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Experimental comparison of first and second-order similarities in a scientometric context

Author

Listed:
  • Cristian Colliander

    (Umeå University)

  • Per Ahlgren

    (Umeå University
    Stockholm University)

Abstract

The measurement of similarity between objects plays a role in several scientific areas. In this article, we deal with document–document similarity in a scientometric context. We compare experimentally, using a large dataset, first-order with second-order similarities with respect to the overall quality of partitions of the dataset, where the partitions are obtained on the basis of optimizing weighted modularity. The quality of a partition is defined in terms of textual coherence. The results show that the second-order approach consistently outperforms the first-order approach. Each difference between the two approaches in overall partition quality values is significant at the 0.01 level.

Suggested Citation

  • Cristian Colliander & Per Ahlgren, 2012. "Experimental comparison of first and second-order similarities in a scientometric context," Scientometrics, Springer;Akadémiai Kiadó, vol. 90(2), pages 675-685, February.
    • Handle: RePEc:spr:scient:v:90:y:2012:i:2:d:10.1007_s11192-011-0491-x
    DOI: 10.1007/s11192-011-0491-x
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    Citations

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    Cited by:

    1. Fabian Meyer-Brötz & Edgar Schiebel & Leo Brecht, 2017. "Experimental evaluation of parameter settings in calculation of hybrid similarities: effects of first- and second-order similarity, edge cutting, and weighting factors," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1307-1325, June.
    2. 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.
    3. Sergey Shashnov & Maxim Kotsemir, 2018. "Research landscape of the BRICS countries: current trends in research output, thematic structures of publications, and the relative influence of partners," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(2), pages 1115-1155, November.
    4. Cristian Colliander & Per Ahlgren, 2019. "Comparison of publication-level approaches to ex-post citation normalization," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(1), pages 283-300, July.
    5. Yang, Hyeonchae & Jung, Woo-Sung, 2016. "Structural efficiency to manipulate public research institution networks," Technological Forecasting and Social Change, Elsevier, vol. 110(C), pages 21-32.
    6. Yun, Jinhyuk, 2022. "Generalization of bibliographic coupling and co-citation using the node split network," Journal of Informetrics, Elsevier, vol. 16(2).
    7. Dejian Yu & Wanru Wang & Shuai Zhang & Wenyu Zhang & Rongyu Liu, 2017. "Hybrid self-optimized clustering model based on citation links and textual features to detect research topics," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-21, October.
    8. Guadalupe Palacios-Núñez & Gabriel Vélez-Cuartas & Juan D. Botero, 2018. "Developmental tendencies in the academic field of intellectual property through the identification of invisible colleges," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(3), pages 1561-1574, June.
    9. Yun, Jinhyuk & Ahn, Sejung & Lee, June Young, 2020. "Return to basics: Clustering of scientific literature using structural information," Journal of Informetrics, Elsevier, vol. 14(4).

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