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Sharp thresholds for higher powers of Hamilton cycles in random graphs

Tamás Makai, Matija Pasch, Kalina Petrova, Leon Schiller

TL;DR

This paper pinpoints the sharp threshold for the existence of the $k$-th power of a Hamilton cycle in $G(n,p)$ for all $k\ge 4$, proving $p^*=(e/n)^{1/k}$ is the precise threshold. The authors extend Riordan's second-moment framework by decomposing the variance into contributions of subgraphs of the target $H$ and crucially separating sparse and dense subgraphs to tighten the bounds and determine the exact constant. A central innovation is the detailed analysis of the densest subgraphs of $H$, showing that $H_s$ are the densest on $s$ vertices and that the density increases with $s$, culminating in $H$ being the densest connected subgraph. By bounding the contributions from sparse completions and dense completions with refined embeddings-count arguments and tail bounds, the paper closes the gap between the first-moment lower bound and the second-moment upper bound, delivering a complete sharp-threshold picture with the correct constant. The results cement the understanding of high-power Hamiltonian structures in random graphs and inform future approaches to related spanning subgraph thresholds.

Abstract

For $k \geq 4$, we establish that $p = (e/n)^{1/k}$ is a sharp threshold for the existence of the $k$-th power $H$ of a Hamilton cycle in the binomial random graph model. Our proof builds upon an approach by Riordan based on the second moment method, which previously established a weak threshold for $H$. This method expresses the second moment bound through contributions of subgraphs of $H$, with two key quantities: the number of copies of each subgraph in $H$ and the subgraphs' densities. We control these two quantities more precisely by carefully restructuring Riordan's proof and treating sparse and dense subgraphs of $H$ separately. This allows us to determine the exact constant in the threshold.

Sharp thresholds for higher powers of Hamilton cycles in random graphs

TL;DR

This paper pinpoints the sharp threshold for the existence of the -th power of a Hamilton cycle in for all , proving is the precise threshold. The authors extend Riordan's second-moment framework by decomposing the variance into contributions of subgraphs of the target and crucially separating sparse and dense subgraphs to tighten the bounds and determine the exact constant. A central innovation is the detailed analysis of the densest subgraphs of , showing that are the densest on vertices and that the density increases with , culminating in being the densest connected subgraph. By bounding the contributions from sparse completions and dense completions with refined embeddings-count arguments and tail bounds, the paper closes the gap between the first-moment lower bound and the second-moment upper bound, delivering a complete sharp-threshold picture with the correct constant. The results cement the understanding of high-power Hamiltonian structures in random graphs and inform future approaches to related spanning subgraph thresholds.

Abstract

For , we establish that is a sharp threshold for the existence of the -th power of a Hamilton cycle in the binomial random graph model. Our proof builds upon an approach by Riordan based on the second moment method, which previously established a weak threshold for . This method expresses the second moment bound through contributions of subgraphs of , with two key quantities: the number of copies of each subgraph in and the subgraphs' densities. We control these two quantities more precisely by carefully restructuring Riordan's proof and treating sparse and dense subgraphs of separately. This allows us to determine the exact constant in the threshold.

Paper Structure

This paper contains 20 sections, 19 theorems, 51 equations.

Key Result

Theorem 1

For all $k\geq 4$, we have that $p^*=(e/n)^{1/k}$ is a sharp threshold for the existence of a $k$-th power $H$ of a Hamilton cycle in $G(n,p)$. That is, for all $\varepsilon>0$ and all $p\le(1-\varepsilon)p^*$, there is whpA sequence $E_n$ of events holds with high probability (whp), if the probabil

Theorems & Definitions (35)

  • Theorem 1
  • Theorem 2
  • Lemma 3: Lemma 4.2 in riordan2000
  • Lemma 4
  • Lemma 5
  • Lemma 6
  • Proposition 7
  • Proposition 8
  • Proposition 9
  • proof : Proof of Theorem \ref{['thm:main_uniform']}
  • ...and 25 more