We seek to identify one or more computationally light-weight centrality metrics that have a high correlation with that of the maximal clique size (the maximum size of the clique a node is part of) - a computationally hard measure. In this pursuit, we compute three well-known measures of evaluating the correlation between two datasets: Product-moment based Pearson's correlation coefficient, Rank-based Spearman's correlation coefficient and Concordance-based Kendall's correlation coefficient. We compute the above three correlation coefficient values between the maximal clique size and each of the four prominent node centrality metrics (degree, eigenvector, betweenness and closeness) for random network graphsand scale-free network graphs as well as for a suite of ten real-world network graphs whose degree distribution ranges from random to scale-free. We also explore the impact of the operating parameters of the theoretical models for generating random networks and scale-free networks on the correlation between maximal clique size and the centrality metrics.

我们试图确定一个或多个计算上轻量级的中心性度量，这些度量与最大团大小（节点所属的团的最大大小）具有高度相关性——这是一种计算上的困难度量。为此，我们计算了评估两个数据集之间相关性的三个众所周知的度量：基于产品矩的 Pearson 相关系数、基于 Rank 的 Spearman 相关系数和基于 Concordance 的 Kendall 相关系数。我们计算最大集团规模与四个突出节点中心性度量（度、特征向量、中间性和接近性）用于随机网络图和无标度网络图以及一组十个真实世界网络图，其度分布范围从随机到无标度。我们还探讨了用于生成随机网络和无标度网络的理论模型的操作参数对最大集团规模与中心性度量之间相关性的影响。