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New method characterizes recovery thresholds for hidden weighted sparse graphs

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a unified characterization for the information-theoretic limits of recovering hidden structures within noisy, high-dimensional data. The study focuses on identifying an unknown graph embedded within a randomly weighted complete graph, where edge weights follow specific distributions. The findings connect KL divergence to the threshold of the Erdős-Rényi random graph model and demonstrate an All-or-Nothing phenomenon for certain distributions like Gaussian. AI

RANK_REASON The cluster contains a research paper published on arXiv detailing a new mathematical method for statistical inference. [lever_c_demoted from research: ic=2 ai=0.4]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New method characterizes recovery thresholds for hidden weighted sparse graphs

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The cluster contains a research paper published on arXiv detailing a new mathematical method for statistical inference. [lever_c_demoted from research: ic=2 ai=0.4]
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Zhe Hou, Jingcheng Liu ·

    Recovery thresholds for hidden weighted sparse graphs

    arXiv:2606.14335v1 Announce Type: cross Abstract: Recovering structural information from noisy high-dimensional data is a fundamental task in statistical inference. We investigate the recovery thresholds for a graph hidden in a randomly weighted complete graph. Specifically, an u…

  2. arXiv cs.LG TIER_1 English(EN) · Jingcheng Liu ·

    Recovery thresholds for hidden weighted sparse graphs

    Recovering structural information from noisy high-dimensional data is a fundamental task in statistical inference. We investigate the recovery thresholds for a graph hidden in a randomly weighted complete graph. Specifically, an unknown graph $H^* \in H_n$ is chosen uniformly at …

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