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Targeting Influential Nodes for Recovery in Bootstrap Percolation on Hyperbolic Networks

  • Christine MarshallEmail author
  • Colm O’Riordan
  • James Cruickshank
Chapter
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Part of the Lecture Notes in Social Networks book series (LNSN)

Abstract

The influence of our peers is a powerful reinforcement for our social behaviour, evidenced in voter behaviour and trend adoption. Bootstrap percolation is a simple method for modelling this process. In this work we look at bootstrap percolation on hyperbolic random geometric graphs, which have been used to model the Internet graph, and introduce a form of bootstrap percolation with recovery, showing that random targeting of nodes for recovery will delay adoption, but this effect is enhanced when nodes of high degree are selectively targeted.

Keywords

Bootstrap percolation Bootstrap percolation with recovery Hyperbolic random geometric graphs 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Christine Marshall
    • 1
    Email author
  • Colm O’Riordan
    • 1
  • James Cruickshank
    • 2
  1. 1.Discipline of Information TechnologyNational University of IrelandGalwayIreland
  2. 2.School of MathematicsNational University of IrelandGalwayIreland

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