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Appropriate similarity measures for author co‐citation analysis

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  • Nees Jan van Eck
  • Ludo Waltman

Abstract

We provide in this article a number of new insights into the methodological discussion about author co‐citation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors' co‐citation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well‐known cosine. The other two similarity measures have not been used before in the bibliometric literature. We show by means of an example that the choice of an appropriate similarity measure has a high practical relevance. Finally, we discuss the use of similarity measures for statistical inference.

Suggested Citation

  • Nees Jan van Eck & Ludo Waltman, 2008. "Appropriate similarity measures for author co‐citation analysis," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 59(10), pages 1653-1661, August.
    • Handle: RePEc:bla:jamist:v:59:y:2008:i:10:p:1653-1661
    DOI: 10.1002/asi.20872
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    Cited by:

    1. Perianes-Rodriguez, Antonio & Waltman, Ludo & van Eck, Nees Jan, 2016. "Constructing bibliometric networks: A comparison between full and fractional counting," Journal of Informetrics, Elsevier, vol. 10(4), pages 1178-1195.
    2. Alexander Tkotz & Jan Christoph Munck & Andreas Erich Wald, 2018. "Innovation Management Control: Bibliometric Analysis Of Its Emergence And Evolution As A Research Field," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 22(03), pages 1-34, April.
    3. Florian Noseleit, 2013. "Entrepreneurship, structural change, and economic growth," Journal of Evolutionary Economics, Springer, vol. 23(4), pages 735-766, September.
    4. Lukun Zheng, 2019. "Using mutual information as a cocitation similarity measure," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(3), pages 1695-1713, June.
    5. Lu Huang & Xiang Chen & Yi Zhang & Yihe Zhu & Suyi Li & Xingxing Ni, 2021. "Dynamic network analytics for recommending scientific collaborators," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(11), pages 8789-8814, November.

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