基于归一化聚类系数的网络聚类强度度量
Measuring the Clustering Strength of a Network via the Normalized Clustering Coefficient
摘要：
在本文中，我们提出了一种新的统计网络，归一化聚类系数，这是一个修改版本的聚类系数，是强大的网络规模，网络密度和程度的异质性在不同的网络生成模型。特别地，在度校正块模型（DCBM）下，可以从归一化的聚类系数推断出“进出比”。在三种流行的网络生成模型下研究了该指标的渐近性质。归一化聚类系数还可用于网络聚类、网络抽样和动态网络分析。仿真和实际数据分析进行了演示这些应用程序。
英文摘要：
In this paper, we propose a novel statistic of networks, the normalized clustering coefficient, which is a modified version of the clustering coefficient that is robust to network size, network density and degree heterogeneity under different network generative models. In particular, under the degree corrected block model (DCBM), the "in-out-ratio" could be inferred from the normalized clustering coefficient. Asymptotic properties of the proposed indicator are studied under three popular network generative models. The normalized clustering coefficient can also be used for networks clustering, network sampling as well as dynamic network analysis. Simulations and real data analysis are carried out to demonstrate these applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
