<ul data-eligibleForWebStory="false"><li>The paper presents evidence of computational hardness for two problems based on a strengthened low-degree conjecture in random graphs.</li><li>The study addresses the matching recovery problem in sparse correlated Erdős-Rényi graphs and the detection problem between correlated sparse stochastic block models and independent stochastic block models.</li><li>The research utilizes 'algorithmic contiguity' to establish bounds on low-degree advantage between probability measures, enabling reductions between different inference tasks.</li><li>The findings offer insights into computational complexity in correlated random graphs and provide a framework for addressing related inference problems.</li></ul>

Algorithmic contiguity from low-degree conjecture and applications in correlated random graphs

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