### Abstract

We consider the entire spectrum of architectures for large, growing, and complex networks, ranging from being heterogeneous (scale-free) to homogeneous (random or small-world), and investigate the infection dynamics by using a realistic three-state epidemiological model. In this framework, a node can be in one of the three states: susceptible (S), infected (I), or refractory (R), and the populations in the three groups are approximately described by a set of nonlinear differential equations. Our heuristic analysis predicts that, (1) regardless of the network architecture, there exists a substantial fraction of nodes that can never be infected, and (2) heterogeneous networks are relatively more robust against spread of infection as compared with homogeneous networks. These are confirmed numerically. We have also considered the problem of deliberate immunization for preventing wide spread of infection, with the result that targeted immunization can be quite effective for heterogeneous networks. We believe these results are important for a host of problems in many areas of natural science and engineering, and in social sciences as well.

Original language | English (US) |
---|---|

Pages (from-to) | 4045-4061 |

Number of pages | 17 |

Journal | International Journal of Modern Physics B |

Volume | 17 |

Issue number | 22-24 I |

State | Published - Sep 30 2003 |

### Fingerprint

### ASJC Scopus subject areas

- Physics and Astronomy (miscellaneous)
- Condensed Matter Physics
- Electronic, Optical and Magnetic Materials
- Statistical and Nonlinear Physics
- Mathematical Physics

### Cite this

*International Journal of Modern Physics B*,

*17*(22-24 I), 4045-4061.

**Infection dynamics on growing networks.** / Lai, Ying-Cheng; Liu, Zonghua; Ye, Nong.

Research output: Contribution to journal › Article

*International Journal of Modern Physics B*, vol. 17, no. 22-24 I, pp. 4045-4061.

}

TY - JOUR

T1 - Infection dynamics on growing networks

AU - Lai, Ying-Cheng

AU - Liu, Zonghua

AU - Ye, Nong

PY - 2003/9/30

Y1 - 2003/9/30

N2 - We consider the entire spectrum of architectures for large, growing, and complex networks, ranging from being heterogeneous (scale-free) to homogeneous (random or small-world), and investigate the infection dynamics by using a realistic three-state epidemiological model. In this framework, a node can be in one of the three states: susceptible (S), infected (I), or refractory (R), and the populations in the three groups are approximately described by a set of nonlinear differential equations. Our heuristic analysis predicts that, (1) regardless of the network architecture, there exists a substantial fraction of nodes that can never be infected, and (2) heterogeneous networks are relatively more robust against spread of infection as compared with homogeneous networks. These are confirmed numerically. We have also considered the problem of deliberate immunization for preventing wide spread of infection, with the result that targeted immunization can be quite effective for heterogeneous networks. We believe these results are important for a host of problems in many areas of natural science and engineering, and in social sciences as well.

AB - We consider the entire spectrum of architectures for large, growing, and complex networks, ranging from being heterogeneous (scale-free) to homogeneous (random or small-world), and investigate the infection dynamics by using a realistic three-state epidemiological model. In this framework, a node can be in one of the three states: susceptible (S), infected (I), or refractory (R), and the populations in the three groups are approximately described by a set of nonlinear differential equations. Our heuristic analysis predicts that, (1) regardless of the network architecture, there exists a substantial fraction of nodes that can never be infected, and (2) heterogeneous networks are relatively more robust against spread of infection as compared with homogeneous networks. These are confirmed numerically. We have also considered the problem of deliberate immunization for preventing wide spread of infection, with the result that targeted immunization can be quite effective for heterogeneous networks. We believe these results are important for a host of problems in many areas of natural science and engineering, and in social sciences as well.

UR - http://www.scopus.com/inward/record.url?scp=0344876064&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0344876064&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:0344876064

VL - 17

SP - 4045

EP - 4061

JO - International Journal of Modern Physics B

JF - International Journal of Modern Physics B

SN - 0217-9792

IS - 22-24 I

ER -