A Bayesian model on the merging errors of coauthorship data

Zheng Xie

Physica A: Statistical Mechanics and its Applications, 2019, vol. 527, issue C

Abstract: Robust analysis of coauthorship networks is based on high quality data. However, ground-truth data are usually unavailable. Empirical data suffer several types of errors, a typical one of which is called merging error, identifying different persons as one entity. Specific features of authors have been used to reduce merging errors. We proposed a Bayesian model on the merging errors of coauthorship data. When knowing the ground truth of specific empirical datasets obtained by a given method, the model contributes to finding informative features to reduce the merging errors of the datasets obtained by the same method. When being given the useful features of reducing merging errors, the model can be utilized to calculate the rate of merging errors for the name entities of authors. Therefore, the model can help to detect compromised name entities; thus has potential contribution to improving the quality of empirical coauthorship data.

Keywords: Name disambiguation; Bayesian model; Coauthorship network (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:527:y:2019:i:c:s0378437119306934

DOI: 10.1016/j.physa.2019.121140

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