Scaling limit of the invasion percolation cluster on a regular tree

We prove existence of the scaling limit of the invasion percolation cluster (IPC) on a regular tree. The limit is a random real tree with a single end. The contour and height functions of the limit are described as certain diffusive stochastic processes. This convergence allows us to recover and make precise certain asymptotic results for the IPC. In particular, we relate the limit of the rescaled level sets of the IPC to the local time of the scaled height function.Comment: Published in at http://dx.doi.org/10.1214/11-AOP731 the Annals of Probability (http://www.imstat.org/aop/) by the Institute of Mathematical Statistics (http://www.imstat.org

A decorated tree approach to random permutations in substitution-closed classes

We establish a novel bijective encoding that represents permutations as forests of decorated (or enriched) trees. This allows us to prove local convergence of uniform random permutations from substitution-closed classes satisfying a criticality constraint. It also enables us to reprove and strengthen permuton limits for these classes in a new way, that uses a semi-local version of Aldous' skeleton decomposition for size-constrained Galton--Watson trees.Comment: New version including referee's corrections, accepted for publication in Electronic Journal of Probabilit

Some families of increasing planar maps

Stack-triangulations appear as natural objects when one wants to define some increasing families of triangulations by successive additions of faces. We investigate the asymptotic behavior of rooted stack-triangulations with $2n$ faces under two different distributions. We show that the uniform distribution on this set of maps converges, for a topology of local convergence, to a distribution on the set of infinite maps. In the other hand, we show that rescaled by $n^{1/2}$, they converge for the Gromov-Hausdorff topology on metric spaces to the continuum random tree introduced by Aldous. Under a distribution induced by a natural random construction, the distance between random points rescaled by $(6/11)\log n$ converge to 1 in probability. We obtain similar asymptotic results for a family of increasing quadrangulations

Local weak convergence and propagation of ergodicity for sparse networks of interacting processes

We study the limiting behavior of interacting particle systems indexed by large sparse graphs, which evolve either according to a discrete time Markov chain or a diffusion, in which particles interact directly only with their nearest neighbors in the graph. To encode sparsity we work in the framework of local weak convergence of marked (random) graphs. We show that the joint law of the particle system varies continuously with respect to local weak convergence of the underlying graph. In addition, we show that the global empirical measure converges to a non-random limit, whereas for a large class of graph sequences including sparse Erd\"{o}s-R\'{e}nyi graphs and configuration models, the empirical measure of the connected component of a uniformly random vertex converges to a random limit. Finally, on a lattice (or more generally an amenable Cayley graph), we show that if the initial configuration of the particle system is a stationary ergodic random field, then so is the configuration of particle trajectories up to any fixed time, a phenomenon we refer to as "propagation of ergodicity". Along the way, we develop some general results on local weak convergence of Gibbs measures in the uniqueness regime which appear to be new.Comment: 46 pages, 1 figure. This version of the paper significantly extends the convergence results for diffusions in v1, and includes new results on propagation of ergodicity and discrete-time models. The complementary results in v1 on autonomous characterization of marginal dynamics of diffusions on trees and generalizations thereof are now presented in a separate paper arXiv:2009.1166

A branching process with coalescence to model random phylogenetic networks

We introduce a biologically natural, mathematically tractable model of random phylogenetic network to describe evolution in the presence of hybridization. One of the features of this model is that the hybridization rate of the lineages correlates negatively with their phylogenetic distance. We give formulas / characterizations for quantities of biological interest that make them straightforward to compute in practice. We show that the appropriately rescaled network, seen as a metric space, converges to the Brownian continuum random tree, and that the uniformly rooted network has a local weak limit, which we describe explicitly