Congestion and centrality in traffic flow on complex networks

TL;DR

This paper explores how traffic density and flow dynamics on complex networks relate to static centrality measures, revealing non-trivial dependencies even at low traffic levels.

Contribution

It investigates the relationship between centrality measures and traffic density in particle hopping models on scale-free networks, highlighting dynamic-structural interactions.

Findings

Traffic density depends non-trivially on betweenness centrality.

Non-central nodes can carry significant traffic even at low densities.

Network structure influences flow speed and distribution.

Abstract

The central points of communication network flow has often been identified using graph theoretical centrality measures. In real networks, the state of traffic density arises from an interplay between the dynamics of the flow and the underlying network structure. In this work we investigate the relationship between centrality measures and the density of traffic for some simple particle hopping models on networks with emerging scale-free degree distributions. We also study how the speed of the dynamics are affected by the underlying network structure. Among other conclusions, we find that, even at low traffic densities, the dynamical measure of traffic density (the occupation ratio) has a non-trivial dependence on the static centrality (quantified by "betweenness centrality"), which non-central vertices getting a comparatively large portion of the traffic.