Graph connectivity with fixed endpoints in the random-connection model

Abstract We consider the count of subgraphs with an arbitrary configuration of endpoints in the random-connection model based on a Poisson point process on ${\mathord{\mathbb R}}^d$ . We present combinatorial expressions for the computation of the cumulants and moments of all orders of such subgraph counts, which allow us to estimate the growth of cumulants as the intensity of the underlying Poisson point process goes to infinity. As a consequence, we obtain a central limit theorem with explicit convergence rates under the Kolmogorov distance and connectivity bounds. Numerical examples are presented using a computer code in SageMath for the closed-form computation of cumulants of any order, for any type of connected subgraph, and for any configuration of endpoints in any dimension $d{\geq} 1$ . In particular, graph connectivity estimates, Gram–Charlier expansions for density estimation, and correlation estimates for joint subgraph counting are obtained.

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Publication Details

Journal
Probability in the Engineering and Informational Sciences
Published
2025-02-21
DOI
https://doi.org/10.1017/s0269964824000160
Citations
1
Primary Topic
Complex Network Analysis Techniques
Type
article
Field-Weighted Citation Impact
1.32
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article

Graph connectivity with fixed endpoints in the random-connection model

Nicolas Privault, Qingwei Liu
1 citations
Probability in the Engineering and Informational Sciences
Complex Network Analysis Techniques
1.32
article

Graph connectivity with fixed endpoints in the random-connection model

Nicolas Privault, Qingwei Liu
article en
1 citations

Abstract

Abstract We consider the count of subgraphs with an arbitrary configuration of endpoints in the random-connection model based on a Poisson point process on ${\mathord{\mathbb R}}^d$ . We present combinatorial expressions for the computation of the cumulants and moments of all orders of such subgraph counts, which allow us to estimate the growth of cumulants as the intensity of the underlying Poisson point process goes to infinity. As a consequence, we obtain a central limit theorem with explicit convergence rates under the Kolmogorov distance and connectivity bounds. Numerical examples are presented using a computer code in SageMath for the closed-form computation of cumulants of any order, for any type of connected subgraph, and for any configuration of endpoints in any dimension $d{\geq} 1$ . In particular, graph connectivity estimates, Gram–Charlier expansions for density estimation, and correlation estimates for joint subgraph counting are obtained.

Probability in the Engineering and Informational SciencesVol. 39(3)
Openalex Percentile: Top 22%
Complex Network Analysis Techniques
1.32
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