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新方法在随机块模型中恢复Bethe Hessian特征向量

一篇新发表在arXiv上的论文详细介绍了一种在随机块模型的Kesten-Stigum机制下恢复Bethe Hessian的异常特征向量的方法。该技术将先前的恢复保证扩展到任何大于一的固定共同期望度数的情景,其性能与非回溯谱方法相当。该方法包括识别负异常特征值的递归公式,并使用极限泊松树及其格林函数来构建近似特征向量。 AI

排序理由 该集群包含一篇关于统计学新理论方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.4]

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新方法在随机块模型中恢复Bethe Hessian特征向量

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该集群包含一篇关于统计学新理论方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.4]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Dingding Dong, Theo McKenzie ·

    Bethe Hessian Recovery through the Kesten-Stigum Regime

    arXiv:2610.41802v1 Announce Type: cross Abstract: We show that outlier eigenvectors of the Bethe Hessian achieve weak recovery in the stochastic block model down to the Kesten-Stigum threshold, for every fixed common expected degree $d>1$. This extends previous recovery guarantee…