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Community Detection for Large-scale Multi-dimensional Network
Received date: 2014-05-27
Revised date: 2014-07-04
Online published: 2014-08-20
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.
Wu Xiaolan , Zhang Chengzhi . Community Detection for Large-scale Multi-dimensional Network[J]. Library and Information Service, 2014 , 58(16) : 122 -130 . DOI: 10.13266/j.issn.0252-3116.2014.16.019
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