Robustness of Network Measures to Link Errors

TL;DR

This paper investigates how link errors in complex networks affect the reliability of three key node centrality measures, using stochastic models, simulations, and analytical theory to assess robustness.

Contribution

It introduces stochastic models for link errors and provides an analytical framework to evaluate the robustness of centrality measures in the presence of link inaccuracies.

Findings

Degree centrality is most sensitive to link errors.

Betweenness centrality shows moderate robustness.

Analytical results closely match simulation outcomes.

Abstract

In various applications involving complex networks, network measures are employed to assess the relative importance of network nodes. However, the robustness of such measures in the presence of link inaccuracies has not been well characterized. Here we present two simple stochastic models of false and missing links and study the effect of link errors on three commonly used node centrality measures: degree centrality, betweenness centrality, and dynamical importance. We perform numerical simulations to assess robustness of these three centrality measures. We also develop an analytical theory, which we compare with our simulations, obtaining very good agreement.