
arXiv: 1806.02903
Given a hypergraph $\mathcal{H}$, the $\mathcal{H}$-bootstrap process starts with an initial set of infected vertices of $\mathcal{H}$ and, at each step, a healthy vertex $v$ becomes infected if there exists a hyperedge of $\mathcal{H}$ in which $v$ is the unique healthy vertex. We say that the set of initially infected vertices percolates if every vertex of $\mathcal{H}$ is eventually infected. We show that this process exhibits a sharp threshold when $\mathcal{H}$ is a hypergraph obtained by randomly sampling hyperedges from an approximately $d$-regular $r$-uniform hypergraph satisfying some mild degree and codegree conditions; this confirms a conjecture of Morris. As a corollary, we obtain a sharp threshold for a variant of the graph bootstrap process for strictly $2$-balanced graphs which generalises a result of Korándi, Peled and Sudakov. Our approach involves an application of the differential equations method.
differential equations method, Probability (math.PR), Random graphs (graph-theoretic aspects), Interacting random processes; statistical mechanics type models; percolation theory, Hypergraphs, hypergraphs, martingales, sharp threshold, FOS: Mathematics, Mathematics - Combinatorics, Martingales with discrete parameter, Combinatorics (math.CO), bootstrap percolation, Mathematics - Probability
differential equations method, Probability (math.PR), Random graphs (graph-theoretic aspects), Interacting random processes; statistical mechanics type models; percolation theory, Hypergraphs, hypergraphs, martingales, sharp threshold, FOS: Mathematics, Mathematics - Combinatorics, Martingales with discrete parameter, Combinatorics (math.CO), bootstrap percolation, Mathematics - Probability
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 4 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
