随机块段模型的极限谱分布

IF 0.9 4区 数学 Q4 PHYSICS, MATHEMATICAL
Giap Van Su, May-Ru Chen, Mei-Hui Guo, Hao-Wei Huang
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引用次数: 0

摘要

随机块模型(SBM)是Erdõs–Rényi图的扩展,在数据分析、恢复图数据中的社区结构和社交网络等众多领域都有应用。在本文中,我们考虑具有任意大小的K个社区的正规中心SBM邻接矩阵。当矩阵的大小趋于无穷大时,我们导出了极限经验谱密度函数的一个显式公式。利用Stieltjes变换和随机矩阵理论,得到了这类随机矩阵的算子范数的上界。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Limiting spectral distribution of stochastic block model

The stochastic block model (SBM) is an extension of the Erdős–Rényi graph and has applications in numerous fields, such as data analysis, recovering community structure in graph data and social networks. In this paper, we consider the normal central SBM adjacency matrix with K communities of arbitrary sizes. We derive an explicit formula for the limiting empirical spectral density function when the size of the matrix tends to infinity. We also obtain an upper bound for the operator norm of such random matrices by means of the Stieltjes transform and random matrix theory.

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来源期刊
Random Matrices-Theory and Applications
Random Matrices-Theory and Applications Decision Sciences-Statistics, Probability and Uncertainty
CiteScore
1.90
自引率
11.10%
发文量
29
期刊介绍: Random Matrix Theory (RMT) has a long and rich history and has, especially in recent years, shown to have important applications in many diverse areas of mathematics, science, and engineering. The scope of RMT and its applications include the areas of classical analysis, probability theory, statistical analysis of big data, as well as connections to graph theory, number theory, representation theory, and many areas of mathematical physics. Applications of Random Matrix Theory continue to present themselves and new applications are welcome in this journal. Some examples are orthogonal polynomial theory, free probability, integrable systems, growth models, wireless communications, signal processing, numerical computing, complex networks, economics, statistical mechanics, and quantum theory. Special issues devoted to single topic of current interest will also be considered and published in this journal.
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