Detecting Algorithm Based on Prior Knowledge and Modularity for Networked Community Structure

杜海峰 1; 2; 李树茁 1; Marcus W. Feldman 2; 悦中山 1; 3; 杨绪松 1; 3

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Detecting Algorithm Based on Prior Knowledge and Modularity for Networked Community Structure

更新时间：2025-12-08

- Detecting Algorithm Based on Prior Knowledge and Modularity for Networked Community Structure
- Vol. 41, Issue 6, Pages: 750-754(2007)
- 作者机构：
  
  1. 西安交通大学人口与发展研究所,西安,710049
  2. 斯坦福大学人口与资源研究所, 94305, 美国斯坦福
- 作者简介：
- 基金信息：
- DOI：
  CLC： N94
- Online First：10 June 2007，
  
  Published：2007
- 稿件说明：
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杜海峰 1, 2, 李树茁 1, et al. Detecting Algorithm Based on Prior Knowledge and Modularity for Networked Community Structure[J]. 2007, 41(6): 750-754.
DOI：

杜海峰 1, 2, 李树茁 1, et al. Detecting Algorithm Based on Prior Knowledge and Modularity for Networked Community Structure[J]. 2007, 41(6): 750-754. DOI：

摘要

在分析模块性指标和Newman有关网络社区结构探测算法的基础上

提出了一种基于先验知识与模块性的社区结构探测算法.利用节点度等社会网络结构先验知识

获得一个社区结构的基本划分

然后进行社区的合并

以此获得一个清晰的社区结构.经计算机模拟网络、Ucinet软件网络和中国农民工社会网络的社区结构探测

结果表明所提算法比Newman的迭代次数减少近50%

并且可以获得更好的模块性指标.

Abstract

On the basis of analyzing the modularity and Newman detecting algorithm for network community structure

an algorithm based on prior knowledge and modularity(PKM)is put forward to detect community structure. An original community division is acquired by using the prior knowledge of the structure of social networks

such as the degree of the node

and then the communities are combined so as to get a clarified partition. Through calculation of computer simulation networks

Ucinet networks and Chinese rural-urban migrants social networks

the results indicate that the number of iterations of the proposed algorithm is reduced nearly by 50% compared to that of Newman's

and the higher modularity can be yielded.

关键词

Keywords

references

Newman M E J. Detecting community structure in networks [J]. Eur Phys J:B, 2004,38(2):321-330.

Newman M E J. Fast algorithm for detecting community structure in networks [J]. Phys Rev:E, 2004, 69(6):066133.

罗家德. 社会网分析讲义 [M]. 北京:社会科学文献出版社,2005.

Wasserman S,Faust K. Social network analysis: methods and applications [M]. New York:Cambridge University Press, 1994.

王林,戴冠中. 复杂网络中的社区发现——理论与应用 [J]. 科技导报, 2005,23(8):62-66.

Wang Lin, Dai Guanzhong. Community finding in complex networks — theory and applications [J]. Science Technology Review,2005, 23(8):62-66.

Scott W, Smyth P. A spectral clustering approach to finding communities in graphs[EB/OL].[2006-04-11].http:∥www.datalab. uci.edu/papers/siam_graph_clustering.pdf.

Aaron C, Newman M E J, Moore C. Finding community structure in very large networks [J]. Phys Rev:E,2004,74(3):36104.

Albert R, Barabàsi A L R. Statistical mechanics of complex networks [J]. Reviews of Modern Physics, 2002,74(1):48-97.

Watts D J, Strogatz S H. Collective dynamics of ‘small-world' networks [J]. Nature, 1998, 393(4): 440-442.

Newman M E J, Girvan M. Statistical mechanics of complex networks [M]. Berlin: Springer, 2003.

Dorogovtsev S N, Mendes J F. Scaling properties of scale-free evolving networks: continuous approach [J].Phys Rev:E, 2001,63(5):056125.

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