Finding Planted Cycles in a Random Graph
Julia Gaudio, Colin Sandon, Jiaming Xu, Dana Yang
TL;DR
This work studies the planted 2-factor problem in ER graphs G$(n,\lambda/n)$ with a random planted vertex subset of size $\delta n$. It establishes a sharp phase transition for almost exact recovery at the threshold $\lambda<\frac{1}{(\sqrt{2\delta}+\sqrt{1-\delta})^2}$ and proves impossibility above it. The authors introduce a generating function framework and a greedy trail-based algorithm that achieve almost exact recovery in the feasible regime, showing no computational-statistical gap relative to planted clique heuristics. The analysis combines generating-function counts of $(a,b)$-trails, branching-process techniques, and a sprinkling argument to connect trees into long balanced cycles, offering a detailed All-regime picture and highlighting open questions about intermediate regimes and generalizations.
Abstract
In this paper, we study the problem of finding a collection of planted cycles in an \ER random graph $G \sim \mathcal{G}(n, λ/n)$, in analogy to the famous Planted Clique Problem. When the cycles are planted on a uniformly random subset of $δn$ vertices, we show that almost-exact recovery (that is, recovering all but a vanishing fraction of planted-cycle edges as $n \to \infty$) is information-theoretically possible if $λ< \frac{1}{(\sqrt{2 δ} + \sqrt{1-δ})^2}$ and impossible if $λ> \frac{1}{(\sqrt{2 δ} + \sqrt{1-δ})^2}$. Moreover, despite the worst-case computational hardness of finding long cycles, we design a polynomial-time algorithm that attains almost exact recovery when $λ< \frac{1}{(\sqrt{2 δ} + \sqrt{1-δ})^2}$. This stands in stark contrast to the Planted Clique Problem, where a significant computational-statistical gap is widely conjectured.
