Abstract

The $r$-neighbour bootstrap process is an update rule for the states of vertices in which `uninfected' vertices with at least $r$ `infected' neighbours become infected and a set of initially infected vertices is said to percolate if eventually all vertices are infected. For every $r \geq 3$, a sharp condition is given for the minimum degree of a sufficiently large graph that guarantees the existence of a percolating set of size $r$. In the case $r=3$, for $n$ large enough, any graph on $n$ vertices with minimum degree $\lfloor n/2 \rfloor +1$ has a percolating set of size $3$ and for $r \geq 4$ and $n$ large enough (in terms of $r$), every graph on $n$ vertices with minimum degree $\lfloor n/2 \rfloor + (r-3)$ has a percolating set of size $r$. A class of examples are given to show the sharpness of these results.

Highlights

  • Bootstrap percolation is a model for the spread of an ‘infection’ in a network that was first introduced and investigated by Chalupa, Leath, and Reich [7] as a monotone model of the dynamics of ferromagnetism

  • We shall focus on the conditions for the minimum degree of a graph that imply the existence of a percolating set of the smallest possible size

  • Poloczek, and Reichman [11] investigated Ore-type degree conditions for a graph that guarantee that m(G, 2) = 2

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Summary

Introduction

Bootstrap percolation is a model for the spread of an ‘infection’ in a network that was first introduced and investigated by Chalupa, Leath, and Reich [7] as a monotone model of the dynamics of ferromagnetism. We shall focus on the conditions for the minimum degree of a graph that imply the existence of a percolating set of the smallest possible size. When r 3 and the number of vertices is large relative to r, a different picture emerges and, when n is large, any graph on n vertices with a minimum degree that exceeds n/2 by some constant that depends on r will have a set of size r that percolates in r-neighbour bootstrap percolation.

Graphs with no small percolating sets
Sets with large closure
Structure of large closed sets
Threshold r 4
Findings
Open problems

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