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无标度网络度秩指数的变化范围
引用本文:王林,戴冠中.无标度网络度秩指数的变化范围[J].控制理论与应用,2006,23(4):503-507.
作者姓名:王林  戴冠中
作者单位:1. 西安理工大学,自动化学院,陕西,西安,710048;西北工业大学,自动化学院,陕西,西安,710072
2. 西北工业大学,自动化学院,陕西,西安,710072
摘    要:实证研究表明,绝大多数复杂网络的结点的度分布服从幂律分布,该幂律分布的幂指数的绝对值(度分布指数)介于2和3之间.然而,至今尚未发现为什么度分布指数介于2和3之间的研究结果.本文证明了度分布指数大于2,从而部分回答了上述问题.为此,本文引进度秩指数,并给出了度秩指数和度分布指数之间的关系.通过对度秩指数与网络结构熵之间的关系的刻画,发现了度秩指数与网络结构熵以及网络规模之间的函数依赖关系,从而最终证明了度秩指数的临界值趋于1,并给出了仿真结果.

关 键 词:复杂网络  无标度网络  度分布  度秩指数  网络结构熵
文章编号:1000-8152(2006)04-0503-05
收稿时间:2004-10-26
修稿时间:2004-10-262005-10-09

Range of degree-rank exponent of scale-free networks
WANG Lin,DAI Guan-zhong.Range of degree-rank exponent of scale-free networks[J].Control Theory & Applications,2006,23(4):503-507.
Authors:WANG Lin  DAI Guan-zhong
Affiliation:School of Automation, Xi'an University of Technology, Xi'an Shaanxi 710048, China; School of Automation, Northwestern Polytechnical University,Xi'an Shaanxi 710072, China
Abstract:Empirical study shows that most of complex networks are scale-free, and the node degrees of which obey the power-law distribution with the absolute value of the corresponding power exponent (known as degree distribution exponent) lying between 2 and 3. However, there is no research result on why the degree distribution exponent lies between 2 and 3 so far. The authors prove that the degree distribution exponent is greater than 2 and thus partially solve the problem mentioned above. First, the degree-rank exponent is introduced, the relation between the degree-rank exponent and the degree distribution exponent is also given. Through the characterization on the relation between the degree-rank exponent and the network structural entropy, functional dependent relations among the degree-rank exponent, the network structural entropy and the number of nodes of the network are then obtained and the result that the degree-rank exponent approaches one is proved. Finally, simulation results are given to illustrate the theoretical result.
Keywords:complex network  scale-free network  degree distribution  degree-rank exponent  network structural entropy
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