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Viewing computer science through citation analysis: Salton and Bergmark Redux

Author

Listed:
  • Sitaram Devarakonda

    (Netelabs, NET ESolutions Corporation
    Randstad USA)

  • Dmitriy Korobskiy

    (Netelabs, NET ESolutions Corporation)

  • Tandy Warnow

    (University of Illinois Urbana-Champaign)

  • George Chacko

    (Netelabs, NET ESolutions Corporation)

Abstract

Computer science has experienced dramatic growth and diversification over the last twenty years. Towards a current understanding of the structure of this discipline, we analyze a large sample of the computer science literature from the DBLP database. For insight on the features of this cohort and the relationship within its components, we have constructed article level clusters based on either direct citations or co-citations, and reconciled them with major and minor subject categories in the All Science Journal Classification. We describe complementary insights from clustering by direct citation and co-citation, and both point to the increase in computer science publications and their scope. Our analysis reveals cross-category clusters, some that interact with external fields, such as the biological sciences, while others remain inward looking. Overall, we document an increase in computer science publications and their scope.

Suggested Citation

  • Sitaram Devarakonda & Dmitriy Korobskiy & Tandy Warnow & George Chacko, 2020. "Viewing computer science through citation analysis: Salton and Bergmark Redux," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 271-287, October.
    • Handle: RePEc:spr:scient:v:125:y:2020:i:1:d:10.1007_s11192-020-03624-0
    DOI: 10.1007/s11192-020-03624-0
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    1. Wang, Qi & Waltman, Ludo, 2016. "Large-scale analysis of the accuracy of the journal classification systems of Web of Science and Scopus," Journal of Informetrics, Elsevier, vol. 10(2), pages 347-364.
    2. Ludo Waltman & Nees Jan van Eck, 2012. "A new methodology for constructing a publication‐level classification system of science," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(12), pages 2378-2392, December.
    3. Lovro Šubelj & Nees Jan van Eck & Ludo Waltman, 2016. "Clustering Scientific Publications Based on Citation Relations: A Systematic Comparison of Different Methods," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-23, April.
    4. M. M. Kessler, 1965. "Comparison of the results of bibliographic coupling and analytic subject indexing," American Documentation, Wiley Blackwell, vol. 16(3), pages 223-233, July.
    5. Henry Small, 1973. "Co‐citation in the scientific literature: A new measure of the relationship between two documents," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 24(4), pages 265-269, July.
    6. Perianes-Rodriguez, Antonio & Ruiz-Castillo, Javier, 2017. "A comparison of the Web of Science and publication-level classification systems of science," Journal of Informetrics, Elsevier, vol. 11(1), pages 32-45.
    7. Kevin W. Boyack, 2017. "Investigating the effect of global data on topic detection," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 999-1015, May.
    8. Kevin W. Boyack & Richard Klavans, 2010. "Co-citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately?," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(12), pages 2389-2404, December.
    9. 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.
    10. Richard Klavans & Kevin W. Boyack, 2017. "Which Type of Citation Analysis Generates the Most Accurate Taxonomy of Scientific and Technical Knowledge?," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(4), pages 984-998, April.
    11. Shu, Fei & Julien, Charles-Antoine & Zhang, Lin & Qiu, Junping & Zhang, Jing & Larivière, Vincent, 2019. "Comparing journal and paper level classifications of science," Journal of Informetrics, Elsevier, vol. 13(1), pages 202-225.
    12. Kevin W Boyack & David Newman & Russell J Duhon & Richard Klavans & Michael Patek & Joseph R Biberstine & Bob Schijvenaars & André Skupin & Nianli Ma & Katy Börner, 2011. "Clustering More than Two Million Biomedical Publications: Comparing the Accuracies of Nine Text-Based Similarity Approaches," PLOS ONE, Public Library of Science, vol. 6(3), pages 1-11, March.
    13. Boyack, Kevin W. & Patek, Michael & Ungar, Lyle H. & Yoon, Patrick & Klavans, Richard, 2014. "Classification of individual articles from all of science by research level," Journal of Informetrics, Elsevier, vol. 8(1), pages 1-12.
    14. Scott Emmons & Stephen Kobourov & Mike Gallant & Katy Börner, 2016. "Analysis of Network Clustering Algorithms and Cluster Quality Metrics at Scale," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-18, July.
    15. Kevin W. Boyack & Richard Klavans, 2010. "Co‐citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately?," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(12), pages 2389-2404, December.
    16. Éric Archambault & David Campbell & Yves Gingras & Vincent Larivière, 2009. "Comparing bibliometric statistics obtained from the Web of Science and Scopus," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(7), pages 1320-1326, July.
    17. Tanmoy Chakraborty, 2018. "Role of interdisciplinarity in computer sciences: quantification, impact and life trajectory," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(3), pages 1011-1029, March.
    18. Kevin W. Boyack & Henry Small & Richard Klavans, 2013. "Improving the accuracy of co-citation clustering using full text," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(9), pages 1759-1767, September.
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    Cited by:

    1. Barbara McGillivray & Gard B. Jenset & Khalid Salama & Donna Schut, 2022. "Investigating patterns of change, stability, and interaction among scientific disciplines using embeddings," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-15, December.
    2. Xiaoguang Wang & Hongyu Wang & Han Huang, 2021. "Evolutionary exploration and comparative analysis of the research topic networks in information disciplines," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(6), pages 4991-5017, June.

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