Identifying edges that facilitate the generation of extreme events in networked dynamical systems

TL;DR

This paper investigates how specific edges in time-dependent interaction networks facilitate the recruitment of units leading to extreme events in networked dynamical systems, using edge centrality and network decomposition techniques.

Contribution

It introduces a novel approach combining edge centrality and network decomposition to identify edges that promote extreme events, revealing edges not evident from the underlying topology.

Findings

Certain edges with high centrality facilitate recruitment into extreme events

Edge-based analysis reveals edges not apparent in static topology

Method improves understanding of extreme event generation in complex networks

Abstract

The collective dynamics of complex networks of FitzHugh-Nagumo units exhibits rare and recurrent events of high amplitude (extreme events) that are preceded by so-called proto-events during which a certain fraction of the units become excited. Although it is well known that a sufficiently large fraction of excited units is required to turn a proto-event into an extreme event, it is not yet clear how the other units are being recruited into the final generation of an extreme event. Addressing this question and mimicking typical experimental situations, we investigate the centrality of edges in time-dependent interaction networks. We derived these networks from time series of the units' dynamics employing a widely used bivariate analysis technique. Using our recently proposed edge centrality concepts together with an edge-based network decomposition technique, we observe that the recruitment is primarily facilitated by sets of certain edges that have no equivalent in the underlying topology. Our finding might aid to improve the understanding of generation of extreme events in natural networked dynamical systems.