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A comparison of two techniques for bibliometric mapping: Multidimensional scaling and VOS

Open access
van Eck, Nees Jan, Waltman, Ludo, Dekker, Rommert, van den Berg, Jan·2010·Journal of the American Society for Information Science and Technology

Abstract

VOS is a new mapping technique that can serve as an alternative to the well‐known technique of multidimensional scaling (MDS). We present an extensive comparison between the use of MDS and the use of VOS for constructing bibliometric maps. In our theoretical analysis, we show the mathematical relation between the two techniques. In our empirical analysis, we use the techniques for constructing maps of authors, journals, and keywords. Two commonly used approaches to bibliometric mapping, both based on MDS, turn out to produce maps that suffer from artifacts. Maps constructed using VOS turn out not to have this problem. We conclude that in general maps constructed using VOS provide a more satisfactory representation of a dataset than maps constructed using well‐known MDS approaches.

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Metadata

Authors
van Eck, N. J., Waltman, L., Dekker, R., & van den Berg, J.
Venue
Journal of the American Society for Information Science and Technology
Publisher
Wiley
Published
2010-12
Volume
61
Issue
12
Pages
2405-2416
ISSN
  • 1532-2882 (ISSN-L)
  • 1532-2890
OpenAlex
W2953107155
Cited by
888
References
3
FWCI
8.93
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: A comparison of two techniques for bibliometric mapping: Multidimensional scaling and VOS. https://app.cochat.ai/papers/10.1002/asi.21421
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van Eck, N. J., Waltman, L., Dekker, R., & van den Berg, J. (2010). A comparison of two techniques for bibliometric mapping: Multidimensional scaling and VOS. Journal of the American Society for Information Science and Technology. https://doi.org/10.1002/asi.21421

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.