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This is a Quantitative Study study.

October 2, 2025Open Access

Bayesian community detection in assortative stochastic block model with unknown number of communities

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The incorporation of assortativity enhances community detection accuracy in comparison to traditional methods.
Simulation results demonstrate the effectiveness of Bayesian nonparametric approaches in identifying communities under assortative conditions.
The study applies collapsed Gibbs samplers to estimate block assignments effectively within the stochastic block model framework.
Findings indicate that enforcing assortativity can yield significant improvements in community detection outcomes.

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This analysis reveals the impact of assortativity on community detection in networks, suggesting improved outcomes via Bayesian methods.

Amongero et al. (2025) studied this question.

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