吉林大学学报(工学版) ›› 2013, Vol. 43 ›› Issue (01): 98-105.

• 论文 • 上一篇    下一篇

基于环路紧密度的复杂网络社区挖掘方法

刘大有1,2, 杨建宁1,2, 杨博1,2, 赵学华1,2, 金弟3   

  1. 1. 吉林大学 计算机科学与技术学院, 长春 130012;
    2. 吉林大学 符号与知识工程教育部重点实验室 长春130012;
    3. 天津大学 计算机科学与技术学院, 天津 300072
  • 收稿日期:2012-05-06 出版日期:2013-01-01 发布日期:2013-01-01
  • 通讯作者: 杨博(1974-),男,教授,博士生导师.研究方向:Agent系统,数据挖掘,复杂网络分析.E-mail:ybo@jlu.edu.cn E-mail:ybo@jlu.edu.cn
  • 作者简介:刘大有(1942-),男,教授,博士生导师.研究方向:多Agent系统,移动Agent,知识工程与专家系统,数据挖掘以及空间推理.E-mail:liudy@jlu.edu.cn
  • 基金资助:

    国家自然科学基金项目(60873149,60973088,61133011,61170092);模式识别国家重点实验室开放课题;中央高校基本科研业务费专项资金(20093177);教育部新世纪优秀人才支持计划项目(NCET-11-0204);2011年教育部博士学术新人奖项目(450060454018).

Community mining from complex networks based on loop tightness

LIU Da-you1,2, YANG Jian-ning1,2, YANG Bo1,2, ZHAO Xue-hua1,2, Jin Di3   

  1. 1. College of Computer Science and Technology, Jilin University, Changchun 130012, China;
    2. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China;
    3. School of Computer Science and Engineering, Tianjin University, Tianjin 300072, China
  • Received:2012-05-06 Online:2013-01-01 Published:2013-01-01

摘要: 提出了一种基于环路紧密度的复杂网络社区挖掘算法(LTA):首先提出一种快速发现网络环路和计算其紧密值的算法,然后根据环路紧密值将网络聚类,再次揭示网络环路与社区结构的联系。并使用人工合成网络和真实网络数据集对LTA进行了验证,实验结果证明了LTA对复杂网络社区挖掘问题的有效性和高效性。

关键词: 人工智能, 数据挖掘, 复杂网络, 社区挖掘, 环路紧密度算法

Abstract: In this paper, a Loop Tightness Algorithm (LTA) is proposed. First, it finds the network loops and calculates it tightness value quickly. Then, it obtains the communities of the networks based on the tightness values. Finally, it reveals the relationship between the network loops and the community structure. The LTA is tested and validated by means of synthetic networks and real networks.

Key words: artificial infelligence, data mining, complex networks, community mining, loop tightness algorithm(LTA)

中图分类号: 

  • TP18
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