Neural networks for fast estimation of social network centrality measures

Ashok Kumar, Kishan G. Mehrotra, Chilukuri K Mohan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

Centrality measures are extremely important in the analysis of social networks, with applications such as identification of the most influential individuals for effective target marketing. Eigenvector centrality and PageRank are among the most useful centrality measures, but computing these measures can be prohibitively expensive for large social networks. This paper shows that neural networks can be effective in learning and estimating the ordering of vertices in a social network based on these measures, requiring far less computational effort, and proving to be faster than early termination of the power grid method that can be used for computing the centrality measures. Two features describing the size of the social network and two vertex-specific attributes sufficed as inputs to the neural networks, requiring very few hidden neurons.

Original languageEnglish (US)
Title of host publicationProceedings of the 5th International Conference on Fuzzy and Neuro Computing, FANCCO - 2015
PublisherSpringer Verlag
Pages175-184
Number of pages10
Volume415
ISBN (Print)9783319272115
DOIs
StatePublished - 2015
Event5th International Conference on Fuzzy and Neuro Computing, FANCCO 2015 - Hyderabad, India
Duration: Dec 17 2015Dec 19 2015

Publication series

NameAdvances in Intelligent Systems and Computing
Volume415
ISSN (Print)21945357

Other

Other5th International Conference on Fuzzy and Neuro Computing, FANCCO 2015
CountryIndia
CityHyderabad
Period12/17/1512/19/15

Keywords

  • Centrality
  • Eigenvector centrality
  • PageRank
  • Social network

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science(all)

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