期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

基于MATLAB环境的聚类系数的计算 被引量：3

The Calculation of the Clustering Coefficient Based on MATLAB
下载PDF
导出
摘要 本文对复杂网络的一个重要测度聚类系数进行了深入研究,给出了通过点的邻居子图以及通过三角形途径计算网络聚类系数的方法,并结合MATLAB函数特点设计了这两种方法的M文件SUB-GC.m和TRIC.m.通过大量计算比较,调用SUBGC.m文件计算复杂网络系数的方法是一种比较高效的计算方法. In this paper, we do profound study on one of the important measurement of a complex network, which is called clustering coefficient, and present two methods of calculating the clustering coefficient （ C ） of a complex network. One is based on the neighbor sub graph of a vertex and the other is based on the triangles. Besides, according to the characteristics of the functions in MATLAB, we program the two M-file which are SUBGC. m and TRIC. m. After numerous comparison in calculation, the method of using SUBGC. m is proved a rather high efficient method of calculating the clustering coefficient （ C ） of the complex network.
作者 李岸巍 阮豫红
机构地区 太原工业学院基础部
出处 《山西师范大学学报（自然科学版）》 2009年第3期32-35,共4页 Journal of Shanxi Normal University(Natural Science Edition)
关键词 复杂网络 聚类系数 MATLAB 计算方法 complex network clustering coefficient MATLAB method of calculation
分类号 TP393 [自动化与计算机技术—计算机应用技术]
  • 相关文献

参考文献21

  • 1Watts D J, Strogatz S H. Collective dynamics of small-world networks [ J ]. Nature, 1998,393:440 - 442.
  • 2Newman M E J, Strogatz S H, Watts D J. Random graph with arbitrary degree distribution and their applications [J]. Phys Rev E, 2001,64: 026118.
  • 3Newman M E J, Watts D J, Strogatz S H. Random graph models of social networks[J]. Proceedings of the National Academy of Science of the United States of America , 2002, 99:2566 - 2572.
  • 4Thomas Schank, Dorothea Wagner. Approximating clustering coefficient and transitivity [ J ]. Journal of Graph Algorithms and Applications, 2005,9:265 - 275.
  • 5Barrat A, Weigt M. On the properties of small-world networks models[ J]. Eur Phys J, 2000, 13:547 - 560.
  • 6Albert R, Barabasi A L. Statistical mechanics of complex networks [J]. Rev Mod Phys, 2002,74:47 -97.
  • 7Konstantin Klemm , VEctor M Eguiluz. Growing scale-free networks with small-world behavior[ J]. Phys Rev E ,2002, 65.
  • 8Agata Fronczak, Piotr Fronczak, et al. Mean-field theory for clustering coefficients in Barabdsi-Albert networks[J]. Phys Rev E , 2003,68.
  • 9Marian Bogun a, Romualdo Pastor-Satorras. Class of correlated random networks with hidden variables [ J]. Phys Rev E ,2003, 68:036112.
  • 10Newman M E J, Park J. Why social networks are different from other types of networks[ J ]. Phys Rev E, 2003,68:036122.

同被引文献16

  • 1Watts D J,Strogatz S H.Collective dynamics of small-world networks[J]. Nature, 1998,393.- 440-442.
  • 2Friedel C, Zimmer R :Inferring topol-ogy from clustering coefficients in protein-protein interaction networks [I]. BM C Bioinformatics, 2006,7: 519.
  • 3Radicchi F,Castellano C,Cecconi F, Loreto V,Parisi D:Defining and iden- tifying communities in networks[J]. P NAS, 2004, 101 : 2658-2663.
  • 4Li M,Wang J,Chen X,Wang H,Pan Y. A local average connectivity-based method for identifying essential pro- teins from the network level[J]. Comput Biol Chem 2011,35:143-150.
  • 5Watts D J, Strogatz S H. Collective dynamics of small-world networks[J]. Nature ,1998,393:440-442.
  • 6Friedel C, Zimmer R: Inferring topology from clustering coeffi- cients in protein-protein interaction networks[J]. BMC Bioinformatics ,2006, 7:519.
  • 7Radicchi F, Castellano C, Cecconi F, Loreto V, Parisi D: Defin- ing and identifying communities in networks[J]. PNAS , 2004, 101: 2658-2663.
  • 8Li M, Wang J, Chen X, Wang H, Pan Y: A local average con- nectivity-based method for identifying essential proteins from the network level[J]. Comput Biol Chem 2011, 35:143-150.
  • 9岑健,胥布工,张清华,朱月君.基于证据理论的免疫检测器在轴承故障诊断中的应用[J].轴承,2009(8):42-45. 被引量：7
  • 10徐富强,郑婷婷,方葆青.基于广义回归神经网络(GRNN)的函数逼近[J].巢湖学院学报,2010,12(6):11-16. 被引量：20

引证文献3

二级引证文献5

山西师范大学学报（自然科学版）
2009年 第3期

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部