We first consider irreducible critical multitype Bienaymé trees and extend the results to the case, when they possess a critical irreducible component with attached subcritical components. We study these trees under two distinct conditioning frameworks: first, conditioning on the value of a linear combination of the numbers of vertices of given types; and second, conditioning on the precise number of vertices belonging to a selected subset of types. We prove that, under a finite exponential moment condition, the scaling limit as the tree size tends to infinity is given by the Brownian Continuum Random Tree. Additionally, we establish strong non-asymptotic tail bounds for the height of such trees. Our main tools include a flattening operation applied to multitype trees and sharp estimates regarding the structure of monotype trees with a given sequence of degrees.

This work is devoted to the analysis of a Gibbs partition model, also known as a composition scheme. We consider a natural new condition on the component weights. It leads to a new behavior for the total number of components. We discover a condensation phenomenon, producing a unique giant component comprising almost the entire mass. Additionally, we prove a point process limit describing the asymptotic size of the non-maximal components exhibiting a sublinear power-law growth. A particular motivation for our article stems from applications, ranging from simple random walks in the cube, over lattice paths models in the plane, pairs of directed random walks, over to urn models and card guessing games.

We introduce classes of supergraphs and superpermutations with novel universal graphon and permuton limiting objects whose construction involves the two-parameter Poisson-Dirichlet process introduced by Pitman and Yor (1997). We demonstrate the universality of these limiting objects through general invariance principles in a heavy-tailed regime and establish a comprehensive phase diagram for the asymptotic shape of superstructures.

Abstract. We create a novel connection between Boltzmann sampling methods and Devroye’s algorithm to develop highly efficient sampling procedures that generate objects from important combinatorial classes with a given size [Formula: see text] in expected time [Formula: see text]. This performance is best possible and significantly improves the state of the art for samplers of subcritical graph classes (such as cactus graphs, outerplanar graphs, and series-parallel graphs), subcritical substitution-closed classes of permutations, Bienaymé–Galton–Watson trees conditioned on their number of leaves, and several further examples. Our approach allows for this high level of universality, as it applies in general to classes admitting bijective encodings by so-called enriched trees, which are rooted trees with additional structures on the offspring of each node.

The main goal of this paper is to determine the asymptotic behavior of the number X_n of cut-vertices in random planar maps with n edges. It is shown that X_n/n → c in probability (for some explicit c>0). For so-called subcritial subclasses of planar maps like outerplanar maps we obtain a central limit theorem, too.

We prove limit theorems describing the asymptotic behaviour of a typical vertex in random simply generated trees as their sizes tends to infinity. In the standard case of a critical Galton-Watson tree conditioned to be large, the limit is the invariant random sin-tree constructed by Aldous (1991). Our main contribution lies in the condensation regime where vertices of macroscopic degree appear. Here we describe in complete generality the asymptotic local behaviour from a random vertex up to its first ancestor with "large" degree. Beyond this distinguished ancestor, different behaviours may occur, depending on the branching weights. In a subregime of complete condensation, we obtain convergence toward a novel limit tree, that describes the asymptotic shape of the vicinity of the full path from a random vertex to the root vertex. This includes the important case where the offspring distribution follows a power law up to a factor that varies slowly at infinity.