Work

A Note on the Identifiability of the Degree‐Corrected Stochastic Block Model

Year: 2025

Type: article

Abstract: ABSTRACT In this short note, we address the identifiability issues inherent in the degree‐corrected stochastic block model (DCSBM). We provide a rigorous proof demonstrating that the parameters of the DCSBM are identifiable up to a scaling factor and a permutation of the community labels, under a mild condition.

Source: Stat

DOI: 10.1002/sta4.70067

Authors: John Park, Yunpeng Zhao, Ning Hao

Institutions: University of Arizona, Colorado State University

Language: English

Topic: Complex Network Analysis Techniques

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: bronze

Funder: National Science Foundation

Awards: Collaborative Research: CDS&E-MSS: Community detection via covariance structures, RTG: Applied Mathematics and Statistics for Data-Driven Discovery, Collaborative Research: CDS&E-MSS: Community detection via covariance structures