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New research explores phase transition in Stochastic Block Model with many communities

By PulseAugur Editorial · [1 sources] ·

A new research paper explores the phase transition for the Stochastic Block Model (SBM) when the number of communities exceeds the square root of the number of nodes. The study provides evidence supporting a new threshold proposed by Chin et al. (2025) for this many-communities regime. The authors prove that low-degree polynomial methods fail below this threshold across all graph densities and demonstrate that community recovery is possible above it, even in moderately sparse regimes, by analyzing specific motif occurrences. AI

IMPACT This research refines understanding of community detection in complex networks, potentially impacting graph-based machine learning algorithms.

RANK_REASON The cluster contains an academic paper detailing theoretical advancements in statistical machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New research explores phase transition in Stochastic Block Model with many communities

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The cluster contains an academic paper detailing theoretical advancements in statistical machine learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Alexandra Carpentier, Christophe Giraud, Nicolas Verzelen ·

    Phase Transition for Stochastic Block Model with more than $\sqrt{n}$ Communities

    arXiv:2509.15822v3 Announce Type: replace Abstract: Predictions from statistical physics postulate that recovery of the communities in the Stochastic Block Model (SBM) with a fixed number $K$ of communities is possible in polynomial time above, and only above, the Kesten-Stigum (…

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