Merging error analysis of name disambiguation based on author similarity
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Falsely identifying different authors as one is called merging error in the name disambiguation of coauthorship networks. Research on the measurement and distribution of merging errors helps to collect high quality coauthorship networks. In the aspect of measurement, we provide a Bayesian model to measure the errors through author similarity. We illustratively use the model and coauthor similarity to measure the errors caused by initial-based name disambiguation methods. The empirical result on large-scale coauthorship networks shows that using coauthor similarity cannot increase the accuracy of disambiguation through surname and the initial of the first given name. In the aspect of distribution, expressing coauthorship data as hypergraphs and supposing the merging error rate is proper to hyperdegree with an exponent, we find that hypergraphs with a range of network properties highly similar to those of low merging error hypergraphs can be constructed from high merging error hypergraphs. It implies that focusing on the error correction of high hyperdegree nodes is a labor- and time-saving approach of improving the data quality for coauthorship network analysis.
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