3-Connected Cores In Random Planar Graphs
Nikolaos Fountoulakis, Konstantinos Panagiotou

TL;DR
This paper investigates the structure of large random biconnected planar graphs, revealing a phase transition in core sizes and establishing that such graphs typically contain a unique giant 3-connected core with high probability.
Contribution
It provides a general theorem characterizing the size distribution of cores in random biconnected graphs, especially planar ones, and identifies conditions for the emergence of a giant core.
Findings
A giant core contains approximately 76.5% of vertices in typical planar graphs.
Cores are either dominated by a large core or are all logarithmic in size.
Sharp concentration results for core counts of all sizes are established.
Abstract
The study of the structural properties of large random planar graphs has become in recent years a field of intense research in computer science and discrete mathematics. Nowadays, a random planar graph is an important and challenging model for evaluating methods that are developed to study properties of random graphs from classes with structural side constraints. In this paper we focus on the structure of random biconnected planar graphs regarding the sizes of their 3-connected building blocks, which we call cores. In fact, we prove a general theorem regarding random biconnected graphs. If B_n is a graph drawn uniformly at random from a class B of labeled biconnected graphs, then we show that with probability 1-o(1) B_n belongs to exactly one of the following categories: (i) Either there is a unique giant core in B_n, that is, there is a 0 < c < 1 such that the largest core contains ~…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsComputational Geometry and Mesh Generation · Advanced Graph Theory Research · Stochastic processes and statistical mechanics
