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Seed journal citation network maps: A method based on network theory

Author

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  • C.M. Calero Medina
  • T.N. van Leeuwen

Abstract

The study presented here shows a method based on network theory to identify the most important journals related to a given journal, the seed journal. In just one simple network map, we get the relevant citation environment of a specific seed journal. It is of interest to librarians, publishers, scientists and science policy makers. These journal citation network maps are useful for these various stakeholders in and around the science system, as they provide information on the level of journal connections, unlike the more traditional structures such as the Journal Subject Categories, the classification system applied in the products of Thomson Reuters (Journal Citation Reports, Web of Science, etc.). These network maps show the closest relations journals can have, based on citation relations, suggesting influence relations between journals in such a way that traditional field boundaries are transcended.

Suggested Citation

  • C.M. Calero Medina & T.N. van Leeuwen, 2012. "Seed journal citation network maps: A method based on network theory," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(6), pages 1226-1234, June.
  • Handle: RePEc:bla:jamist:v:63:y:2012:i:6:p:1226-1234
    DOI: 10.1002/asi.22631
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    Cited by:

    1. Inchae Park & Keeeun Lee & Byungun Yoon, 2015. "Exploring Promising Research Frontiers Based on Knowledge Maps in the Solar Cell Technology Field," Sustainability, MDPI, vol. 7(10), pages 1-30, October.
    2. Hakyeon Lee, 2015. "Uncovering the multidisciplinary nature of technology management: journal citation network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(1), pages 51-75, January.
    3. Andrea Moretta Tartaglione & Ylenia Cavacece & Giuseppe Russo & Giuseppe Granata, 2019. "A Systematic Mapping Study on Customer Loyalty and Brand Management," Administrative Sciences, MDPI, vol. 9(1), pages 1-21, January.
    4. Yu, Qi & Ding, Ying & Song, Min & Song, Sungjeon & Liu, Jianhua & Zhang, Bin, 2015. "Tracing database usage: Detecting main paths in database link networks," Journal of Informetrics, Elsevier, vol. 9(1), pages 1-15.

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