标题： 关于网络数据中确定社区数量的综述 显示英文标题

标题： Review on Determining the Number of Communities in Network Data

摘要： 本文综述了网络模型中假设检验和聚类的统计方法。 我们分析了 Bickel 等人（2016）的方法，用于推导最大特征值的渐近零分布，注意到其收敛速度较慢，并且需要进行自助法校正。 Jin 等人提出的 SCORE 方法（2015）以及 Chen 等人提出的 NCV 方法（2018）被评估用于度矫正块模型中的聚类效果，由于 NCV 的时间密集性质，它面临一些挑战。 我们建议探索特征向量分量分布以作为潜在的效率改进。 显示英文摘要

摘要： This paper reviews statistical methods for hypothesis testing and clustering in network models. We analyze the method by Bickel et al. (2016) for deriving the asymptotic null distribution of the largest eigenvalue, noting its slow convergence and the need for bootstrap corrections. The SCORE method by Jin et al. (2015) and the NCV method by Chen et al. (2018) are evaluated for their efficacy in clustering within Degree-Corrected Block Models, with NCV facing challenges due to its time-intensive nature. We suggest exploring eigenvector entry distributions as a potential efficiency improvement.