Critical random graphs and the differential equations technique

Critical random graphs and the differential equations technique
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临界随机图和微分方程技术

DOI:
10.1007/s13226-017-0249-0
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发表时间:
2018
影响因子:
0.7
通讯作者:
Bhamidi S
Bhamidi S
中科院分区:
数学4区
文献类型:
--
作者:
Bhamidi S

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在过去的几年里,已经假设了大量的随机图模型来理解经验观察网络的属性。这些模型中的大多数都带有一个参数t(通常与边缘密度有关)和一个(依赖于模型的)临界值c,用于指定何时出现巨型组件。有证据表明,对于广泛的一类模型,在矩条件下,这种涌现的性质是普遍的,看起来像经典的Erdens-Rényi随机图,以及(a)在该窗口中的组件的尺寸(所有最大分量大小的比例为2/3)和(B)分量的结构(重新比例为n-1/3)收敛到与连续随机树相关的随机分形。本说明的目的是对这一领域的最新突破进行非技术性概述,强调证明此类结果的特殊工具,称为微分方程技术,该技术首先在Wormald [52,53]的工作中开发并广泛用于概率组合学,并在[10-12]中由作者及其合作者在临界随机图的背景下开发。
Over the last few years a wide array of random graph models have been postulated to understand properties of empirically observed networks. Most of these models come with a parametert(usually related to edge density) and a (model dependent) critical timetcthat specifies when a giant component emerges. There is evidence to support that for a wide class of models, under moment conditions, the nature of this emergence is universal and looks like the classical Erdős-Rényi random graph, in the sense of the critical scaling window and (a) the sizes of the components in this window (all maximal component sizes scaling liken2/3) and (b) the structure of components (rescaled byn−1/3) converge to random fractals related to the continuum random tree. The aim of this note is to give a non-technical overview of recent breakthroughs in this area, emphasizing a particular tool in proving such results called the differential equations technique first developed and used extensively in probabilistic combinatorics in the work of Wormald [52, 53] and developed in the context of critical random graphs by the authors and their collaborators in [10–12].
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