Selectivity-based spreading dynamics on complex networks

Rui Yang, Liang Huang, Ying-Cheng Lai

Research output: Contribution to journalArticlepeer-review

41 Scopus citations


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
Issue number2
StatePublished - Aug 19 2008

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Statistics and Probability
  • Condensed Matter Physics


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