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New adaptive strategies improve community recovery in stochastic block models

Researchers have developed new strategies for community recovery in stochastic block models, focusing on scenarios with limited and noisy data access. The study introduces adaptive querying methods that can achieve exact recovery with fewer queries than traditional uniform querying approaches. These adaptive strategies are particularly effective when combined with a subsampled copy of the network data, allowing for targeted information gathering to improve recovery accuracy. AI

RANK_REASON Academic paper published on arXiv detailing a new theoretical approach to a statistical modeling problem. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv stat.ML →

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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Sabyasachi Basu, Manuj Mukherjee, Lutz Oettershagen, Suhas Thejaswi ·

    Query-Limited Community Recovery in Stochastic Block Models

    arXiv:2606.02055v1 Announce Type: cross Abstract: We study exact community recovery in the two-community stochastic block model on $n$ vertices under limited and noisy access to network data. The learner may query a noisy neighborhood oracle that reveals each true neighbor of a q…