A novel approach based on journal coupling to determine authors who are most likely to be part of the same invisible college

Jose A. Garcia, Rosa Rodriguez-Sanchez, J. Fdez-Valdivia

doi:10.2478/jdis-2025-0006

Journal of Data and Information Science >

0 20241211 - 20241211

DOI: https://doi.org/10.2478/jdis-2025-0006

Research Papers

A novel approach based on journal coupling to determine authors who are most likely to be part of the same invisible college

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Department of Computer Science and Artificial Intelligence, CITIC-UGR, Universidad de Granada, 18071 Granada, Spain

†Jose A. Garcia (Email: jags@decsai.ugr.es).

Received date: 2024-09-11

Revised date: 2024-11-18

Accepted date: 2024-11-19

Online published: 2024-12-11

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Abstract

Purpose: In this paper, we use author clustering based on journal coupling (i.e., shared academic journals) to determine researchers who have the same scientific interests and similar conceptual frameworks. The basic assumption is that authors who publish in the same academic journals are more likely to share similar conceptual frameworks and interests than those who never publish in the same venues. Therefore, they are more likely to be part of the same invisible college (i.e., authors in this subgroup contribute materially to research on the same topic and often publish their work in similar publication venues).
Design/methodology/approach: Test in a controlled exercise the grouping of authors based on journal coupling to determine invisible colleges in a research field using a case study of 302 authors who had published in the Information Science and Library Science (IS&LS) category of the Web of Science Core Collection. For each author, we retrieved all the scientific journals in which this author had published his/her articles. We then used the cosine measure to calculate the similarity between authors (both first and second order).
Findings: In this paper, using journal coupling of IS&LS authors, we found four main invisible colleges: “Information Systems”, “Business and Information Management”, “Quantitative Information Science” and “Library Science.” The main journals that determine the existence of these invisible colleges were Inform Syst Res, Inform Syst J, J Bus Res, J Knowl Manage, J Informetr, Pro Int Conf Sci Inf, Int J Geogr Inf Sci, J Am Med Inform Assn, and Learn Publ. However, the main journals that demonstrate that IS&LS determine a field were J Am Soc Inf Sci Tec/J Assoc Inf Sci Tech, Scientometrics, Inform Process Manag, and J Inf Sci.
Research limitations: The results shown in this article are from a controlled exercise. The analysis performed using journal coupling excludes books, book chapters, and conference papers. In this article, only academic journals were used for the representation of research results.
Practical implications: Our results may be of interest to IS&LS scholars. This is because these results provide a new lens for grouping authors, making use of the authors’ journal publication profile and journal coupling. Furthermore, extending our approach to the study of the structure of other disciplines would possibly be of interest to historians of science as well as scientometricians.
Originality/value: This is a novel approach based on journal coupling to determine authors who are most likely to be part of the same invisible college.

Key words： Authors; Journal Coupling; Clustering; Invisible College; IS&LS Category; Web of Science Core Collection

Cite this article

Jose A. Garcia, Rosa Rodriguez-Sanchez, J. Fdez-Valdivia . A novel approach based on journal coupling to determine authors who are most likely to be part of the same invisible college[J]. Journal of Data and Information Science, 0 : 20241211 -20241211 . DOI: 10.2478/jdis-2025-0006

References

[1] Ahlgren, P., & Colliander, C. (2009). Document-document similarity approaches and science mapping: Experimental comparison of five approaches.Journal of Informetrics, 3(1), 49-63.
[2] Ahlgren, P., & Colliander, C. (2012). Experimental comparison of first and second-order similarities in a scientometric context.Scientometrics, 90(2), 675-685.
[3] Astrom, F. (2010). The visibility of information science and library science research in bibliometric mapping of the LIS field. The Library Quarterly: Information, Community, Policy, 80(2), 143-159. https://doi.org/10.1086/651005
[4] Baeza-Yates, R. A., & Ribeiro-Neto, B. A. (1999). Modern information retrieval. Addison-Wesley.
[5] Borgman, C. L., & Furner, J. (2002). Scholarly communication and bibliometrics. In B. Cronin (Ed.), Annual Review of Information Science and Technology (Vol. 36, pp. 2-73). Medford, NJ: Information Today.
[6] Everitt B., Landau S., & Leese M. (2001). Cluster analysis (4th ed.). Arnold.
[7] Garcia J. A., Rodríguez-Sánchez R., Fdez-Valdivia J., Torres-Salinas D., Robinson-García N. (2012). Mapping academic institutions according to their journal publication profile: Spanish universities as a case study. Journal of the American Society for Information Science and Technology, 63(11), 2328-2340. https://doi.org/10.1002/asi.22735
[8] Griffith B. C., Small H. G., Stonehill J. A., & Dey S. (1974). Structure of scientific literatures. 2. Toward a macrostructure and microstructure for science.Science Studies, 4, 339-365.
[9] Janssens, F. (2007). Clustering of scientific fields by integrating text mining and bibliometrics (Unpublished doctoral dissertation). Katholieke Universiteit, Leuven.
[10] Kessler, M. M. (1963). Bibliographic coupling between scientific papers.American Documentation, 14(1), 10-25.
[11] Klavans, R., & Boyack, K. W. (2009). Toward a consensus map of science.Journal of the American Society for Information Science and Technology, 60(3), 455-476.
[12] Leydesdorff, L., & Rafols, I. (2009). A global map of science based on the ISI subject categories.Journal of the American Society for Information Science and Technology, 60(2), 348-369.
[13] Minguillo, D. (2010). Toward a new way of mapping scientific fields: Authors’ competence for publishing in scholarly journals.Journal of the American Society for Information Science & Technology, 61(4), 772-786.
[14] Moya-Anegón F., Herrero-Solana V., & Jiménez-Contreras E. (2006). A connectionist and multivariate approach to science maps: The SOM, clustering, and MDS applied to Library and Information Science research.Journal of Information Science, 32, 63-77.
[15] Moya-Anegón F., Vargas-Quesada B., Chinchilla-Rodríguez Z., Corera-Álvarez E., Muñoz-Fernández F. J., & Herrero-Solana V. (2007). Visualizing the marrow of science.Journal of the American Society for Information Science and Technology, 58, 2167-2179.
[16] Moya-Anegón F., Vargas-Quesada B., Herrero-Solana V., Chinchilla-Rodríguez Z., Corera-Álvarez E., & Muñoz-Fernández F. J. (2004). A new technique for building maps of large scientific domains based on the co-citation of classes and categories.Scientometrics, 61, 129-145.
[17] Narin F., Carpenter M., & Berlt N. C. (1972). Interrelationships of scientific journals.Journal of the American Society for Information Science, 23, 323-331.
[18] Ni C., Sugimoto C. R., & Jiang J. (2013). Venue-author-coupling: A measure for identifying disciplines through author communities.Journal of the American Society for Information Science and Technology, 64(2), 265-279.
[19] Nixon, J. M. (2014). Core journals in library and information science: Developing a methodology for ranking LIS journals.College and Research Libraries, 75(1), 66-90.
[20] Noyons, C. (2004). Science maps within a science policy context. In H. F. Moed et al.(Eds.), Handbook of Quantitative Science and Technology Research
[21] Pajek 5.18 (2023). Available from: http://mrvar.fdv.uni-lj.si/pajek/
[22] Price, D. J. de Solla, & Beaver, D. (1966). Collaboration in an invisible college. American Psychologist, 2011-2018. https://doi.org/10.1037/h0024051
[23] Qiu J. P., Dong K., & Yu H. Q. (2014). Comparative study on structure and correlation among author co-occurrence networks in bibliometrics. Scientometrics, 101, 1345-1360. https://doi.org/10.1007/s11192-014-1315-6
[24] Robinson-García,N., Rodríguez-Sánchez, R., García, J. A., Torres-Salinas, D., & Fdez-Valdivia, J.(2013). Network analysis of Spanish universities according to their journal publication profile in scientific areas. Revista Española de Documentación Científica, 362013.4.1042
[25] Safón, V., & Docampo, D. (2023). What are you reading? From core journals to trendy journals in the Library and Information Science (LIS) field. Scientometrics, 128, 2777-2801. https://doi.org/10.1007/s11192-023-04673-x
[26] Salton, G., & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval.Information Processing and Management, 24(5), 513-523.
[27] Saracevic, T. (1999). Information science.Journal of the American Society for Information Science, 50, 1051-1063.
[28] Virtanen P., Gommers R., Oliphant T. E., Haberland M., Reddy T., Cournapeau D., Burovski E., Peterson P., Weckesser W., Bright J., van der Walt, S. J., Brett M., Wilson J., Millman K. J., Mayorov N., Nelson A. R. J., Jones E., Kern R., Larson E., Carey C. J., Polat I., Feng Y., Moore E. W., VanderPlas J., Laxalde D., Perktold J., Cimrman R., Henriksen I., Quintero E. A., Harris C. R., Archibald A. M., Ribeiro A. H., Pedregosa F., van Mulbregt P., & SciPy 1.0 Contributors. (2020). SciPy 1.0: Fundamental algorithms for scientific computing in Python. Nature Methods, 17(3), 261-272. https://doi.org/10.1038/s41592-019-0686-2
[29] Small, H. (1973). Co-citation in the scientific literature: A new measure of the relationship between two documents.Journal of the American Society for Information Science, 24(4), 265-269.
[30] Small, H., & Garfield, E. (1985). The geography of science: Disciplinary and national mappings.Journal of Information Science, 11, 147-159.
[31] Small, H. G., & Koenig, M. E. D. (1977). Journal clustering using a bibliographic coupling method.Information Processing and Management, 13(5), 277-288.
[32] Tijssen, R. J. W., & Van Raan, A. F. J. (1994). Mapping changes in science and technology: Bibliometric co-occurrence analysis of the R&D literature. Evaluation Review, 18(1), 98-115. https://doi.org/10.1177/0193841X9401800110
[33] Vinkler, P. (2019). Core journals and elite subsets in scientometrics.Scientometrics, 121(1), 241-259.
[34] Walters, W. H., & Wilder, E. I. (2015). Worldwide contributors to the literature of library and information science: Top authors, 2007-2012.Scientometrics, 103(1), 301-327.
[35] Weerasinghe, S. (2017). Citation analysis of library and information science research output for collection development.Journal of the University Librarians Association of Sri Lanka, 20(1), 1-18.
[36] White, H. D. (2001). Authors as citers over time. Journal of the American Society for Information Science, 52, 87-108. https://doi.org/10.1002/1097-4571(2000)9999:9999<::AID-ASI1542>3.0.CO; 2-T
[37] White, H. D., & McCain, K. W. (1998). Visualizing a discipline: An author co-citation analysis of information science, 1972-1995.Journal of the American Society for Information Science, 49, 327-355.

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