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Banzhaf Index for Influence Maximization

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 9471)

Abstract

Social media has changed the way people communicate with each other and has brought people together. Enterprises are increasingly using it as a medium for marketing activities. However, due to the size of these networks, marketers often look for key customers (influencers) to drive the campaign to the community. In this paper, we take a game theoretic approach to identify key influencers in a network. We begin with defining coalition games to model the social network and then use the concept of Banzhaf index to measure the utility of each user to the coalition. We further extend this concept towards identification of influencers and compare the resulting algorithm against existing works on influence maximization on several datasets. Improvements are observed.

Keywords

  • Social Network
  • Centrality Measure
  • Collaboration Network
  • Marginal Contribution
  • Coalition Game

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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  • DOI: 10.1007/978-3-319-27433-1_18
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Correspondence to Balaji Vasan Srinivasan .

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Srinivasan, B.V., Kumar, A.S. (2015). Banzhaf Index for Influence Maximization. In: Liu, TY., Scollon, C., Zhu, W. (eds) Social Informatics. SocInfo 2015. Lecture Notes in Computer Science(), vol 9471. Springer, Cham. https://doi.org/10.1007/978-3-319-27433-1_18

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  • DOI: https://doi.org/10.1007/978-3-319-27433-1_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27432-4

  • Online ISBN: 978-3-319-27433-1

  • eBook Packages: Computer ScienceComputer Science (R0)