### Résumé

One model of real-life spreading processes is that of first-passage percolation (also called the SI model) on random graphs. Social interactions often follow bursty patterns, which are usually modelled with independent and identically distributed heavy-tailed passage times on edges. On the other hand, random graphs are often locally tree-like, and spreading on trees with leaves might be very slow due to bottleneck edges with huge passage times. Here we consider the SI model with passage times following a power-law distribution P(ξ>t)t
^{-α} with infinite mean. For any finite connected graph G with a root s, we find the largest number of vertices κ(G,s) that are infected in finite expected time, and prove that for every k≤κ(G,s), the expected time to infect k vertices is at most O(k
^{1/α}). Then we show that adding a single edge from s to a random vertex in a random tree typically increases κ(T,s) from a bounded variable to a fraction of the size of , thus severely accelerating the process. We examine this acceleration effect on some natural models of random graphs: critical Galton - Watson trees conditioned to be large, uniform spanning trees of the complete graph, and on the largest cluster of near-critical Erdos-Rényi graphs. In particular, at the upper end of the critical window, the process is already much faster than exactly at criticality.

langue originale | Anglais |
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Pages (de - à) | 858-886 |

Nombre de pages | 29 |

journal | Advances in Applied Probability |

Volume | 50 |

Numéro de publication | 3 |

Date de mise en ligne précoce | 31 août 2017 |

Les DOIs | |

état | Publié - 1 sept. 2018 |

### Empreinte digitale

### Contient cette citation

*Advances in Applied Probability*,

*50*(3), 858-886. https://doi.org/10.1017/apr.2018.39