[1]王娜,李霞,徐红英.社会网络分析之社区发现研究[J].深圳大学学报理工版,2014,31(1):35-42.[doi:10.3724/SP.J.1249.2014.01035]
 Wang Na,Li Xia,and Xu Hongying.Research on community detection in social network[J].Journal of Shenzhen University Science and Engineering,2014,31(1):35-42.[doi:10.3724/SP.J.1249.2014.01035]
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社会网络分析之社区发现研究()
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《深圳大学学报理工版》[ISSN:1000-2618/CN:44-1401/N]

卷:
第31卷
期数:
2014年第1期
页码:
35-42
栏目:
电子与信息科学
出版日期:
2014-01-14

文章信息/Info

Title:
Research on community detection in social network
文章编号:
20140105
作者:
王娜李霞徐红英
深圳大学信息工程学院,深圳 518060
Author(s):
Wang Na Li Xia and Xu Hongying
College of Information Engineering, Shenzhen University, Shenzhen 518060, P.R.China
关键词:
数据挖掘社会网络社区发现异质多模网络异质多维网络
Keywords:
data mining social network community detection heterogeneous multimodal information network heterogeneous network with multidimensional relations
分类号:
TP 393;TP 391
DOI:
10.3724/SP.J.1249.2014.01035
文献标志码:
A
摘要:
评述基于链接的同质社会网络社区发现方法,介绍基于多维链接关系和多模信息属性的异质网络社区发现方法,指出社会网络个体间不仅存在多种相互联系,其本身还存在描述自身特性的多种特征信息属性;对社会网络认识的逐渐深入,需融合多方面信息协同处理.根据链接关系矩阵,选取博客平台BlogCatalog,在协同训练框架下融合用户特征信息并进行仿真,模拟异质多模社会网络社区发现.结果表明,对多种链接信息和内容属性信息的融合研究和协同处理可为社会网络社区发现提供准确丰富的信息.
Abstract:
The analysis of social networks, in particular, the discovery of communities within a network, has been a focus of recent research with diverse applications in several fields. In many social networks, there exist different link relations between users while attributes or content information and factors such as demographic details or user-generated content may be associated with those users. In this paper, we outline the state-of-the-art community detection methods based on linked homogeneous social networks. Then, we emphasize community detection in a heterogeneous social network either with multimodal information for each user in the network or with multidimensional relations between users. For the heterogeneous multimode social network, a new community detection method is proposed in the framework of co-training to combine both links and content analysis. Experimental simulations on a real heterogeneous multimode social network dataset were performed and the results have shown that integration of links information and content attributes provided richer and more accurate information for detecting social network community structures.

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备注/Memo

备注/Memo:
Received:2013-07-11;Revised:2013-12-06;Accepted:2013-12-16
Foundation:National Natural Science Foundation of China(61171124,60902069)
Corresponding author:Professor Wang Na.E-mail:wangna@szu.edu.cn
Citation:Wang Na,Li Xia,Xu Hongying.Research on community detection in social network[J]. Journal of Shenzhen University Science and Engineering, 2014, 31(1): 35-42.(in Chinese)
基金项目:国家自然科学基金资助项目(61171124,60902069)
作者简介:王娜(1977-),女(汉族),河北省保定市人,深圳大学教授.E-mail:wangna@szu.edu.cn
引文:王娜,李霞,徐红英.社会网络分析之社区发现研究[J]. 深圳大学学报理工版,2014,31(1):35-42.
更新日期/Last Update: 2014-01-08