IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v89y2011i3d10.1007_s11192-011-0489-4.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-011-0489-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11192-011-0489-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Chaomei Chen, 2003. "Visualizing scientific paradigms: An introduction," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 54(5), pages 392-393, March.
    2. Si Hyung Joo & Yeonbae Kim, 2010. "Measuring relatedness between technological fields," Scientometrics, Springer;Akadémiai Kiadó, vol. 83(2), pages 435-454, May.
    3. Louise Wiles & Timothy Olds & Marie Williams, 2010. "Evidence base, quantitation and collaboration: three novel indices for bibliometric content analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 85(1), pages 317-328, October.
    4. Han Woo Park & Loet Leydesdorff, 2009. "Knowledge linkage structures in communication studies using citation analysis among communication journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 81(1), pages 157-175, October.
    5. Jyun-Cheng Wang & Cheng-hsin Chiang & Shu-Wei Lin, 2010. "Network structure of innovation: can brokerage or closure predict patent quality?," Scientometrics, Springer;Akadémiai Kiadó, vol. 84(3), pages 735-748, September.
    6. P. S. Nagpaul, 2002. "Visualizing cooperation networks of elite institutions in India," Scientometrics, Springer;Akadémiai Kiadó, vol. 54(2), pages 213-228, June.
    7. Haiyang Lu & Yuqiang Feng, 2009. "A measure of authors’ centrality in co-authorship networks based on the distribution of collaborative relationships," Scientometrics, Springer;Akadémiai Kiadó, vol. 81(2), pages 499-511, November.
    8. Limei Zhao & Qingpu Zhang, 2011. "Mapping knowledge domains of Chinese digital library research output, 1994–2010," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(1), pages 51-87, October.
    9. Rongying Zhao & Ju Wang, 2011. "Visualizing the research on pervasive and ubiquitous computing," Scientometrics, Springer;Akadémiai Kiadó, vol. 86(3), pages 593-612, March.
    10. Hsin-Ning Su & Pei-Chun Lee, 2010. "Mapping knowledge structure by keyword co-occurrence: a first look at journal papers in Technology Foresight," Scientometrics, Springer;Akadémiai Kiadó, vol. 85(1), pages 65-79, October.
    11. Eli M. Blatt, 2009. "Differentiating, describing, and visualizing scientific space: A novel approach to the analysis of published scientific abstracts," Scientometrics, Springer;Akadémiai Kiadó, vol. 80(2), pages 385-406, August.
    12. Haiyan Hou & Hildrun Kretschmer & Zeyuan Liu, 2008. "The structure of scientific collaboration networks in Scientometrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 75(2), pages 189-202, May.
    13. Chaomei Chen & Ray J. Paul & Bob O'Keefe, 2001. "Fitting the jigsaw of citation: Information visualization in domain analysis," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 52(4), pages 315-330.
    14. Eugene Garfield & A. I. Pudovkin & V. S. Istomin, 2003. "Why do we need algorithmic historiography?," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 54(5), pages 400-412, March.
    15. William H. DeLone & Ephraim R. McLean, 1992. "Information Systems Success: The Quest for the Dependent Variable," Information Systems Research, INFORMS, vol. 3(1), pages 60-95, March.
    16. Frizo Janssens & Wolfgang Glänzel & Bart Moor, 2008. "A hybrid mapping of information science," Scientometrics, Springer;Akadémiai Kiadó, vol. 75(3), pages 607-631, June.
    17. Antonio Perianes-Rodríguez & Carlos Olmeda-Gómez & Félix Moya-Anegón, 2010. "Detecting, identifying and visualizing research groups in co-authorship networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 82(2), pages 307-319, February.
    18. Han Woo Park & Loet Leydesdorff, 2008. "Korean journals in the Science Citation Index: What do they reveal about the intellectual structure of S&T in Korea?," Scientometrics, Springer;Akadémiai Kiadó, vol. 75(3), pages 439-462, June.
    19. Han Woo Park & Heung Deug Hong & Loet Leydesdorff, 2005. "A comparison of the knowledge-based innovation systems in the economies of South Korea and the Netherlands using Triple Helix indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 65(1), pages 3-27, October.
    20. Gohar Feroz Khan & Junghoon Moon & Han Woo Park & Bobby Swar & Jae Jeung Rho, 2011. "A socio-technical perspective on e-government issues in developing countries: a scientometrics approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 87(2), pages 267-286, May.
    21. Loet Leydesdorff, 2004. "Top-down decomposition of the Journal Citation Reportof the Social Science Citation Index: Graph- and factor-analytical approaches," Scientometrics, Springer;Akadémiai Kiadó, vol. 60(2), pages 159-180, June.
    22. Loet Leydesdorff, 2007. "Betweenness centrality as an indicator of the interdisciplinarity of scientific journals," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 58(9), pages 1303-1319, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gohar Feroz Khan, 2013. "Social media-based systems: an emerging area of information systems research and practice," Scientometrics, Springer;Akadémiai Kiadó, vol. 95(1), pages 159-180, April.
    2. Muaz Niazi & Amir Hussain, 2011. "Agent-based computing from multi-agent systems to agent-based models: a visual survey," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(2), pages 479-499, November.
    3. Bar-Ilan, Judit, 2008. "Informetrics at the beginning of the 21st century—A review," Journal of Informetrics, Elsevier, vol. 2(1), pages 1-52.
    4. JinHyo Joseph Yun & EuiSeob Jeong & JinSeu Park, 2016. "Network Analysis of Open Innovation," Sustainability, MDPI, vol. 8(8), pages 1-21, July.
    5. Li, Eldon Y. & Liao, Chien Hsiang & Yen, Hsiuju Rebecca, 2013. "Co-authorship networks and research impact: A social capital perspective," Research Policy, Elsevier, vol. 42(9), pages 1515-1530.
    6. Han Woo Park & Loet Leydesdorff, 2009. "Knowledge linkage structures in communication studies using citation analysis among communication journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 81(1), pages 157-175, October.
    7. Matthew A. Shapiro & Han Woo Park, 2012. "Regional development in South Korea: accounting for research area in centrality and networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 90(1), pages 271-287, January.
    8. Park, Han Woo & Leydesdorff, Loet, 2010. "Longitudinal trends in networks of university-industry-government relations in South Korea: The role of programmatic incentives," Research Policy, Elsevier, vol. 39(5), pages 640-649, June.
    9. Michel Zitt, 2015. "Meso-level retrieval: IR-bibliometrics interplay and hybrid citation-words methods in scientific fields delineation," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(3), pages 2223-2245, March.
    10. Seong Eun Cho & Han Woo Park, 2012. "Government organizations’ innovative use of the Internet: The case of the Twitter activity of South Korea’s Ministry for Food, Agriculture, Forestry and Fisheries," Scientometrics, Springer;Akadémiai Kiadó, vol. 90(1), pages 9-23, January.
    11. Leonardo Reyes-Gonzalez & Claudia N. Gonzalez-Brambila & Francisco Veloso, 2016. "Using co-authorship and citation analysis to identify research groups: a new way to assess performance," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(3), pages 1171-1191, September.
    12. Jingjing Zhang & Yan Yan & Jiancheng Guan, 2015. "Scientific relatedness in solar energy: a comparative study between the USA and China," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(2), pages 1595-1613, February.
    13. Gao, Qiang & Liang, Zhentao & Wang, Ping & Hou, Jingrui & Chen, Xiuxiu & Liu, Manman, 2021. "Potential index: Revealing the future impact of research topics based on current knowledge networks," Journal of Informetrics, Elsevier, vol. 15(3).
    14. Minjeong Kim & Han Woo Park, 2012. "Measuring Twitter-based political participation and deliberation in the South Korean context by using social network and Triple Helix indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 90(1), pages 121-140, January.
    15. Wei Wang & Dechao Ma & Fengzhi Wu & Mengxin Sun & Shuangqing Xu & Qiuyue Hua & Ziyuan Sun, 2023. "Exploring the Knowledge Structure and Hotspot Evolution of Greenwashing: A Visual Analysis Based on Bibliometrics," Sustainability, MDPI, vol. 15(3), pages 1-35, January.
    16. Carusi, Chiara & Bianchi, Giuseppe, 2019. "Scientific community detection via bipartite scholar/journal graph co-clustering," Journal of Informetrics, Elsevier, vol. 13(1), pages 354-386.
    17. Arif Mehmood & Byung-Won On & Ingyu Lee & Han Woo Park & Gyu Sang Choi, 2018. "Corroborating social media echelon in cancer research," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(2), pages 801-813, March.
    18. Lin Zhang & Frizo Janssens & Liming Liang & Wolfgang Glänzel, 2010. "Journal cross-citation analysis for validation and improvement of journal-based subject classification in bibliometric research," Scientometrics, Springer;Akadémiai Kiadó, vol. 82(3), pages 687-706, March.
    19. Chen, Guo & Xiao, Lu, 2016. "Selecting publication keywords for domain analysis in bibliometrics: A comparison of three methods," Journal of Informetrics, Elsevier, vol. 10(1), pages 212-223.
    20. Peng, Tai-Quan, 2015. "Assortative mixing, preferential attachment, and triadic closure: A longitudinal study of tie-generative mechanisms in journal citation networks," Journal of Informetrics, Elsevier, vol. 9(2), pages 250-262.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:scient:v:89:y:2011:i:3:d:10.1007_s11192-011-0489-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.