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arXiv 2403.20185math.PR
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随机朋友树

Random friend trees

Louigi Addario Berry, Simon Briend, Luc Devroye, Serte Donderwinkel, Céline Kerriou, Gábor Lugosi

AI总结:

本文研究具有完全重定向的随机朋友树模型,证明其局部连接机制导致宏观枢纽与大量叶子共存的结构,并分析了邻域、直径和度分布等性质。

摘要:

我们研究一种具有完全重定向特征的随机递归树模型,该模型被称为随机朋友树,由 Saramäki 和 Kaski 引入。顶点以顺序方式逐个添加:选择一个已有的目标顶点,并均匀随机地连接到它的一个邻居(或朋友)。该模型具有有趣的涌现性质,例如高度偏斜的度序列。与优先连接模型相比,这些涌现现象源于局部而非全局的连接机制。所得树的结构也与优先连接树和均匀随机递归树显著不同:每条边都关联到一个度渐近线性的宏观枢纽,并且在大小为 n 的树中,以高概率除至多 n^{9/10} 个顶点外,其余所有顶点都是叶子。我们证明了关于固定顶点和边的邻域的各种结果,并研究了直径和度分布等宏观性质,从而深入揭示了树的整体结构。我们还提出了关于该模型及相关模型的一系列开放问题。

英文摘要:

We study a random recursive tree model featuring complete redirection called the random friend tree and introduced by Saramäki and Kaski. Vertices are attached in a sequential manner one by one by selecting an existing target vertex and connecting to one of its neighbours (or friends), chosen uniformly at random. This model has interesting emergent properties, such as a highly skewed degree sequence. In contrast to the preferential attachment model, these emergent phenomena stem from a local rather than a global attachment mechanism. The structure of the resulting tree is also strikingly different from both the preferential attachment tree and the uniform random recursive tree: every edge is incident to a macro-hub of asymptotically linear degree, and with high probability all but at most $n^{9/10}$ vertices in a tree of size $n$ are leaves. We prove various results on the neighbourhood of fixed vertices and edges, and we study macroscopic properties such as the diameter and the degree distribution, providing insights into the overall structure of the tree. We also present a number of open questions on this model and related models.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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