An Overview of Asymptotic Normality in Stochastic Blockmodels: Cluster Analysis and Inference
Abstract
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. 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.
Fields of Study & Subjects
Complex Network Analysis Techniques
Stochastic processes and statistical mechanics
Advanced Clustering Algorithms Research
Publication details
- Journal / source
- Statistical Science
- Publisher
- Institute of Mathematical Statistics
- Volume
- 41
- Issue
- 2
- Pages
- 354-385
- Publication date
- 8 April 2026
- Language
- EN