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A dense network sub-grouping algorithm for co-citation analysis and its implementation in the software tool Sitkis

Author

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  • Henri A. Schildt

    (Institute of Strategy and International Business, Department of Industrial Engineering and Management, Helsinki University of Technology)

  • Juha T. Mattsson

    (Institute of Strategy and International Business, Department of Industrial Engineering and Management, Helsinki University of Technology)

Abstract

Summary Clustering algorithms are used prominently in co-citation analysis by analysts aiming to reveal research streams within a field. However, clustering of widely cited articles is not robust to small variations in citation patterns. We propose an alternative algorithm, dense network sub-grouping, which identifies dense groups of co-cited references. We demonstrate the algorithm using a data set from the field of family business research and compare it to two alternative methods, multidimensional scaling and clustering. We also introduce a free software tool, Sitkis, that implements the algorithm and other common bibliometric methods. The software identifies journal-, country- and university-specific citation patterns and co-citation groups, enabling the identification of “invisible colleges.”

Suggested Citation

  • Henri A. Schildt & Juha T. Mattsson, 2006. "A dense network sub-grouping algorithm for co-citation analysis and its implementation in the software tool Sitkis," Scientometrics, Springer;Akadémiai Kiadó, vol. 67(1), pages 143-163, April.
    • Handle: RePEc:spr:scient:v:67:y:2006:i:1:d:10.1556_scient.67.2006.1.9
    DOI: 10.1556/Scient.67.2006.1.9
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    Citations

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    Cited by:

    1. Jan Lampe & Priscilla Sarai Kraft & Andreas Bausch, 2020. "Mapping the Field of Research on Entrepreneurial Organizations (1937–2016): A Bibliometric Analysis and Research Agenda," Entrepreneurship Theory and Practice, , vol. 44(4), pages 784-816, July.
    2. Yuen-Hsien Tseng & Ming-Yueh Tsay, 2013. "Journal clustering of library and information science for subfield delineation using the bibliometric analysis toolkit: CATAR," Scientometrics, Springer;Akadémiai Kiadó, vol. 95(2), pages 503-528, May.
    3. Pei-Chun Lee & Hsin-Ning Su & Te-Yi Chan, 2010. "Assessment of ontology-based knowledge network formation by Vector-Space Model," Scientometrics, Springer;Akadémiai Kiadó, vol. 85(3), pages 689-703, December.
    4. Sumita Raghuram & Philipp Tuertscher & Raghu Garud, 2010. "Research Note ---Mapping the Field of Virtual Work: A Cocitation Analysis," Information Systems Research, INFORMS, vol. 21(4), pages 983-999, December.
    5. Ricardo Arencibia-Jorge & Ronald Rousseau, 2009. "Influence of individual researchers’ visibility on institutional impact: an example of Prathap’s approach to successive h-indices," Scientometrics, Springer;Akadémiai Kiadó, vol. 79(3), pages 507-516, June.
    6. Boubaker, Sabri & Goodell, John W. & Kumar, Satish & Sureka, Riya, 2023. "COVID-19 and finance scholarship: A systematic and bibliometric analysis," International Review of Financial Analysis, Elsevier, vol. 85(C).
    7. Geoffrey M. Hodgson & Juha-Antti Lamberg, 2018. "The past and future of evolutionary economics: some reflections based on new bibliometric evidence," Evolutionary and Institutional Economics Review, Springer, vol. 15(1), pages 167-187, June.
    8. Gomez-Jauregui, Valentin & Gomez-Jauregui, Cecilia & Manchado, Cristina & Otero, Cesar, 2014. "Information management and improvement of citation indices," International Journal of Information Management, Elsevier, vol. 34(2), pages 257-271.
    9. Mariani, Marcello & Borghi, Matteo, 2019. "Industry 4.0: A bibliometric review of its managerial intellectual structure and potential evolution in the service industries," Technological Forecasting and Social Change, Elsevier, vol. 149(C).

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