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 ake measurement error and m

DOI: 10.1016/j.socnet.2012.01.003

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@article{Wang2012MeasurementEI, title={Measurement error in network data: A re-classification}, author={Dan J. Wang and Xiaolin Shi and Daniel A. McFarland and Jure Leskovec}, journal={Social Networks}, year={2012}, volume={34}, pages={396-409} }