Asymptotic Enumeration of Graph Classes with Many Components
Konstantinos Panagiotou, Leon Ramzews

TL;DR
This paper develops a probabilistic framework to asymptotically enumerate graph classes with many components, revealing phase transitions and explicit formulas for the number of such graphs with given vertices and components.
Contribution
It introduces a novel probabilistic approach using Boltzmann generators to derive asymptotic formulas for graph classes with many components, including phase transition analysis.
Findings
Asymptotic enumeration formulas with explicit functions of
Identification of a critical * where behavior changes
Application of probabilistic methods to combinatorial enumeration
Abstract
We consider graph classes in which every graph has components in a class of connected graphs. We provide a framework for the asymptotic study of , the number of graphs in with vertices and components, where . Assuming that the number of graphs with vertices in satisfies \begin{align*} \lvert \mathcal{C}_n\rvert\sim b n^{-(1+\alpha)}\rho^{-n}n!, \quad n\to \infty \end{align*} for some and -- a property commonly encountered in graph enumeration -- we show that \begin{align*} \lvert\mathcal{G}_{n,N}\rvert\sim c(\lambda) n^{f(\lambda)} (\log n)^{g(\lambda)} \rho^{-n}h(\lambda)^{N}\frac{n!}{N!}, \quad n\to \infty \end{align*} for explicitly given and . These functions are piecewise…
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
TopicsMarkov Chains and Monte Carlo Methods · Stochastic processes and statistical mechanics · Random Matrices and Applications
