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Using mutual information as a cocitation similarity measure

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  • Lukun Zheng

    (Western Kentucky University)

Abstract

The debate regarding to which similarity measure can be used in co-citation analysis lasted for many years. The mostly debated measure is Pearson’s correlation coefficient r. It has been used as similarity measure in literature since the beginning of the technique in the 1980s. However, some researchers criticized using Pearson’s r as a similarity measure because it does not fully satisfy the mathematical conditions of a good similarity metric and (or) because it doesn’t meet some natural requirements a similarity measure should satisfy. Alternative similarity measures like cosine measure and chi square measure were also proposed and studied, which resulted in more controversies and debates about which similarity measure to use in co-citation analysis. In this article, we put forth the hypothesis that the researchers with high mutual information are closely related to each other and that the mutual information can be used as a similarity measure in author co-citation analysis. Given two researchers, the mutual information between them can be calculated based on their publications and their co-citation frequencies. A mutual information proximity matrix is then constructed. This proximity matrix meet the two requirements formulated by Ahlgren et al. (J Am Soc Inf Sci Technol 54(6):550–560, 2003). We conduct several experimental studies for the validation of our hypothesis and the results using mutual information are compared to the results using other similarity measures.

Suggested Citation

  • Lukun Zheng, 2019. "Using mutual information as a cocitation similarity measure," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(3), pages 1695-1713, June.
  • Handle: RePEc:spr:scient:v:119:y:2019:i:3:d:10.1007_s11192-019-03098-9
    DOI: 10.1007/s11192-019-03098-9
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    References listed on IDEAS

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    1. Per Ahlgren & Bo Jarneving & Ronald Rousseau, 2003. "Requirements for a cocitation similarity measure, with special reference to Pearson's correlation coefficient," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 54(6), pages 550-560, April.
    2. 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.
    3. Howard D. White, 2003. "Author cocitation analysis and Pearson's r," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 54(13), pages 1250-1259, November.
    4. Loet Leydesdorff & Liwen Vaughan, 2006. "Co‐occurrence matrices and their applications in information science: Extending ACA to the Web environment," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(12), pages 1616-1628, October.
    5. Leo Egghe, 2010. "Good properties of similarity measures and their complementarity," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(10), pages 2151-2160, October.
    6. Pawe{l} Fiedor, 2014. "Mutual Information Rate-Based Networks in Financial Markets," Papers 1401.2548, arXiv.org.
    7. Howard D. White & Belver C. Griffith, 1981. "Author cocitation: A literature measure of intellectual structure," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 32(3), pages 163-171, May.
    8. Loet Leydesdorff, 2008. "On the normalization and visualization of author co‐citation data: Salton's Cosine versus the Jaccard index," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 59(1), pages 77-85, January.
    9. Leo Egghe, 2010. "Good properties of similarity measures and their complementarity," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(10), pages 2151-2160, October.
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