Jamming in complex networks with degree correlation

TL;DR

This paper investigates how degree correlations in complex networks affect congestion, showing disassortative networks facilitate better transport by reducing pressure congestion, supported by theoretical and real-world network analysis.

Contribution

It demonstrates that degree correlation types influence congestion levels, with disassortative networks reducing pressure congestion, and validates findings through real-world network applications.

Findings

Disassortative networks have lower pressure congestion than uncorrelated networks.

Assortative networks exhibit higher pressure congestion.

Real-world network data confirms the theoretical predictions.

Abstract

We study the effects of the degree-degree correlations on the pressure congestion J when we apply a dynamical process on scale free complex networks using the gradient network approach. We find that the pressure congestion for disassortative (assortative) networks is lower (bigger) than the one for uncorrelated networks which allow us to affirm that disassortative networks enhance transport through them. This result agree with the fact that many real world transportation networks naturally evolve to this kind of correlation. We explain our results showing that for the disassortative case the clusters in the gradient network turn out to be as much elongated as possible, reducing the pressure congestion J and observing the opposite behavior for the assortative case. Finally we apply our model to real world networks, and the results agree with our theoretical model.