Disease spreading in complex networks: A numerical study with Principal Component Analysis

作者：

Highlights：

• A spreading disease model is set for a population in complex networks.

• The disease is analyzed in a wide range of networks.

• Only average degree helps to estimate the disease propagation for all networks.

• Principal Component Analysis is used to reduce the dimensionality of variables.

• It returns the most influent parameters for disease propagation.

摘要

•A spreading disease model is set for a population in complex networks.•The disease is analyzed in a wide range of networks.•Only average degree helps to estimate the disease propagation for all networks.•Principal Component Analysis is used to reduce the dimensionality of variables.•It returns the most influent parameters for disease propagation.

论文关键词：Complex networks,Epidemiology,Principal Component Analysis,SIR model,Random graphs

论文评审过程：Received 3 January 2017, Revised 21 November 2017, Accepted 9 December 2017, Available online 12 December 2017, Version of Record 19 December 2017.

论文官网地址：https://doi.org/10.1016/j.eswa.2017.12.021