Detecting, identifying and visualizing research groups in co-authorship networks

Abstract

The present paper proposes a method for detecting, identifying and visualizing research groups. The data used refer to nine Carlos III University of Madrid departments, while the findings for the Communication Technologies Department illustrate the method. Structural analysis was used to generate co-authorship networks. Research groups were identified on the basis of factorial analysis of the raw data matrix and similarities in the choice of co-authors. The resulting networks distinguished the researchers participating in the intra-departmental network from those not involved and identified the existing research groups. Fields of research were characterized by the Journal of Citation Report subject category assigned to the bibliographic references cited in the papers written by the author-factors. The results, i.e., the graphic displays of the structures of the socio-centric and co-authorship networks and the strategies underlying collaboration among researchers, were later discussed with the members of the departments analyzed. The paper constitutes a starting point for understanding and characterizing networking within research institutions.

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References

  1. Balakrishnan, H., & Deo, N. (2006). Discovering communities in complex networks. Proceedings of the 44th Annual Southeast Regional Conference (pp. 280–285). New York: Association for Computing Machinery.

  2. Beaver, D., & Rosen, R. (1978). Studies in scientific collaboration. Part I. The professional origins of scientific co-authorship. Scientometrics, 1, 65–84.

    Article  Google Scholar 

  3. Bordons, M., & Zulueta, M. A. (1997) Comparison of research team activity in two biomedical fields. Scientometrics, 40(3), 423–436.

    Article  Google Scholar 

  4. Bordons, M., Zulueta, M. A., Cabrero, A., & Barrigón, S. (1995a). Identifying research teams with bibliometric tools. Proceedings of the 5th International Conference of the International Society for Scientometrics and Informetrics (pp. 83–91). Medford: Learned Information.

  5. Bordons, M., Zulueta, M. A., Cabrero, A., & Barrigón, S. (1995b). Research performance at the micro level: analysis of structure and dynamics of pharmacological research teams. Research Evaluation, 5(2), 137–142.

    Google Scholar 

  6. Calero, C., Buter, R., Cabello, C., & Noyons, E. C. M. (2006). How to identify research groups using publication analysis: An example in the field of nanotechnology. Scientometrics, 66(2), 365–376.

    Article  Google Scholar 

  7. Chen, C., & Carr, L. (1999a). Visualizing the evolution of a subject domain: a case study. IEEE visualization (pp. 449–452). San Francisco: IEEE Computer Society.

    Google Scholar 

  8. Chen, C., & Carr, L. (1999b). A semantic-centric approach to information visualization. Proceedings of the 3rd International Conference on Information Visualisation (pp. 18–23). Londres: IEEE Computer Society.

  9. Chen, C., & Paul, R. J. (2001). Visualizing a knowledge domain’s intellectual structure. IEEE Computer, 34(3), 65–71.

    Google Scholar 

  10. Chen, C., Paul, R. J., & O’Keefe, B. (2001). Fitting the jigsaw of citation: information visualization in domain analysis. Journal of the American Society for Information Science and Technology, 52(4), 315–330.

    Article  Google Scholar 

  11. Cohen, J. E. (1991). Size, age and productivity of scientific and technical research groups. Scientometrics, 20(3), 395–416.

    Article  Google Scholar 

  12. Ding, Y., Chowdhury, G. G., & Foo, S. (2000). Journal as markers of intellectual space: Journal co-citation analysis of information retrieval area, 1987–1997. Scientometrics, 47(1), 55–73.

    Article  Google Scholar 

  13. Donetti, L., & Muñoz, M. A. (2004). Detecting network communities: A new systematic and efficient algorithm. Journal of Statistical Mechanics: Theory and Experiment, 10012, 1–15.

    Google Scholar 

  14. Eisenberg, J. D. (2002). SVG essentials. Beijing: O’Reilly.

    Google Scholar 

  15. Etzkowitz, H. (2003). Research groups as quasy-firms: The invention of the entrepreneurial university. Research Policy, 32(1), 109–121.

    Article  Google Scholar 

  16. Gálvez, C., & Moya-Anegón, F. (2006). The unification of institutional addresses applying parametrized finite-state graphs (P-FSG). Scientometrics, 69(2), 323–345.

    Article  Google Scholar 

  17. Gálvez, C., & Moya-Anegón, F. (2007). Standardizing formats of corporate source data. Scientometrics, 70(1), 3–26.

    Article  Google Scholar 

  18. Geroimenko, V., & Chen, C. (2005). Visualizing information using SVG and X3D: XML-based technologies for the XML-based Web. London: Springer.

    MATH  Book  Google Scholar 

  19. Harsanyi, M. A. (1993). Multiple authors, multiple problems. Bibliometrics and the study of scholarly collaboration: a literature review. Library and Information Science Research, 15, 325–354.

    Google Scholar 

  20. Herrero-Solana, V., & Hassan, Y. (2006). Metodología para el desarrollo de interfaces visuales de recuperación de información: Análisis y comparación. Information Research, 11(3), from http://informationr.net/ir/11-3/paper258.html.

  21. Ichise, R., Takeda, H., & Uemaya, K. (2006). Exploration of researchers’ social network for discovering communities. In: New Frontiers in Artificial Intelligence. New York: Springer-Verlag (Joint JSAI Workshop, 2005).

  22. Kamada, T., & Kawai, S. (1989). An algorithm for drawing general undirected graphs. Information Processing Letters, 31(1), 7–15.

    MATH  Article  MathSciNet  Google Scholar 

  23. Katz, J. S. (1992). Bibliometric assessment of intranational University-University collaboration. Dissertation, University of Sussex, Brighton.

  24. Katz, J. S., & Martin, B. R. (1997). What is research collaboration? Research Policy, 26, 1–18.

    Article  Google Scholar 

  25. Laudel, G. (2002). What do we measure by co-authorship? Research Evaluation, 11(1), 3–15.

    Article  Google Scholar 

  26. Liu, X., Bollen, J., Nelson, M. L., & van de Sompel, H. (2005). Co-authorship networks in the digital library research community. Information Processing and Management, 41, 1462–1480.

    Article  Google Scholar 

  27. McCain, K. W. (1990). Mapping authors in intellectual space: A technical overview. Journal of the American Society for Information Science, 41(6), 433–443.

    Article  Google Scholar 

  28. Melin, G., & Persson, O. (1996). Studying research collaboration using co-authorships. Scientometrics, 36(3), 363–377.

    Article  Google Scholar 

  29. Miquel, J. F., Okubo, Y., Narváez, N., & Frigoletto, L. (1989). Les scientifiques sont-ils ouverts à la coopération internationale? La Recherche, 20(206), 116–118.

    Google Scholar 

  30. Monfort, N. (2004). Discovering communities through information structure and dynamics: A review of recent research, Pennsylvania State University (Technical Report, no MS-CIS-04–18).

  31. Moreno, J. L. (1953). Who shall survive? Foundations of sociometry, group psychotherapy and sociodrama. New York: Beacon House.

    Google Scholar 

  32. Moya-Anegón, F., Jiménez Contreras, E., & Moneda Corrochano, M. (1998). Research fronts in library and information science in Spain. Scientometrics, 42(2), 229–246.

    Article  Google Scholar 

  33. 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 cocitation of classes and categories. Scientometrics, 61(1), 129–145.

    Article  Google Scholar 

  34. Newman, M. E. J. (2004a). Detecting community structure in networks. European Physical Journal B, 38, 321–330.

    Article  Google Scholar 

  35. Newman, M. E. J. (2004b). Coauthorship networks and patterns of scientific collaboration. Proceedings of the National Academy of Sciences of the U S A, 101(Suppl 1), 5200–5205.

    Article  Google Scholar 

  36. Noyons, E. C. M., Moed, H. F., & van Raan, A. F. J. (1999). Integrating research performance analysis and science mapping. Scientometrics, 46(3), 591–604.

    Article  Google Scholar 

  37. Palla, G., Derényi, I., Farkas, I., & Vicsek, T. (2005). Uncovering the overlapping community structure of complex networks in nature and society. Nature, 435, 814–818.

    Article  Google Scholar 

  38. Radicchi, F., Castellano, C., Cecconi, F., Loreto, V., & Parisi, D. (2004). Defining and identifying communities in networks. Proceedings of the National Academy of Sciences of the U S A, 101(9), 2658–2663.

    Article  Google Scholar 

  39. Reichardt, J., & BornHoldt, S. (2004). Detecting fuzzy community structures in complex networks with a potts model. Physical Review Letters, 93(21), 218701-1–218701-4.

    Article  Google Scholar 

  40. Seglen, P. O., & Aksnes, D. W. (2000). Scientific productivity and group size: a bibliometric analysis of Norwegian microbiological research. Scientometrics, 49(1), 125–143.

    Article  Google Scholar 

  41. Smith, D., & Katz, J. S. (2000). Collaborative approaches to research. Brighton: Science Policy Research Unit.

    Google Scholar 

  42. Subramanyam, K. (1983). Bibliometric studies of research collaboration: A review. Journal of Information Science, 6(1), 33–38.

    Article  Google Scholar 

  43. Vargas-Quesada, B., & Moya-Anegón, F. (2007). Visualizing the structure of science. Berlin: Springer.

    Google Scholar 

  44. von Tunzelmann, N., Ranga, M., Martin, B. R., & Geuna, A. (2003). The effects of size on research performance: A SPRU review. Brighton: University of Sussex.

    Google Scholar 

  45. Vuckovic-Dekic, L. (2003). Authoship-coauthorship. Archive of Oncology, 11(3), 211–212.

    Article  Google Scholar 

  46. White, H. D., & McCain, K. W. (1997). Visualization of literatures. Annual Review of Information Science and Technology, 32, 99–168.

    Google Scholar 

  47. W3C. Scalable Vector Graphics (SVG) Full 1.2 Specification. [Online]. World Wide Web Consortium, 2005. Retrieved October 6, 2007, from http://www.w3.org/TR/SVG12.

  48. Wu, F., & Huberman, B. A. (2004). Finding communities in linear time: A physics approach. European Physical Journal B, 38(2), 331–338.

    Article  Google Scholar 

  49. Zulueta, M. A., & Bordons, M. (1999). A global approach to the study of teams in multidisciplinary research areas through bibliometric indicators. Research Evaluation, 8(2), 111–118.

    Article  Google Scholar 

  50. Zulueta, M. A., Cabrero, A., & Bordons, M. (1999). Identificación y estudio de grupos de investigación a través de indicadores bibliométricos. Revista Española de Documentación Científica, 23(3), 333–347.

    Google Scholar 

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Correspondence to Antonio Perianes-Rodríguez.

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Perianes-Rodríguez, A., Olmeda-Gómez, C. & Moya-Anegón, F. Detecting, identifying and visualizing research groups in co-authorship networks. Scientometrics 82, 307–319 (2010). https://doi.org/10.1007/s11192-009-0040-z

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Keywords

  • Scientific collaboration
  • Research groups
  • Coauthorship
  • Network analysis
  • Information visualization