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.
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Marshall, C., O’Riordan, C., Cruickshank, J. (2018). Targeting Influential Nodes for Recovery in Bootstrap Percolation on Hyperbolic Networks. In: Alhajj, R., Hoppe, H., Hecking, T., Bródka, P., Kazienko, P. (eds) Network Intelligence Meets User Centered Social Media Networks. ENIC 2017. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-90312-5_1
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