Detection-Recovery Gap for Planted Dense Cycles
February 13, 2023 Β· Declared Dead Β· π 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 ErdΕs-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 $p = C q = Ξ(1)$ 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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