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

Autor:	Morita, Satoru
Rok vydání:	2023
Předmět:	Physics - Physics and Society Condensed Matter - Statistical Mechanics Nonlinear Sciences - Adaptation and Self-Organizing Systems Quantitative Biology - Populations and Evolution
Zdroj:	Progress of Theoretical and Experimental Physics, 2023, 11, 111J01 (2023)
Druh dokumentu:	Working Paper
DOI:	10.1093/ptep/ptad132
Popis:	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 complex networks and their dynamics. Comment: 5 pages, 3 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2308.05611 Zobrazit plný text záznamu View this record from Arxiv