平均闭合系数的中心极限定理

Mingao Yuan
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引用次数: 0

摘要

现实世界中的许多网络都存在边缘聚类现象,这种现象通常用平均聚类系数来衡量。最近,有人提出了另一种量化局部聚类的方法--平均闭合系数。研究表明,平均闭合系数具有许多有用的特性,可以捕捉经典平均聚类系数所遗漏的补充信息。本文研究了异质随机图的平均闭合系数的渐近分布。我们证明了标准化平均闭合系数的分布收敛于标准正态分布。在 Erd\"{o}s-R\'{e}nyi 随机图中,平均闭合系数的方差表现出与平均聚类系数相同的相变现象。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Central limit theorem for the average closure coefficient
Many real-world networks exhibit the phenomenon of edge clustering, which is typically measured by the average clustering coefficient. Recently, an alternative measure, the average closure coefficient, is proposed to quantify local clustering. It is shown that the average closure coefficient possesses a number of useful properties and can capture complementary information missed by the classical average clustering coefficient. In this paper, we study the asymptotic distribution of the average closure coefficient of a heterogeneous Erd\"{o}s-R\'{e}nyi random graph. We prove that the standardized average closure coefficient converges in distribution to the standard normal distribution. In the Erd\"{o}s-R\'{e}nyi random graph, the variance of the average closure coefficient exhibits the same phase transition phenomenon as the average clustering coefficient.
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