This paper provides a selective review of the statistical network analysis literature focused on clustering and inference problems for stochastic blockmodels and their variants. We survey asymptotic normality results for stochastic blockmodels as a means of thematically linking classical statistical concepts to contemporary research in network data analysis. Of note, multiple different forms of asymptotically Gaussian behavior arise in stochastic blockmodels and are useful for different purposes, pertaining to estimation and testing, the characterization of cluster structure in community detection, and understanding latent space geometry. This paper concludes with a discussion of open problems and ongoing research activities addressing asymptotic normality and its implications for statistical network modeling.

翻译：本文对统计网络分析文献中聚焦于随机块模型及其变体的聚类与推断问题进行了选择性综述。我们系统梳理了随机块模型的渐近正态性研究成果，旨在将经典统计概念与当代网络数据分析研究进行主题性联结。值得注意的是，随机块模型中存在多种不同形式的渐近高斯行为，这些行为在估计与检验、社区发现中聚类结构的刻画以及潜在空间几何理解等方面具有不同用途。本文最后讨论了关于渐近正态性及其对统计网络建模影响的开放性问题与当前研究动态。