Extreme events on complex networks

TL;DR

This paper investigates the occurrence and recurrence of extreme events on complex networks using random walk models, revealing that nodes with fewer links are more prone to such events and providing analytical estimates verified by simulations.

Contribution

It introduces analytical methods to predict extreme events on networks and demonstrates their robustness across different routing strategies, informing network design.

Findings

Nodes with fewer links are more prone to extreme events.

Analytical estimates match numerical simulations.

Results are robust even with shortest path routing.

Abstract

We study the extreme events taking place on complex networks. The transport on networks is modelled using random walks and we compute the probability for the occurance and recurrence of extreme events on the network. We show that the nodes with smaller number of links are more prone to extreme events than the ones with larger number of links. We obtain analytical estimates and verify them with numerical simulations. They are shown to be robust even when random walkers follow shortest path on the network. The results suggest a revision of design principles and can be used as an input for designing the nodes of a network so as to smoothly handle an extreme event.