Robustness of Network Measures to Link Errors

Researchers studying biological networks use a variety of measures to identify ``important'' nodes in their networks. However, the robustness of these measures in the presence of link inaccuracies stemming from noisy data has not been well characterized. Here we present two simple models of false and missing links and their effect on different commonly used centrality measures, focusing particularly on degree centrality, betweenness centrality, and dynamical importance. We show that, compared to degree centrality, betweenness centrality and dynamical importance are much more robust in the face of noise if the false positives are randomly distributed. When the noise has more structure, the differences in the robustness levels of the various metrics can change dramatically.