Markov properties for the vertical edge profile in random labelled trees
Alexis Metz-Donnadieu
TL;DR
The paper develops a general Markov-structure framework for the vertical edge profile of random labelled trees with edge increments in $\{-1,0,1\}$, using an excursion-forest decomposition to reveal a time-homogeneous Markov chain for $(X_m^+,X_m^-)_{m\ge1}$ and its size-conditioned extension to $(X_m^+,X_m^-,M_m^-)_{m\ge1}$. It provides explicit kernels for the labelled binary-tree model and connects these probabilistic objects to combinatorial enumeration via the excursion decomposition, with broader implications for random maps. The work also situates these discrete results in the context of super-Brownian motion and the Integrated Super-Brownian Excursion (ISE), outlining scaling limits and a companion paper that identifies convergence to continuous local-time processes. By bridging probabilistic decomposition with combinatorial structure, the paper offers new tools for analysing vertical profiles in labelled trees and related random-geometric models, with potential to inform SDE descriptions and universality across scaling regimes.
Abstract
We study a broad class of random labelled trees in which integer-valued labels evolve along the edges according to increments in $\{-1, 0, 1\}$. These models include e.g. branching random walks, embedded complete and incomplete binary trees, random Cayley and plane trees with uniform displacements along edges. Motivated by recent work suggesting a Markovian structure in the vertical profile of such trees, we introduce the vertical edge profile, which counts both oriented edges connecting label $k-1$ to label $k$ and oriented edges connecting label $k$ to label $k-1$. We show that the vertical edge profile forms a time-homogeneous Markov chain for a wide class of models, and this remains true (provided we enrich this process by the total mass of the tree below each label) if we condition on the total size of the tree. We give explicit transition kernels in the case of labelled incomplete binary trees, which are closely related to known enumeration formulas. To establish these results, we study a decomposition of labelled trees into excursions above and below fixed label levels, yielding a forest structure with tractable probabilistic laws. We further explain briefly how these findings connect to the theory of super-Brownian motion and the Integrated Super-Brownian Excursion (ISE). In a companion paper, we show that the vertical edge profile converges, after rescaling,to the local time and its derivative of Brownian motion indexed by the Brownian tree.
