Consistent model selection in a collection of stochastic block models

Fuente: arXiv
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Main Author: Arts, Lucie
Format: Preprint
Published: 2025
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author Arts, Lucie
author_facet Arts, Lucie
contents We introduce the penalized Krichevsky-Trofimov (KT) estimator as a convergent method for estimating the number of nodes clusters when observing multiple networks within both multi-layer and dynamic Stochastic Block Models. We establish the consistency of the KT estimator, showing that it converges to the correct number of clusters in both types of models when the number of nodes in the networks increases. Our estimator does not require a known upper bound on this number to be consistent. Furthermore, we show that these consistency results hold in both dense and sparse regimes, making the penalized KT estimator robust across various network configurations. We illustrate its performance on synthetic datasets.
format Preprint
id arxiv_https___arxiv_org_abs_2502_03848
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Consistent model selection in a collection of stochastic block models
Arts, Lucie
Statistics Theory
We introduce the penalized Krichevsky-Trofimov (KT) estimator as a convergent method for estimating the number of nodes clusters when observing multiple networks within both multi-layer and dynamic Stochastic Block Models. We establish the consistency of the KT estimator, showing that it converges to the correct number of clusters in both types of models when the number of nodes in the networks increases. Our estimator does not require a known upper bound on this number to be consistent. Furthermore, we show that these consistency results hold in both dense and sparse regimes, making the penalized KT estimator robust across various network configurations. We illustrate its performance on synthetic datasets.
title Consistent model selection in a collection of stochastic block models
topic Statistics Theory
url https://arxiv.org/abs/2502.03848