The scaling limit of random cubic planar graphs

Abstract We study the random cubic planar graph with an even number of vertices. We show that the Brownian map arises as Gromov–Hausdorff–Prokhorov scaling limit of as tends to infinity, after rescaling distances by for a specific constant . This is the first time a model of random graphs that are not embedded into the plane is shown to converge to the Brownian map. Our approach features a new method that allows us to relate distances on random graphs to first‐passage percolation distances on their 3‐connected core.

Authors

Institutions

Publication Details

Journal
Journal of the London Mathematical Society
Published
2024-11-01
DOI
https://doi.org/10.1112/jlms.70018
Citations
1
Primary Topic
Stochastic processes and statistical mechanics
Type
article
Field-Weighted Citation Impact
0.89

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The scaling limit of random cubic planar graphs

Benedikt Stufler
1 citations
Journal of the London Mathematical Society
Stochastic processes and statistical mechanics
0.89
article

The scaling limit of random cubic planar graphs

Benedikt Stufler
article en
1 citations

Abstract

Abstract We study the random cubic planar graph with an even number of vertices. We show that the Brownian map arises as Gromov–Hausdorff–Prokhorov scaling limit of as tends to infinity, after rescaling distances by for a specific constant . This is the first time a model of random graphs that are not embedded into the plane is shown to converge to the Brownian map. Our approach features a new method that allows us to relate distances on random graphs to first‐passage percolation distances on their 3‐connected core.

Journal of the London Mathematical SocietyVol. 110(5)
TU Wien (AT)
Austrian Science Fund
Openalex Percentile: Top 27%
Stochastic processes and statistical mechanics
0.89
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.