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Identifying a better measure of relatedness for mapping science

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  • Richard Klavans
  • Kevin W. Boyack

Abstract

Measuring the relatedness between bibliometric units (journals, documents, authors, or words) is a central task in bibliometric analysis. Relatedness measures are used for many different tasks, among them the generating of maps, or visual pictures, showing the relationship between all items from these data. Despite the importance of these tasks, there has been little written on how to quantitatively evaluate the accuracy of relatedness measures or the resulting maps. The authors propose a new framework for assessing the performance of relatedness measures and visualization algorithms that contains four factors: accuracy, coverage, scalability, and robustness. This method was applied to 10 measures of journal–journal relatedness to determine the best measure. The 10 relatedness measures were then used as inputs to a visualization algorithm to create an additional 10 measures of journal–journal relatedness based on the distances between pairs of journals in two‐dimensional space. This second step determines robustness (i.e., which measure remains best after dimension reduction). Results show that, for low coverage (under 50%), the Pearson correlation is the most accurate raw relatedness measure. However, the best overall measure, both at high coverage, and after dimension reduction, is the cosine index or a modified cosine index. Results also showed that the visualization algorithm increased local accuracy for most measures. Possible reasons for this counterintuitive finding are discussed.

Suggested Citation

  • Richard Klavans & Kevin W. Boyack, 2006. "Identifying a better measure of relatedness for mapping science," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(2), pages 251-263, January.
    • Handle: RePEc:bla:jamist:v:57:y:2006:i:2:p:251-263
    DOI: 10.1002/asi.20274
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    Citations

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    Cited by:

    1. Ludo Waltman & Nees Jan Eck, 2012. "A new methodology for constructing a publication-level classification system of science," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(12), pages 2378-2392, December.
    2. Boyack, Kevin W. & Klavans, Richard, 2008. "Measuring science–technology interaction using rare inventor–author names," Journal of Informetrics, Elsevier, vol. 2(3), pages 173-182.
    3. Kose, Toshihiro & Sakata, Ichiro, 2019. "Identifying technology convergence in the field of robotics research," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 751-766.
    4. Leydesdorff, Loet & Rafols, Ismael, 2012. "Interactive overlays: A new method for generating global journal maps from Web-of-Science data," Journal of Informetrics, Elsevier, vol. 6(2), pages 318-332.
    5. 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.
    6. Copiello, Sergio, 2019. "Peer and neighborhood effects: Citation analysis using a spatial autoregressive model and pseudo-spatial data," Journal of Informetrics, Elsevier, vol. 13(1), pages 238-254.
    7. Kostoff, Ronald N. & Geisler, Elie, 2007. "The unintended consequences of metrics in technology evaluation," Journal of Informetrics, Elsevier, vol. 1(2), pages 103-114.
    8. Liu, Yunmei & Yang, Liu & Chen, Min, 2021. "A new citation concept: Triangular citation in the literature," Journal of Informetrics, Elsevier, vol. 15(2).
    9. Dzikowski, Piotr, 2018. "A bibliometric analysis of born global firms," Journal of Business Research, Elsevier, vol. 85(C), pages 281-294.
    10. Zhang, Yi & Shang, Lining & Huang, Lu & Porter, Alan L. & Zhang, Guangquan & Lu, Jie & Zhu, Donghua, 2016. "A hybrid similarity measure method for patent portfolio analysis," Journal of Informetrics, Elsevier, vol. 10(4), pages 1108-1130.
    11. Katy Börner, 2007. "Making Sense of Mankind's Scholarly Knowledge and Expertise: Collecting, Interlinking, and Organizing What We Know and Different Approaches to Mapping (Network) Science," Environment and Planning B, , vol. 34(5), pages 808-825, October.
    12. Kopka, Alexander & Grashof, Nils, 2022. "Artificial intelligence: Catalyst or barrier on the path to sustainability?," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    13. Klavans, Richard & Boyack, Kevin W., 2014. "Mapping altruism," Journal of Informetrics, Elsevier, vol. 8(2), pages 431-447.

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