Distributed flow optimization and cascading effects in weighted complex networks

TL;DR

This paper studies how a specific edge weighting scheme affects flow efficiency and robustness to cascading failures in scale-free networks, revealing optimal conditions for resilience and throughput.

Contribution

It introduces a detailed analysis of flow and cascading effects under a weighted scheme in scale-free networks, identifying optimal parameters for robustness.

Findings

Transport efficiency depends on topology-weight correlations.

Network resilience is maximized at a specific weighting parameter.

Optimal throughput and robustness occur at the same control parameter value.

Abstract

We investigate the effect of a specific edge weighting scheme $\sim (k_i k_j)^{\beta}$ on distributed flow efficiency and robustness to cascading failures in scale-free networks. In particular, we analyze a simple, yet fundamental distributed flow model: current flow in random resistor networks. By the tuning of control parameter $\beta$ and by considering two general cases of relative node processing capabilities as well as the effect of bandwidth, we show the dependence of transport efficiency upon the correlations between the topology and weights. By studying the severity of cascades for different control parameter $\beta$, we find that network resilience to cascading overloads and network throughput is optimal for the same value of $\beta$ over the range of node capacities and available bandwidth.