Detection-Recovery Gap for Planted Dense Cycles

February 13, 2023 Β· Declared Dead Β· πŸ› Annual Conference Computational Learning Theory

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Authors Cheng Mao, Alexander S. Wein, Shenduo Zhang arXiv ID 2302.06737 Category math.ST Cross-listed cs.DS, stat.ML Citations 13 Venue Annual Conference Computational Learning Theory Last Checked 6 months ago
Abstract
Planted dense cycles are a type of latent structure that appears in many applications, such as small-world networks in social sciences and sequence assembly in computational biology. We consider a model where a dense cycle with expected bandwidth $n Ο„$ and edge density $p$ is planted in an ErdΕ‘s-RΓ©nyi graph $G(n,q)$. We characterize the computational thresholds for the associated detection and recovery problems for the class of low-degree polynomial algorithms. In particular, a gap exists between the two thresholds in a certain regime of parameters. For example, if $n^{-3/4} \ll Ο„\ll n^{-1/2}$ and $p = C q = Θ(1)$ for a constant $C>1$, the detection problem is computationally easy while the recovery problem is hard for low-degree algorithms.
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