计算机科学 ›› 2009, Vol. 36 ›› Issue (8): 243-246.

• 人工智能 • 上一篇    下一篇

基于局部信息检测的多粒度社团挖掘方法

朱涛,常国岑,郭戎潇,李项军   

  1. (空军工程大学电讯工程学院 西安710077)
  • 出版日期:2018-11-16 发布日期:2018-11-16
  • 基金资助:
    本文受军队科研基金资助项目(编号:KJ06104)资助。

Method of Multi-granularity Community Structure Mining Based on Local Information Detection

ZHU Tao,CHANG Guo-cen,GUO Rong-xiao, LI Xiang-jun   

  • Online:2018-11-16 Published:2018-11-16

摘要: 从复杂性和动态性特征出发,给出了复杂网络局部模块度的定义,并提出了基于局部信息检测的社团发现算法,认为局部模块度值最大的节点集合就是最理想的社团结构。在此基拙上提出了多粒度社团挖掘方法,为多视图观察复杂网络结构特征提供了新的研究思路。最后的实验分析表明了方法的有效性和可行性。

关键词: 复杂网络,社团挖掘,局部信息检测,多粒度

Abstract: Taking consideration of complexity and dynamic of complex networks, a definition of local modularity was proposed, and an algorithm for communication structure mining based on local information detection was given, with the criterion, i, e. the best community is the node group whose local modularity is the largest. Then a method of multi-granularity community structure mining was proposed, which provides new ideas to observe structure characters of complex networks from various angles. Final experiments verify its efficiency and feasibility.

Key words: Complex networks, Community structure mining, Local information detection, Multi granularity

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