Probability-graphons: Limits of large dense weighted graphs
Innovations in Graph Theory, Volume 2 (2025), pp. 25-117.

We introduce probability-graphons which are probability kernels that generalize graphons to the case of weighted graphs. Probability-graphons appear as the limit objects to study sequences of large weighted graphs whose distribution of subgraph sampling converge. The edge-weights are taken from a general Polish space, which also covers the case of decorated graphs. Here, graphs can be either directed or undirected. Starting from a distance $d_\mathrm{m}$ inducing the weak topology on measures, we define a cut distance on probability-graphons, making it a Polish space, and study the properties of this cut distance. In particular, we exhibit a tightness criterion for probability-graphons related to relative compactness in the cut distance. We also prove that under some conditions on the distance $d_\mathrm{m}$, which are satisfied for some well-know distances like the Lévy–Prokhorov distance, and the Fortet–Mourier and Kantorovitch–Rubinshtein norms, the topology induced by the cut distance on the space of probability-graphons is independent from the choice of $d_\mathrm{m}$. Eventually, we prove that this topology coincides with the topology induced by the convergence in distribution of the sampled subgraphs.

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DOI: 10.5802/igt.7
Classification: 05C80, 60B10
Keywords: random graphs, stochastic networks, graphons, probability-graphons, dense graph limits, weighted graphs, decorated graphs

Abraham, Romain 1; Delmas, Jean-François 2; Weibel, Julien 3

1 Institut Denis Poisson Université d’Orléans, Université de Tours, CNRS France
2 CERMICS Ecole des Ponts France
3 Institut Denis Poisson Université d’Orléans, Université de Tours, CNRS France and CERMICS Ecole des Ponts France
License: CC-BY 4.0
Copyrights: The authors retain unrestricted copyrights and publishing rights
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Abraham, Romain; Delmas, Jean-François; Weibel, Julien. Probability-graphons: Limits of large dense weighted graphs. Innovations in Graph Theory, Volume 2 (2025), pp. 25-117. doi : 10.5802/igt.7. https://igt.centre-mersenne.org/articles/10.5802/igt.7/

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