Please wait a minute...
文章检索
复杂系统与复杂性科学  2016, Vol. 13 Issue (4): 56-61    DOI: 10.13306/j.1672-3813.2016.04.008
  本期目录 | 过刊浏览 | 高级检索 |
基于节点动态连接度的网络社团划分算法
贾珺a,b, 胡晓峰a, 贺筱媛b
国防大学 a.信息作战与指挥训练教研部;b.研究生院,北京 100091
Finding Community Structure in Networks Using Node’s Dynamic Connection Degree
JIA Jun, HU Xiaofeng, HE Xiaoyuan
a. The Department of Information Operation Command Training; b. The Department of Graduate,National Defense University,Beijing 100091,China
全文: PDF(900 KB)  
输出: BibTeX | EndNote (RIS)      
摘要 首先定义了节点动态连接度这一概念,然后介绍了基于节点动态连接度的网络社团划分算法,之后再对其中相关参数的取值范围和社团划分结果之间的关系进行了分析,并以Zachary网络为例验证了分析结论。在此基础上,以dolphins、polbooks和football 3个实际网络为对象,进行了社团划分实验,证明了本算法可通过动态调整参数实现对不同网络的社团划分。最后将实验结果与其他几种常见的社团划分算法结果进行了比较,证明了算法的优势,并对算法中需要注意的一些问题进行了说明。
服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
贾珺
胡晓峰
贺筱媛
关键词 节点动态连接度社团结构社团划分    
Abstract:This paper gives the definition of node’s dynamic connection degree at first, and then introduces the algorithm of finding the community structure by the node’s dynamic connection degree. After that, it analyzes the range of parameter’s value in node’s connection degree and proves it by experimenting on the Zachary network. On this basis, it experiments on three real networks which are dolphins, polbooks and football. The result proves that this algorithm can find different network’s community structure correctly by adjust its parameter’s value. At last, it compares the result with some other common algorithms’ and illustrates some matters that need attention.
Key wordsnode′s dynamic connection degree    community structure    finding community
收稿日期: 2015-07-06      出版日期: 2025-02-25
ZTFLH:  TP391.9  
基金资助:国家自然科学基金(U1435218, 61174035, 61273189, 61374179)
作者简介: 贾珺(1981-),男,陕西西安人,博士研究生,助理研究员,主要研究方向为军事运筹学。
引用本文:   
贾珺, 胡晓峰, 贺筱媛. 基于节点动态连接度的网络社团划分算法[J]. 复杂系统与复杂性科学, 2016, 13(4): 56-61.
JIA Jun, HU Xiaofeng, HE Xiaoyuan. Finding Community Structure in Networks Using Node’s Dynamic Connection Degree[J]. Complex Systems and Complexity Science, 2016, 13(4): 56-61.
链接本文:  
https://fzkx.qdu.edu.cn/CN/10.13306/j.1672-3813.2016.04.008      或      https://fzkx.qdu.edu.cn/CN/Y2016/V13/I4/56
[1] Wasserman S, Faust K. Social Network Analysis: Methods and Applications[M].Cambridge:Cambridge University Press, 1994.
[2] Radicchi F, Castellano C, Cecconi F, et al, Defining and identifying communities in networks[J].Proc Natl Acad Sci,2004,101(9):2658-2663.
[3] 骆志刚,丁凡,蒋晓舟,等.复杂网络社团发现算法研究新进展[J].国防科技大学学报, 2011,33(1):47-52.
Luo Zhigang, Ding Fan, Jiang Xiaozhou, et al. New progress on community detection in complex networks[J].Journal of National University of Defense Technology, 2011, 33(1): 47-52.
[4] Kernighan B W, Lin S. An efficient heuristic procedure for partitioning graphs[J].Bell system technical journal, 1970, 49(2): 291-307.
[5] Pothen A, Simon H D, Liou K P. Partitioning sparse matrices with eigenvectors of graphs[J].SIAM Journal on Matrix Analysis and Applications, 1990, 11(3): 430-452.
[6] Capocci A, Servedio V D P, Caldarelli G, et al. Detecting communities in large networks[J].Physica A: Statistical Mechanics and Its Applications, 2005, 352(2): 669-676.
[7] Newman M E J. Finding community structure in networks using the eigenvectors of matrices[J].Physical Review E, 2006, 74(3): 036104.
[8] Newman M E J. Fast algorithm for detecting community structure in networks[J].Physical Review E, 2004, 69(6): 066133.
[9] Clauset A, Newman M E J, Moore C. Finding community structure in very large networks[J].Physical Review E, 2004, 70(6): 066111.
[10] Girvan M, Newman M E J. Community structure in social and biological networks[J].Proceedings of the National Academy of Sciences, 2002, 99(12): 7821-7826.
[11] Newman M E J, Girvan M. Finding and evaluating community structure in networks[J].Physical Review E, 2004, 69(2): 026113.
[12] Fortunato S, Latora V, Marchiori M. Method to find community structures based on information centrality[J].Phys Rev E, 2004, 70(5): 056104.
[13] Pan Y, Li D H, Liu J G, et al. Detecting community structure in complex networks via node similarity[J].Physica A: Statistical Mechanics and Its Applications, 2010, 389(14): 2849-2857.
[14] 袁超, 柴毅. 基于簇相似度的网络社团结构探测算法[J].物理学报, 2012, 61(21): 539-547.
Yuan Chao, Chai Yi. Group similarity based algorithm for network community structure detection[J].Acta Phys Sin, 2012,61(21):539-547.
[15] 王兴元,赵仲祥.基于节点间依赖度的社团结构划分方法[J].物理学报, 2014, 63(17): 178901.
Wang Xingyuan, Zhao Zhongxiang. Partitioning community structure in complex network based on node dependent degree[J].Acta Phys Sin,2014,63(17): 178901.
[16] Zhanga J, Zhanga G, Yanga J, et al. Local community detection algorithm based on hierarchical clustering[J].Journal of Information & Computational Science 2015,12(7):2805-2813.
[17] Rosvall M, Bergstrom C T. An information-theoretic framework for resolving community structure in complex networks[J].Proceedings of the National Academy of Sciences, 2007, 104(18): 7327-7331.
[18] Deng K, Zhang J P, Yang J. An efficient multi-objective community detection algorithm in complex networks[J].Tehniki vjesnik, 2015, 22(2):319-328.
[19] Zachary W W. An information flow model for conflict and fission in small groups[J].Journal of Anthropological Research, 1977,33(4): 452-473.
[20] Lusseau D, Schneider K, Boisseau O J, et al. The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations[J].Behavioral Ecology and Sociobiology, 2003, 54(4): 396-405.
[1] 马佳骏, 勾文沙, 李小萌, 史毅, 肖明忠, 陈清华. “一带一路”沿线国家的人口迁移及其演化[J]. 复杂系统与复杂性科学, 2024, 21(3): 23-29.
[2] 高天, 许小可. 基于社团结构的抑制校园新冠传播研究[J]. 复杂系统与复杂性科学, 2024, 21(3): 9-16.
[3] 高峰. 复杂网络深度重叠结构的发现[J]. 复杂系统与复杂性科学, 2024, 21(2): 15-21.
[4] 张铭娜, 肖婧, 许小可. 展示网络重叠社团结构的可视化布局算法[J]. 复杂系统与复杂性科学, 2023, 20(4): 10-17.
[5] 周建云, 刘真真, 许小可. 参照零模型的实证网络传播影响因素分析[J]. 复杂系统与复杂性科学, 2019, 16(3): 40-47.
[6] 张姣, 刘三阳, 白艺光. 基于社团结构的组合信息重连策略[J]. 复杂系统与复杂性科学, 2019, 16(2): 1-8.
[7] 徐兵, 赵亚伟, 徐杨远翔. 基于关联群演化相似度的社团追踪算法[J]. 复杂系统与复杂性科学, 2019, 16(1): 14-25.
[8] 范如国, 崔迎迎, 张应青. 多元偏好、社团结构与网络合作涌现仿真研究[J]. 复杂系统与复杂性科学, 2016, 13(4): 26-34.
[9] 陈增强, 谢征, 张青. 基于非负矩阵分解的复杂网络重构[J]. 复杂系统与复杂性科学, 2016, 13(3): 26-32.
[10] 李婵婵, 蒋国平. 社团结构网络环境下SIS病毒传播建模与分析[J]. 复杂系统与复杂性科学, 2016, 13(2): 67-73.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed