吉林大学学报(工学版) ›› 2015, Vol. 45 ›› Issue (5): 1592-1600.doi: 10.13229/j.cnki.jdxbgxb201505031

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谱分析与启发式遗传算法相结合的多尺度社区检测方法

郭玉泉1, 李雄飞1, 刘昕2   

  1. 1.吉林大学 计算机科学与技术学院,长春 130012;
    2.吉林大学 网络中心,长春 130022
  • 收稿日期:2014-05-04 出版日期:2015-09-01 发布日期:2015-09-01
  • 通讯作者: 刘昕(1976-),女,高级工程师.研究方向:智能交通与网络计算.E-mail:lx@jlu.edu.cn
  • 作者简介:郭玉泉(1974-）,男,博士研究生.研究方向:复杂网络分析,数据挖掘.E-mail:guoyuquan@sohu.com
  • 基金资助:
    吉林省科技发展计划项目(20090468,20100508,201105017)

Heuristic genetic algorithm associated with spectral analysis uncovering multi-scale community of complex networks

GUO Yu-quan1, LI Xiong-fei1, LIU Xin2   

  1. 1.College of Computer Science and Technology, Jilin University, Changchun 130012,China;
    2.Network Center, Jilin University, Changchun 130022,China
  • Received:2014-05-04 Online:2015-09-01 Published:2015-09-01

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摘要： 针对常规的社区检测方法不能揭示出社区结构的多尺度特征这一问题,本文通过对复杂网络传导率函数C与社区平均凝聚概率的分析,提出了一种局部启发变异策略,同时将复杂网络谱分析与遗传算法相结合,提出了多尺度社区检测算法HGASA。在人工网络和现实网络上对HGASA算法进行了测试,实验结果表明了HGASA算法的有效性和高效性。

关键词: 计算机应用, 多尺度社区, 遗传算法, 启发函数, 局部启发变异算法

Abstract: The community structure of complex networks has attracted much attention. However, previous methods can not investigate the multi-scale property of the community. To address this problem, by analyzing conduct function C and average agglomerate probability, a local heuristic heteromorphosis strategy is proposed; then, spectral analysis of complex networks is combined with genetic algorithm; finally a multi-scale community detection algorithm HGASA (Heuristic genetic algorithm with spectral analysis) is proposed. Extensive tests on artificial networks and real world networks justify the superiority of the HGASA algorithm.

Key words: computer application, multiple-scale community, genetic algorithm, heuristic function, local heuristic mutation algorithm

中图分类号: 

  • TP391
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