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

John Park; Yunpeng Zhao; Ning Hao

doi:10.1002/sta4.70067

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

John Park, Yunpeng Zhao, Ning Hao

Research output: Contribution to journal › Article › peer-review

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.

Original language	English (US)
Article number	e70067
Journal	Stat
Volume	14
Issue number	2
DOIs	https://doi.org/10.1002/sta4.70067
State	Published - Jun 2025

Keywords

clustering
community detection
networks

ASJC Scopus subject areas

Statistics and Probability
Statistics, Probability and Uncertainty

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10.1002/sta4.70067

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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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N2 - 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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KW - community detection

KW - networks

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JO - Stat

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

Abstract

Keywords

ASJC Scopus subject areas

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