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

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

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

6 Scopus citations

Abstract

The identification of influential spreaders of information via social networks can assist in the acceleration or hindrance of information dissemination, in increased product exposure, and in the detection of contagious disease outbreaks. Hub nodes, high betweenness nodes, high closeness nodes, and high k-shell nodes have been identified as good initial spreaders. However, researchers have overlooked node diversity within network structures as a means of measuring spreading ability. The two-step framework described in this paper uses a robust and insensitive measure that combines global diversity and local features (e.g., degree centrality) to identify the most influential social network nodes. Preliminary experiment results indicate that the proposed method performs well and maintains stability in single initial spreader scenarios associated with different social network datasets.

Original languageEnglish
Title of host publicationASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
EditorsMartin Ester, Guandong Xu, Xindong Wu, Xindong Wu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages948-953
Number of pages6
ISBN (Electronic)9781479958771
DOIs
StatePublished - 10 Oct 2014
Event2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2014 - Beijing, China
Duration: 17 Aug 201420 Aug 2014

Publication series

NameASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

Conference

Conference2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2014
CountryChina
CityBeijing
Period17/08/1420/08/14

Keywords

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

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