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New method enables exact community recovery in directed stochastic block models

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method for exact community recovery in sparse directed stochastic block models. The approach utilizes neighborhood smoothing of connection-probability profiles, clustering vertices based on their estimated outgoing connection-probability profiles. This method establishes a finite-sample uniform row-wise error bound and demonstrates consistency, enabling exact recovery when profile separation exceeds estimation error. The technique accommodates vanishing sparsity factors, asymmetric probability matrices, and a diverging number of communities, with numerical studies and a neuronal connectome application illustrating its behavior. AI

IMPACT Introduces a novel statistical method for network analysis that could inform future AI research in community detection and graph-based learning.

RANK_REASON Academic paper detailing a new statistical method. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

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

New method enables exact community recovery in directed stochastic block models

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Academic paper detailing a new statistical method. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Behzad Aalipur, Yichen Qin ·

    Exact Recovery by Neighborhood Smoothing in Directed Stochastic Block Models

    arXiv:2601.16427v3 Announce Type: replace-cross Abstract: We study exact community recovery in sparse directed stochastic block models using neighborhood smoothing of connection-probability profiles. The proposed method clusters vertices according to their estimated outgoing conn…

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