The distance profile of rooted and unrooted simply generated trees

IF 0.9 4区 数学 Q3 COMPUTER SCIENCE, THEORY & METHODS Combinatorics, Probability & Computing Pub Date : 2020-09-01 DOI:10.1017/s0963548321000304
Gabriel Berzunza Ojeda, S. Janson
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Abstract

It is well known that the height profile of a critical conditioned Galton–Watson tree with finite offspring variance converges, after a suitable normalisation, to the local time of a standard Brownian excursion. In this work, we study the distance profile, defined as the profile of all distances between pairs of vertices. We show that after a proper rescaling the distance profile converges to a continuous random function that can be described as the density of distances between random points in the Brownian continuum random tree. We show that this limiting function a.s. is Hölder continuous of any order $\alpha<1$ , and that it is a.e. differentiable. We note that it cannot be differentiable at 0, but leave as open questions whether it is Lipschitz, and whether it is continuously differentiable on the half-line $(0,\infty)$ . The distance profile is naturally defined also for unrooted trees contrary to the height profile that is designed for rooted trees. This is used in our proof, and we prove the corresponding convergence result for the distance profile of random unrooted simply generated trees. As a minor purpose of the present work, we also formalize the notion of unrooted simply generated trees and include some simple results relating them to rooted simply generated trees, which might be of independent interest.
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有根树和无根树的距离曲线
众所周知,具有有限子嗣方差的临界条件高尔顿-沃森树的高度轮廓,经过适当的归一化后,收敛于标准布朗偏移的局部时间。在这项工作中,我们研究了距离轮廓,定义为顶点对之间所有距离的轮廓。我们证明,经过适当的重新缩放后,距离轮廓收敛为一个连续的随机函数,该函数可以描述为布朗连续统随机树中随机点之间的距离密度。我们证明了这个极限函数a.s.是任意阶$\alpha<1$的Hölder连续函数,并且它是a.e.可微函数。我们注意到它在0处不可微,但对于它是否为Lipschitz,以及它在半线上是否连续可微,仍有疑问$(0,\infty)$。与为有根树木设计的高度轮廓相反,距离轮廓自然也为无根树木定义。在我们的证明中使用了这一方法,并证明了随机无根简单生成树距离轮廓的相应收敛结果。作为本工作的次要目的,我们还形式化了无根简单生成树的概念,并包含了一些将它们与有根简单生成树相关的简单结果,这可能是独立的兴趣。
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来源期刊
Combinatorics, Probability & Computing
Combinatorics, Probability & Computing 数学-计算机:理论方法
CiteScore
2.40
自引率
11.10%
发文量
33
审稿时长
6-12 weeks
期刊介绍: Published bimonthly, Combinatorics, Probability & Computing is devoted to the three areas of combinatorics, probability theory and theoretical computer science. Topics covered include classical and algebraic graph theory, extremal set theory, matroid theory, probabilistic methods and random combinatorial structures; combinatorial probability and limit theorems for random combinatorial structures; the theory of algorithms (including complexity theory), randomised algorithms, probabilistic analysis of algorithms, computational learning theory and optimisation.
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