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

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.

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Publication Details

Journal
Stat
Published
2025-05-19
DOI
https://doi.org/10.1002/sta4.70067
Primary Topic
Complex Systems and Time Series Analysis
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Hyoshin Park, Ning Hao, Yunpeng Zhao
Stat
Complex Systems and Time Series Analysis
article

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

Hyoshin Park, Ning Hao, Yunpeng Zhao
article en

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.

StatVol. 14(2)
University of Arizona (US), Colorado State University (US)
National Science Foundation
Openalex Percentile: Top 84%
Complex Systems and Time Series Analysis
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