Monochromatic subgraphs in randomly colored dense multiplex networks

Abstract Given a sequence of graphs $G_n$ and a fixed graph H, denote by $T(H, G_n)$ the number of monochromatic copies of the graph H in a uniformly random c -coloring of the vertices of $G_n$ . In this paper we study the joint distribution of a finite collection of monochromatic graph counts in networks with multiple layers (multiplex networks). Specifically, given a finite collection of graphs $H_1, H_2, \ldots, H_d$ we derive the joint distribution of $(T(H_1, G_n^{(1)}), T(H_2, G_n^{(2)}), \ldots, T(H_d, G_n^{(d)}))$ , where $\mathbf{G}_n = (G_n^{(1)}, G_n^{(2)}, \ldots, G_n^{(d)})$ is a collection of dense graphs on the same vertex set converging in the multiplex cut-metric. The limiting distribution is the sum of two independent components: a multivariate Gaussian and a sum of independent bivariate stochastic integrals. This extends previous results on the marginal convergence of monochromatic subgraphs in a sequence of graphs to the joint convergence of a finite collection of monochromatic subgraphs in a sequence of multiplex networks. Several applications and examples are discussed.

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Publication Details

Journal
Advances in Applied Probability
Published
2026-03-05
DOI
https://doi.org/10.1017/apr.2026.10055
Primary Topic
Limits and Structures in Graph Theory
Type
article
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article

Monochromatic subgraphs in randomly colored dense multiplex networks

Mauricio Daros, Bhaswar Bikram Bhattacharya
Advances in Applied Probability
Limits and Structures in Graph Theory
article

Monochromatic subgraphs in randomly colored dense multiplex networks

Mauricio Daros, Bhaswar Bikram Bhattacharya
article en

Abstract

Abstract Given a sequence of graphs $G_n$ and a fixed graph H, denote by $T(H, G_n)$ the number of monochromatic copies of the graph H in a uniformly random c -coloring of the vertices of $G_n$ . In this paper we study the joint distribution of a finite collection of monochromatic graph counts in networks with multiple layers (multiplex networks). Specifically, given a finite collection of graphs $H_1, H_2, \ldots, H_d$ we derive the joint distribution of $(T(H_1, G_n^{(1)}), T(H_2, G_n^{(2)}), \ldots, T(H_d, G_n^{(d)}))$ , where $\mathbf{G}_n = (G_n^{(1)}, G_n^{(2)}, \ldots, G_n^{(d)})$ is a collection of dense graphs on the same vertex set converging in the multiplex cut-metric. The limiting distribution is the sum of two independent components: a multivariate Gaussian and a sum of independent bivariate stochastic integrals. This extends previous results on the marginal convergence of monochromatic subgraphs in a sequence of graphs to the joint convergence of a finite collection of monochromatic subgraphs in a sequence of multiplex networks. Several applications and examples are discussed.

Advances in Applied Probability
California University of Pennsylvania (US), Fundação Getulio Vargas (BR)
Openalex Percentile: Top 31%
Limits and Structures in Graph Theory
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