Journal of Statistical Physics

, Volume 155, Issue 1, pp 72–92 | Cite as

Bootstrap Percolation in Power-Law Random Graphs

Article

Abstract

A bootstrap percolation process on a graph \(G\) is an “infection” process which evolves in rounds. Initially, there is a subset of infected nodes and in each subsequent round each uninfected node which has at least \(r\) infected neighbours becomes infected and remains so forever. The parameter \(r\ge 2\) is fixed. Such processes have been used as models for the spread of ideas or trends within a network of individuals. We analyse this process in the case where the underlying graph is an inhomogeneous random graph, which exhibits a power-law degree distribution, and initially there are \(a(n)\) randomly infected nodes. The main focus of this paper is the number of vertices that will have been infected by the end of the process. The main result of this work is that if the degree sequence of the random graph follows a power law with exponent \(\beta \), where \(2 < \beta < 3\), then a sublinear number of initially infected vertices is enough to spread the infection over a linear fraction of the nodes of the random graph, with high probability. More specifically, we determine explicitly a critical function \(a_c(n)\) such that \(a_c(n) = o(n)\) with the following property. Assuming that \(n\) is the number of vertices of the underlying random graph, if \(a(n) \ll a_c(n)\), then the process does not evolve at all, with high probability as \(n\) grows, whereas if \(a(n)\gg a_c(n)\), then there is a constant \(\varepsilon > 0\) such that, with high probability, the final set of infected vertices has size at least \(\varepsilon n\). This behaviour is in sharp contrast with the case where the underlying graph is a \(G(n, p)\) random graph with \(p=d/n\). It follows from an observation of Balogh and Bollobás that in this case if the number of initially infected vertices is sublinear, then there is lack of evolution of the process. It turns out that when the maximum degree is \(o(n^{1/(\beta - 1)})\), then \(a_c(n)\) depends also on \(r\). But when the maximum degree is \(\Theta (n^{1/(\beta - 1)})\), then \(a_c (n) = n^{\beta - 2 \over \beta - 1}\).

Keywords

Bootstrap percolation Power-law random graph Sharp threshold 

Mathematics Subject Classification

05C80 60K35 60C05 

Notes

Acknowledgments

We would like to thank Rob Morris for pointing out an oversight in an earlier version of this paper. We are also grateful to the anonymous referees for valuable comments and suggestions to improve the presentation of the paper. Hamed Amini gratefully acknowledges financial support from the Austrian Science Fund (FWF) though project P21709. Nikolaos Fountoulakis’s research has been supported by a Marie Curie Intra-European Research Fellowship PIEF-GA-2009-255115 hosted by the Max-Planck-Institut für Informatik.

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.École Polytechnique Fédérale de LausanneLausanneSwitzerland
  2. 2.School of MathematicsUniversity of BirminghamBirminghamUK

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