, Volume 82, Issue 2, pp 307–319 | Cite as

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

  • Antonio Perianes-RodríguezEmail author
  • Carlos Olmeda-Gómez
  • Félix Moya-Anegón


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.


Scientific collaboration Research groups Coauthorship Network analysis Information visualization 


  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.Google Scholar
  2. Beaver, D., & Rosen, R. (1978). Studies in scientific collaboration. Part I. The professional origins of scientific co-authorship. Scientometrics, 1, 65–84.CrossRefGoogle Scholar
  3. Bordons, M., & Zulueta, M. A. (1997) Comparison of research team activity in two biomedical fields. Scientometrics, 40(3), 423–436.CrossRefGoogle 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.Google Scholar
  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.CrossRefGoogle 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.Google Scholar
  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.CrossRefGoogle Scholar
  11. Cohen, J. E. (1991). Size, age and productivity of scientific and technical research groups. Scientometrics, 20(3), 395–416.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle Scholar
  17. Gálvez, C., & Moya-Anegón, F. (2007). Standardizing formats of corporate source data. Scientometrics, 70(1), 3–26.CrossRefGoogle Scholar
  18. Geroimenko, V., & Chen, C. (2005). Visualizing information using SVG and X3D: XML-based technologies for the XML-based Web. London: Springer.zbMATHCrossRefGoogle 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
  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).Google Scholar
  22. Kamada, T., & Kawai, S. (1989). An algorithm for drawing general undirected graphs. Information Processing Letters, 31(1), 7–15.zbMATHCrossRefMathSciNetGoogle Scholar
  23. Katz, J. S. (1992). Bibliometric assessment of intranational University-University collaboration. Dissertation, University of Sussex, Brighton.Google Scholar
  24. Katz, J. S., & Martin, B. R. (1997). What is research collaboration? Research Policy, 26, 1–18.CrossRefGoogle Scholar
  25. Laudel, G. (2002). What do we measure by co-authorship? Research Evaluation, 11(1), 3–15.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle Scholar
  28. Melin, G., & Persson, O. (1996). Studying research collaboration using co-authorships. Scientometrics, 36(3), 363–377.CrossRefGoogle 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).Google Scholar
  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.CrossRefGoogle 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.CrossRefGoogle Scholar
  34. Newman, M. E. J. (2004a). Detecting community structure in networks. European Physical Journal B, 38, 321–330.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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
  48. Wu, F., & Huberman, B. A. (2004). Finding communities in linear time: A physics approach. European Physical Journal B, 38(2), 331–338.CrossRefGoogle 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.CrossRefGoogle 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

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2009

Authors and Affiliations

  • Antonio Perianes-Rodríguez
    • 1
    • 3
    Email author
  • Carlos Olmeda-Gómez
    • 1
    • 3
  • Félix Moya-Anegón
    • 2
    • 3
  1. 1.SCImago Research Group. Department of Library and Information ScienceCarlos III University of MadridGetafeSpain
  2. 2.Department of Information ScienceUniversity of GranadaGranadaSpain
  3. 3.SCImago Research GroupUniversity of GranadaGranadaSpain

Personalised recommendations