Measurement error in network data: A re-classification

Research on measurement error in network data has typically focused on missing data. We embed missing data, which we term false negative nodes and edges, in a broader classification of error scenarios. This includes false positive nodes and edges and falsely aggregated and disaggregated nodes. We simulate these six measurement errors using an online social network and a publication citation network, reporting their effects on four node-level measures – degree centrality, clustering coefficient, network constraint, and eigenvector centrality. Our results suggest that in networks with more positively-skewed degree distributions and higher average clustering, these measures tend to be less resistant to most forms of measurement error. In addition, we argue that the sensitivity of a given measure to an error scenario depends on the idiosyncracies of the measure's calculation, thus revising the general claim from past research that the more ‘global’ a measure, the less resistant it is to measurement error. Finally, we anchor our discussion to commonly-used networks in past research that suffer from these different forms of measurement error and make recommendations for correction strategies.

► We simulate measurement error on two empirical networks, an online friendship graph and a citation graph. ► Networks with higher average clustering and more positively skewed degree distributions are less robust to measurement error. ► Clustering coefficient and network constraint are less robust to error than centrality measures. ► Missing nodes and edges are not consistently more harmful than spurious nodes and edges. ► Error correction strategies include focusing data cleaning on active node subsets and conditional imputation methods.