Journal of Computer Applications ›› 2015, Vol. 35 ›› Issue (1): 157-161.DOI: 10.11772/j.issn.1001-9081.2015.01.0157

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Topic group discovering algorithm based on trust chain in social network

LI Meizi1,2, XIANG Yang1, ZHANG Bo2, JIN Bo3   

  1. 1. College of Electronics and Information Engineering, Tongji University, Shanghai 201804, China;
    2. College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 200234, China;
    3. The Third Research Institute of Ministry of Public Security, Shanghai 200234, China
  • Received:2014-08-08 Revised:2014-09-27 Online:2015-01-01 Published:2015-01-26

社会网络中基于信任链的主题群组发现算法

李美子1,2, 向阳1, 张波2, 金波3   

  1. 1. 同济大学 电子与信息工程学院, 上海201804;
    2. 上海师范大学 信息与机电工程学院, 上海200234;
    3. 公安部第三研究所, 上海201204
  • 通讯作者: 金波
  • 作者简介:李美子(1979-),女,河北定州人,讲师,博士研究生,主要研究方向:社交网络、可信计算;向阳(1962-),男,山东泰安人,教授,博士生导师,主要研究方向:智能信息系统、数据挖掘;张波(1978-),男,江苏常州人,副教授,博士,主要研究方向:社交网络分析、语义计算、可信计算.
  • 基金资助:

    国家自然科学基金资助项目(61103069, 71171148);上海市教委科研创新项目(13YZ052);信息网络安全公安部重点实验室开放课题资助项目(C14602).

Abstract:

To solve the challenge of accurate user group discovering, a user topic discovering algorithm based on trust chain, which was composed by three steps, i.e., topic space discovering, group core user discovering and topic group discovering, was proposed. Firstly, the related definitions of the proposed algorithm were given formally. Secondly, the topic space was discovered through the topic-correlation calculation method and a user interest calculation method for topic space was addressed. Further, the trust chain model, which was composed by atomic, serial, and parallel trust chains, and its trust computation method of topic space were presented. Finally, the detail algorithms of topic group discovering, including topic space discovering algorithm, core user discovering algorithm and topic group discovering algorithm, were proposed. The experimental results show that the average accuracy of the proposed algorithm is 4.1% and 11.3% higher than that of the traditional interest-based and edge density-based group discovering methods. The presented algorithm can improve the accuracy of user group organizing effectively, and it will have good application value for user identifying and classifying in social network.

Key words: social network, topic group, topic space, interest degree, trust chain model

摘要:

针对社会网络中用户群组准确发现难题,提出了一种基于信任链的用户主题群组发现方法.该方法包括3个部分:主题空间发现、群组核心用户发现和主题群组发现.首先,给出了社会网络主题群组的相关形式化定义;然后,通过主题相关度计算发现主题空间,并给出主题空间上用户兴趣度计算方法;其次,提出原子、串联和并联信任链计算模型,并给出主题空间上的信任链计算方法;最后,分别给出主题空间发现算法、核心用户发现算法和主题群组发现算法.实验结果表明,提出的用户群组发现算法相比基于兴趣度的群组发现算法和边紧密度群组发现算法,平均准确率提升4.1%和11.3%,能够有效提升用户群组组织的准确度,在社会网络用户分类识别方面具有较好的应用价值.

关键词: 社会网络, 主题群组, 主题空间, 兴趣度, 信任链模型

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