Congestion-gradient driven transport on complex networks

TL;DR

This paper investigates how local congestion-aware routing rules affect transport efficiency in complex networks, revealing an optimal level of congestion awareness that maximizes capacity before rigid flow causes jamming.

Contribution

It introduces a model of congestion-gradient driven transport with varying routing awareness and identifies the existence of an optimal congestion awareness level for maximum network capacity.

Findings

Transport capacity peaks at an intermediate congestion awareness level.

Networks tend to jam at any nonzero load when using purely local information routing.

A correlation between node congestion and betweenness centrality is established.

Abstract

We present a study of transport on complex networks with routing based on local information. Particles hop from one node of the network to another according to a set of routing rules with different degrees of congestion awareness, ranging from random diffusion to rigid congestion-gradient driven flow. Each node can be either source or destination for particles and all nodes have the same routing capacity, which are features of ad-hoc wireless networks. It is shown that the transport capacity increases when a small amount of congestion awareness is present in the routing rules, and that it then decreases as the routing rules become too rigid when the flow becomes strictly congestion-gradient driven. Therefore, an optimum value of the congestion awareness exists in the routing rules. It is also shown that, in the limit of a large number of nodes, networks using routing based on local information jam at any nonzero load. Finally, we study the correlation between congestion at node level and a betweenness centrality measure.