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通过中间特征值统计探索复杂网络中β-高斯系综的普遍性。

Exploring universality of the β-Gaussian ensemble in complex networks via intermediate eigenvalue statistics.

作者信息

Mishra Ankit, Cheong Kang Hao

机构信息

Science, Mathematics and Technology, Singapore University of Technology and Design, 8 Somapah Road, S487372, Singapore.

School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, S637371, Singapore.

出版信息

Phys Rev E. 2024 Jan;109(1-1):014218. doi: 10.1103/PhysRevE.109.014218.

Abstract

The eigenvalue statistics are an important tool to capture localization to delocalization transition in physical systems. Recently, a β-Gaussian ensemble is being proposed as a single parameter to describe the intermediate eigenvalue statistics of many physical systems. It is critical to explore the universality of a β-Gaussian ensemble in complex networks. In this work, we study the eigenvalue statistics of various network models, such as small-world, Erdős-Rényi random, and scale-free networks, as well as in comparing the intermediate level statistics of the model networks with that of a β-Gaussian ensemble. It is found that the nearest-neighbor eigenvalue statistics of all the model networks are in excellent agreement with the β-Gaussian ensemble. However, the β-Gaussian ensemble fails to describe the intermediate level statistics of higher order eigenvalue statistics, though there is qualitative agreement till n<4. Additionally, we show that the nearest-neighbor eigenvalue statistics of the β-Gaussian ensemble is in excellent agreement with the intermediate higher order eigenvalue statistics of model networks.

摘要

本征值统计是捕捉物理系统中从局域化到非局域化转变的重要工具。最近,有人提出用β-高斯系综作为一个单一参数来描述许多物理系统的中间本征值统计。探索β-高斯系综在复杂网络中的普遍性至关重要。在这项工作中,我们研究了各种网络模型的本征值统计,如小世界网络、厄多斯-雷尼随机网络和无标度网络,并将模型网络的中间层统计与β-高斯系综的中间层统计进行比较。结果发现,所有模型网络的最近邻本征值统计与β-高斯系综非常吻合。然而,β-高斯系综无法描述高阶本征值统计的中间层统计,尽管直到n<4时存在定性的一致性。此外,我们表明β-高斯系综的最近邻本征值统计与模型网络的中间高阶本征值统计非常吻合。

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