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arXiv 2405.04417math.PRmath-phmath.COmath.MP
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$W$-随机谱测度的大偏差原理

The Large Deviation Principle for $W$-random spectral measures

Mahya Ghandehari, Georgi S. Medvedev

AI总结:

本文基于$W$-随机图的LDP证明随机对称Hilbert–Schmidt积分算子谱测度的LDP,揭示graphon大偏差对特征值和特征空间的影响。

摘要:

$W$-随机图为大型随机网络建模提供了一个灵活的框架。利用文献[9]中关于$W$-随机图的大偏差原理(LDP),我们证明了相应一类随机对称 Hilbert–Schmidt 积分算子的 LDP。我们的主要结果描述了底层随机 graphon 的大偏差如何影响积分算子的特征值与特征空间。为证明该 LDP,我们论证了与积分算子相关的谱测度对底层 graphon 的连续依赖性,并运用收缩原理。为说明所得结果,我们得到了对应于某些随机图序列的积分算子特征值的首阶渐近式。这些例子给出了若干具有代表性的情形,展示积分算子的特征值与特征空间如何受到大偏差影响。文中还指出了这些观察对动力系统分岔分析和图信号处理的潜在意义。

英文摘要:

The $W$-random graphs provide a flexible framework for modeling large random networks. Using the Large Deviation Principle (LDP) for $W$-random graphs from [9], we prove the LDP for the corresponding class of random symmetric Hilbert-Schmidt integral operators. Our main result describes how the eigenvalues and the eigenspaces of the integral operator are affected by the large deviations in the underlying random graphon. To prove the LDP, we demonstrate continuous dependence of the spectral measures associated with integral operators on the underlying graphons and use the Contraction Principle. To illustrate our results, we obtain leading order asymptotics of the eigenvalues of the integral operators corresponding to certain random graph sequences. These examples suggest several representative scenarios of how the eigenvalues and the eigenspaces of the integral operators are affected by large deviations. Potential implications of these observations for bifurcation analysis of Dynamical Systems and Graph Signal Processing are indicated.

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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