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A Bayesian model on the merging errors of coauthorship data

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  • Xie, Zheng

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.

Suggested Citation

  • Xie, Zheng, 2019. "A Bayesian model on the merging errors of coauthorship data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
    • Handle: RePEc:eee:phsmap:v:527:y:2019:i:c:s0378437119306934
    DOI: 10.1016/j.physa.2019.121140
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    Cited by:

    1. Xie, Zheng, 2020. "Predicting publication productivity for researchers: A piecewise Poisson model," Journal of Informetrics, Elsevier, vol. 14(3).

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