• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

J4 ›› 2014, Vol. 36 ›› Issue (07): 1377-1383.

• 论文 • 上一篇    下一篇

复杂网络中随机图模型研究

黄斌1,吴春旺2,郑丰华3,蔺冰2   

  1. (1.成都信息工程学院数学学院, 四川 成都 610225;2.成都信息工程学院网络工程学院,四川 成都 610225;3.成都信息工程学院计算中心和网络舆情研究所,四川 成都 610225)
  • 收稿日期:2013-09-03 修回日期:2013-11-19 出版日期:2014-07-25 发布日期:2014-07-25
  • 基金资助:

    成都信息工程学院中青年学术带头人科研基金资助项目(J201218 )

Research on the model of random
graphs in complex networks      

HUANG Bin1,WU Chunwang2,ZHENG Fenghua3,LIN Bing2   

  1. (1.College of Mathematics,Chengdu University of Information Technology,Chengdu 610225;
    2.College of Network Engineering,Chengdu University of Information Technology,Chengdu 610225;
    3.Computing Centre and Research of Network Public Opinion,
    Chengdu University of Information Technology,Chengdu 610225,China)
  • Received:2013-09-03 Revised:2013-11-19 Online:2014-07-25 Published:2014-07-25

摘要:

随着复杂网络研究的兴起,随机图成为一种重要复杂网络模型。基于完全图的生成子图的思想,得到了生成随机图的一种新算法,即用去边的方法生成随机图的算法,并用数值实验验证了加边和去边生成的随机图的统计特性(最大度、最小度、聚集系数、平均最短路径和平均度)是相近的,用去边的方法得到的图的度分布曲线在其平均度处达到峰值,随后呈指数下降,这与随机图的度分布是相同的。为了得到稀疏连通的随机图,又提出了一个不去割边的近似随机图生成算法,并从理论上说明了该算法生成的图是连通的,同时通过数值实验验证了图的连通性,并与加边随机图的统计特性进行了比较。

关键词: 随机图, 完全图, 生成子图, 复杂网络, 连通性, 算法

Abstract:

With the development of the study of complex networks, random graphs become an important model in complex networks. On the basis of spanned subgraphs of complete graphs, a new algorithm of generating random graphs is proposed by means of removing the edges of a complete graph. It is verified by numerical experiments that the statistical properties (maximum degree, minimum degree, clustering coefficient, average shortest path and the average degree) of the random graphs generated by increasing or removing the edges are similar to each other. The degree distribution of these graphs obtained by removing edges reaches the peak at the average degree and then turns to decay exponentially. This is the same as the degree distribution of random graphs. In order to get the sparse connective random graphs, an approximate random graph generation algorithm without removing cutting edge is proposed. And it is theoretically explained that the generated graphs are connected ones. Meanwhile, numerical experiments are carried out to verify that the generated graphs are connected, and the comparison of statistical properties is made with the random graphs generated by increasing the edges.

Key words: random graph;complete graph;spanned subgraph;complex networks;connectivity;algorithm