Large deviations for subcritical bootstrap percolation on the random graph
Abstract
We study atypical behavior in bootstrap percolation on the Erdos-R\'enyi random graph. Initially a set S is infected. Other vertices are infected once at least r of their neighbors become infected. Janson et al. (2012) locates the critical size of S, above which it is likely that the infection will spread almost everywhere. Below this threshold, a central limit theorem is proved for the size of the eventually infected set. In this note, we calculate the rate function for the event that a small set S eventually infects an unexpected number of vertices, and identify the least-cost trajectory realizing such a large deviation.
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