English

Detection-Recovery Gap for Planted Dense Cycles

Statistics Theory 2023-06-22 v2 Data Structures and Algorithms Machine Learning Statistics Theory

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 nτn \tau and edge density pp is planted in an Erd\H{o}s-R\'enyi graph G(n,q)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 n3/4τn1/2n^{-3/4} \ll \tau \ll n^{-1/2} and p=Cq=Θ(1)p = C q = \Theta(1) for a constant C>1C>1, the detection problem is computationally easy while the recovery problem is hard for low-degree algorithms.

Keywords

Cite

@article{arxiv.2302.06737,
  title  = {Detection-Recovery Gap for Planted Dense Cycles},
  author = {Cheng Mao and Alexander S. Wein and Shenduo Zhang},
  journal= {arXiv preprint arXiv:2302.06737},
  year   = {2023}
}

Comments

41 pages, 1 figure

R2 v1 2026-06-28T08:39:21.781Z