特征值分解的关联度表示及其在流行病模型中的应用

IF 8.3 4区物理与天体物理 Q1 Physics and Astronomy

Progress of Theoretical and Experimental Physics Pub Date : 2023-11-02 DOI:10.1093/ptep/ptad132

Satoru Morita

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引用次数: 0

摘要

度关联在研究网络结构中起着至关重要的作用;然而，其多种形式对理解其对网络动力学的影响提出了挑战。本研究设计了一种利用特征值分解来表征度相关性的方法。此外，通过对流行病网络模型的基本复制数和类型复制数的近似，证明了该方法的适用性。研究结果阐明了程度相关性与流行行为之间的相互作用，从而有助于更深入地了解社会网络及其动态。

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Representation of degree correlation using eigenvalue decomposition and its application to epidemic models

Abstract Degree correlation plays a crucial role in studying network structures; however, its varied forms pose challenges to understanding its impact on network dynamics. This study devised a method that uses eigenvalue decomposition to characterize degree correlations. Additionally, the applicability of this method was demonstrated by approximating the basic and type reproduction numbers in an epidemic network model. The findings elucidate the interplay between degree correlations and epidemic behavior, thus contributing to a deeper understanding of social networks and their dynamics.

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来源期刊

Progress of Theoretical and Experimental Physics PHYSICS, MULTIDISCIPLINARY-PHYSICS, PARTICLES & FIELDS

CiteScore

12.00

自引率

5.70%

发文量

148

审稿时长

17 weeks

期刊介绍： Progress of Theoretical and Experimental Physics (PTEP) is an international journal that publishes articles on theoretical and experimental physics. PTEP is a fully open access, online-only journal published by the Physical Society of Japan. PTEP is the successor to Progress of Theoretical Physics (PTP), which terminated in December 2012 and merged into PTEP in January 2013. PTP was founded in 1946 by Hideki Yukawa, the first Japanese Nobel Laureate. PTEP, the successor journal to PTP, has a broader scope than that of PTP covering both theoretical and experimental physics. PTEP mainly covers areas including particles and fields, nuclear physics, astrophysics and cosmology, beam physics and instrumentation, and general and mathematical physics.