Measurement error in network data: A re-classification |
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Authors: | Dan J. Wang Xiaolin Shi Daniel A. McFarland Jure Leskovec |
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Affiliation: | 1. Stanford University, Stanford, CA 94305, United States;2. Microsoft Corporation, Redmond, WA 98052, United States |
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Abstract: | 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. |
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Keywords: | Measurement error Missing data Simulation |
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