Subcritical annulus crossing in spatial random graphs
Pith reviewed 2026-05-23 17:40 UTC · model grok-4.3
The pith
Annuli in spatial random graphs are crossed by a single long edge or not at all below a critical intensity.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
For general continuum percolation models with the stated properties, annuli are either not crossed for small intensities or crossed by a single edge. The critical annulus-crossing intensity hat lambda_c is determined by the occurrence of long edges, and this relation is sharp under a modicum of independence. The proof provides a direct link between the decay rates of annulus-crossing probabilities and long-edge probabilities through a multiscale renormalization.
What carries the argument
The critical annulus-crossing intensity hat lambda_c, defined as the infimum of intensities where annuli can be crossed and shown to coincide with the onset of long edges.
Load-bearing premise
The models obey sparseness, translation invariance, and spatial decorrelation, and for the sharpness part, they possess a modicum of independence.
What would settle it
An observation of an annulus being crossed by a chain of multiple short edges at an intensity where the probability of any long edge is zero would contradict the claim that crossings require long edges.
Figures
read the original abstract
We consider general continuum percolation models obeying sparseness, translation invariance, and spatial decorrelation. In particular, this includes models constructed on general point sets other than the standard Poisson point process or the Bernoulli-percolated lattice. Moreover, in our setting the existence of an edge may depend not only on the two end vertices but also on a surrounding vertex set and models are included that are not monotone in some of their parameters. We study the critical annulus-crossing intensity $\widehat{\lambda}_{c}$, which is smaller or equal to the classical critical percolation intensity $\lambda_{c}$ and derive a condition for $\widehat{\lambda}_{c}>0$ by relating the crossing of annuli to the occurrence of long edges. This condition is sharp for models that have a modicum of independence. In a nutshell, our result states that annuli are either not crossed for small intensities or crossed by a single edge. Our proof rests on a multiscale argument that further allows us to directly describe the decay of the annulus-crossing probability with the decay of long edges probabilities. We apply our result to a number of examples from the literature. Most importantly, we extensively discuss the weight-dependent random connection model in a generalised version, for which we derive sufficient conditions for the presence or absence of long edges that are typically easy to check. These conditions are built on a decay coefficient $\zeta$ that has recently seen some attention due to its importance for various proofs of global graph properties.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper considers general continuum percolation models obeying sparseness, translation invariance, and spatial decorrelation (including non-Poisson point sets and non-monotone models). It defines the critical annulus-crossing intensitywidehat{lambda}_c <= lambda_c and derives a condition for widehat{lambda}_c > 0 by relating annulus-crossing events to long-edge probabilities via a multiscale argument; the condition is sharp for models possessing a modicum of independence. The main conclusion is that annuli are either not crossed at small intensities or crossed by a single edge, with the argument also describing the decay rate of crossing probabilities. The result is applied to the weight-dependent random connection model, yielding checkable conditions on a decay coefficient zeta.
Significance. If the result holds, it supplies a general, hypothesis-checkable criterion for subcritical annulus crossing that applies beyond standard Poisson or lattice models and handles non-monotonicity. The multiscale argument that directly ties crossing decay to long-edge decay, together with the explicit sufficient conditions on zeta for the weight-dependent model, strengthens the toolkit for analyzing global properties of spatial random graphs.
minor comments (2)
- [Abstract] Abstract: the claim of a 'derivation and sharpness' is stated without any displayed equations, error bounds, or verification that the multiscale argument closes; while this is acceptable in an abstract, the introduction or §2 should supply at least one key relation (e.g., the inequality linking crossing probability to long-edge probability) so readers can assess the argument before the full proof.
- [Abstract] Abstract, first paragraph: 'modicum of independence' is used to delimit sharpness but is not defined or exemplified at the point of first use; a brief parenthetical or forward reference to the precise assumption (e.g., a weak FKG or independence condition on edge indicators) would improve readability.
Simulated Author's Rebuttal
We thank the referee for their positive assessment of the manuscript, accurate summary of our results, and recommendation of minor revision. No specific major comments were raised.
Circularity Check
No significant circularity; derivation self-contained under explicit hypotheses
full rationale
The paper states a conditional theorem relating annulus-crossing events to long-edge probabilities via a multiscale argument, under explicitly listed assumptions (sparseness, translation invariance, spatial decorrelation) that are independent of the target result. No equations reduce by construction to fitted parameters, self-definitions, or load-bearing self-citations; the sharpness claim is restricted to models with additional independence and the applications to examples (including weight-dependent random connection models) rely on checkable decay conditions rather than internal renormalization. The central claim therefore does not collapse to its inputs.
Axiom & Free-Parameter Ledger
axioms (2)
- domain assumption Models obey sparseness, translation invariance, and spatial decorrelation
- domain assumption Existence of an edge may depend on surrounding vertex set and models need not be monotone
Reference graph
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