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山东大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (1): 37-41.doi: 10.6040/j.issn.1672-3961.1.2016.180

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一种基于结构信息的改进CNM算法

吕振1,2,李苏雪1,2,张传亭1,2,袁东风1,2*   

  1. 1. 山东大学信息科学与工程学院, 山东 济南 250100;2. 山东省中国虹计划协同创新中心, 山东 济南 250100
  • 收稿日期:2016-03-31 出版日期:2017-02-20 发布日期:2016-03-31
  • 通讯作者: 袁东风(1958— ),男,四川安岳人,教授,博士,主要研究方向为5G关键技术,云计算与大数据. E-mail:dfyuan@sdu.edu.cn E-mail:zhenlv_2011@163.com
  • 作者简介:吕振(1992— ),女,山东潍坊人,硕士研究生,主要研究方向为数据挖掘.E-mail:zhenlv_2011@163.com
  • 基金资助:
    山东省自主创新及成果转化重大专项基金资助项目(2014ZZCX03401)

An improved CNM algorithm based on network structure information

LYU Zhen1,2, LI Suxue1,2, ZHANG Chuanting1,2, YUAN Dongfeng1,2*   

  1. 1. School of Information Science and Engineering, Shandong University, Jinan 250100, Shandong, China;
    2. Collaborative Innovation Center of China Rainbow Project, Jinan 250100, Shandong, China
  • Received:2016-03-31 Online:2017-02-20 Published:2016-03-31

摘要: CNM(clauset-newman-moore)算法能有效划分网络社区结构,但是对应划分出的社区准确度不高。对此,结合网络结构信息提出了一种改进CNM算法。通过对输入数据进行迭代删边预处理,精简网络结构,将原始网络分为两个子网络,然后将CNM算法应用到子网络,完成社区发现。在五个不同规模数据集上的试验结果表明,改进CNM方法提高了社区发现的质量和精度,社区模块度在小规模的数据集上得到了显著提升。

关键词: 边介数, 模块度, 社区发现, CNM 改进, 结构信息

Abstract: Although community detection could be effectively accomplished by CNM(clauset-newman-moore)algorithm, the accuracy of the results was unsatisfactory. Consequently, an improved CNM algorithm based on network structure information was proposed, which divided the original network into two parts by removing the edge whose edge betweenness was maximum of all iteratively. These two parts as the input data of CNM algorithm were used to detect communities. The experimental results on five different size of datasets showed that the improved CNM algorithm elevated the quality of community detection, and modularity of these communities peformed well especially in small datasets.

Key words: community detection, improved CNM algorithm, structure information, edge betweenness, modularity

中图分类号: 

  • TP391
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