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Diameter in ultra‐small scale‐free random graphs

机译:超小无标随机图中的直径

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摘要

It is well known that many random graphs with infinite variance degrees are ultra‐small. More precisely, for configuration models and preferential attachment models where the proportion of vertices of degree at least k is approximately k −(τ − 1) with τ ∈ (2,3), typical distances between pairs of vertices in a graph of size n are asymptotic to 2loglogn|log(τ2)| and 4loglogn|log(τ2)|, respectively. In this paper, we investigate the behavior of the diameter in such models. We show that the diameter is of order loglogn precisely when the minimal forward degree d fwd of vertices is at least 2. We identify the exact constant, which equals that of the typical distances plus 2/logdfwd. Interestingly, the proof for both models follows identical steps, even though the models are quite different in nature.
机译:众所周知,许多具有无限方差度的随机图都是超小的。更准确地说,对于度数至少为k的顶点比例约为k -(τ-1)且τ∈(2,3)的配置模型和优先依附模型,成对的典型距离大小为n的图中的顶点渐近到 2 日志 日志 n | 日志 τ 2 | < mn> 4 日志 日志 n | 日志 τ -< / mo> 2 | 。在本文中,我们研究了这种模型中直径的行为。我们显示直径为 log log n 。确定确切的常数,该常数等于典型距离的常数加上 2 / 日志 d fwd 。有趣的是,即使两个模型本质上完全不同,两个模型的证明也遵循相同的步骤。

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