Identify influential social network spreaders

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 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, or 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
Title of host publicationProceedings - 14th IEEE International Conference on Data Mining Workshops, ICDMW 2014
EditorsZhi-Hua Zhou, Wei Wang, Ravi Kumar, Hannu Toivonen, Jian Pei, Joshua Zhexue Huang, Xindong Wu
PublisherIEEE Computer Society
Number of pages7
ISBN (Electronic)9781479942749
StatePublished - 14 Dec 2014
Event14th IEEE International Conference on Data Mining Workshops, ICDMW 2014 - Shenzhen, China
Duration: 14 Dec 2014 → …

Publication series

NameIEEE International Conference on Data Mining Workshops, ICDMW
ISSN (Print)2375-9232
ISSN (Electronic)2375-9259


Conference14th IEEE International Conference on Data Mining Workshops, ICDMW 2014
Period14/12/14 → …


  • entropy
  • Epidemic model
  • k-shell decomposition
  • network diversity
  • social network analysis

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