3 Citations (Scopus)

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 languageEnglish (US)
Pages (from-to)4045-4061
Number of pages17
JournalInternational Journal of Modern Physics B
Volume17
Issue number22-24 I
StatePublished - Sep 30 2003

Fingerprint

Immunization
Growing Networks
Heterogeneous networks
infectious diseases
Infection
Heterogeneous Networks
Natural sciences
Social sciences
Complex networks
Network architecture
Refractory materials
Epidemiological Model
Differential equations
Si
Small World
Social Sciences
Network Architecture
Vertex of a graph
Complex Networks
Nonlinear Differential Equations

ASJC Scopus subject areas

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

Cite this

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

In: International Journal of Modern Physics B, Vol. 17, No. 22-24 I, 30.09.2003, p. 4045-4061.

Research output: Contribution to journalArticle

Lai, Ying-Cheng ; Liu, Zonghua ; Ye, Nong. / Infection dynamics on growing networks. In: International Journal of Modern Physics B. 2003 ; Vol. 17, No. 22-24 I. pp. 4045-4061.
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