Using global diversity and local topology features to identify influential network spreaders

Yu Hsiang Fu, Chung Yuan Huang*, Chuen-Tsai Sun

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

24 Scopus citations


Identifying the most influential individuals spreading ideas, information, or infectious diseases is a topic receiving significant attention from network researchers, since such identification can assist or hinder information dissemination, product exposure, and contagious disease detection. Hub nodes, high betweenness nodes, high closeness nodes, and high k-shell nodes have been identified as good initial spreaders. However, few efforts have been made to use node diversity within network structures to measure spreading ability. The two-step framework described in this paper uses a robust and reliable measure that combines global diversity and local features to identify the most influential network nodes. Results from a series of Susceptible-Infected-Recovered (SIR) epidemic simulations indicate that our proposed method performs well and stably in single initial spreader scenarios associated with various complex network datasets.

Original languageEnglish
Pages (from-to)344-355
Number of pages12
JournalPhysica A: Statistical Mechanics and its Applications
StatePublished - 1 Sep 2015


  • Entropy
  • Node diversity
  • SIR epidemic model
  • Social network analysis k-shell decomposition

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