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.
