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Scale‐free collaboration networks: An author name disambiguation perspective

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  • Jinseok Kim

Abstract

Several studies have found that collaboration networks are scale‐free, proposing that such networks can be modeled by specific network evolution mechanisms like preferential attachment. This study argues that collaboration networks can look more or less scale‐free depending on the methods for resolving author name ambiguity in bibliographic data. Analyzing networks constructed from multiple datasets containing 3.4 M ∼ 9.6 M publication records, this study shows that collaboration networks in which author names are disambiguated by the commonly used heuristic, i.e., forename‐initial‐based name matching, tend to produce degree distributions better fitted to power‐law slopes with the typical scaling parameter (2

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  • Jinseok Kim, 2019. "Scale‐free collaboration networks: An author name disambiguation perspective," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 70(7), pages 685-700, July.
    • Handle: RePEc:bla:jinfst:v:70:y:2019:i:7:p:685-700
    DOI: 10.1002/asi.24158
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    Cited by:

    1. Jinseok Kim & Jason Owen-Smith, 2021. "ORCID-linked labeled data for evaluating author name disambiguation at scale," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(3), pages 2057-2083, March.
    2. Zhao, Zhenyue & Bu, Yi & Kang, Lele & Min, Chao & Bian, Yiyang & Tang, Li & Li, Jiang, 2020. "An investigation of the relationship between scientists’ mobility to/from China and their research performance," Journal of Informetrics, Elsevier, vol. 14(2).

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