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Network of the core: mapping and visualizing the core of scientific domains

Author

Listed:
  • Gohar Feroz Khan

    (Yeungnam University)

  • Junghoon Moon

    (Program in Regional Information, Seoul National University)

  • Han Woo Park

    (Yeungnam University)

Abstract

In this article, we propose mapping and visualizing the core of scientific domains using social network analysis techniques derived from mathematical graph theory. In particular, the concept of Network of the Core is introduced which can be employed to visualize scientific domains by constructing a network among theoretical constructs, models, and concepts. A Network of the Core can be used to reveal hidden properties and structures of a research domain such as connectedness, centrality, density, structural equivalence, and cohesion, by modeling the casual relationship among theoretical constructs. Network of the Core concept can be used to explore the strengths and limitations of a research domain, and graphically and mathematically derive the number research hypotheses. The Network of the Core approach can be applied to any domain given that the investigator has a deep understanding of the area under consideration, a graphical or conceptual view (in the form of a network of association among the theoretical constructs and concepts) of the scientific domain can be obtained, and an underlying theory is available or can be constructed to support Network of the Core formation. Future research directions and several other issues related to the Network of the Core concept are also discussed.

Suggested Citation

  • Gohar Feroz Khan & Junghoon Moon & Han Woo Park, 2011. "Network of the core: mapping and visualizing the core of scientific domains," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(3), pages 759-779, December.
    • Handle: RePEc:spr:scient:v:89:y:2011:i:3:d:10.1007_s11192-011-0489-4
    DOI: 10.1007/s11192-011-0489-4
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    References listed on IDEAS

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    Cited by:

    1. Jiming Hu & Yin Zhang, 2017. "Discovering the interdisciplinary nature of Big Data research through social network analysis and visualization," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(1), pages 91-109, July.

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