29 Citations (Scopus)

Abstract

Most previous studies on spreading dynamics on complex networks are based on the assumption that a node can transmit infection to any of its neighbors with equal probability. In realistic situations, an infected node can preferentially select a targeted node and vice versa. We develop a first-order correction to the standard mean-field theory to address this type of more realistic spreading dynamics on complex networks. Our analysis reveals that, when small-degree nodes are selected more frequently as targets, infection can spread to a larger part of the network. However, when a small set of hub nodes dominates the dynamics, spreading can be severely suppressed. Our analysis yields more accurate predictions for the spreading dynamics than those from the standard mean-field approach.

Original languageEnglish (US)
Article number026111
JournalPhysical Review E - Statistical, Nonlinear, and Soft Matter Physics
Volume78
Issue number2
DOIs
StatePublished - Aug 19 2008

Fingerprint

Selectivity
Complex Networks
selectivity
infectious diseases
Vertex of a graph
Infection
hubs
Mean-field Theory
Mean Field
First-order
predictions
Target
Prediction
Standards

ASJC Scopus subject areas

  • Physics and Astronomy(all)
  • Condensed Matter Physics
  • Statistical and Nonlinear Physics
  • Mathematical Physics

Cite this

Selectivity-based spreading dynamics on complex networks. / Yang, Rui; Huang, Liang; Lai, Ying-Cheng.

In: Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, Vol. 78, No. 2, 026111, 19.08.2008.

Research output: Contribution to journalArticle

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