Model-free algorithms for fast node clustering in SBM type graphs and application to social role inference in animals

• URL: http://arxiv.org/abs/2509.15989v1
• Date: Fri, 19 Sep 2025 13:57:17 GMT
• Title: Model-free algorithms for fast node clustering in SBM type graphs and application to social role inference in animals
• Authors: Bertrand Cloez, Adrien Cotil, Jean-Baptiste Menassol, Nicolas Verzelen,
• Abstract summary: We propose a novel family of model-free algorithms for node clustering and parameter inference in graphs generated from the Block Model (SBM)<n>We benchmark our methods against state-of-the-art techniques, demonstrating significantly faster computation times with the lower order of estimation error.<n>We validate the practical relevance of our algorithms by applying them to empirical network data from behavioral ecology.
• Score: 26.41190755089919
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a novel family of model-free algorithms for node clustering and parameter inference in graphs generated from the Stochastic Block Model (SBM), a fundamental framework in community detection. Drawing inspiration from the Lloyd algorithm for the $k$-means problem, our approach extends to SBMs with general edge weight distributions. We establish the consistency of our estimator under a natural identifiability condition. Through extensive numerical experiments, we benchmark our methods against state-of-the-art techniques, demonstrating significantly faster computation times with the lower order of estimation error. Finally, we validate the practical relevance of our algorithms by applying them to empirical network data from behavioral ecology.

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