisecting K-Means is used to detect community of users. Through the experiments on real Science blog, the result shows that this method can get more accurate user community.
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