Skip to main content
Log in

Locating active actors in the scientific collaboration communities based on interaction topology analyses

  • Published:
Scientometrics Aims and scope Submit manuscript

Abstract

While implementing a large-scale research project, it is necessary to appoint some principle scientists, and let each principle scientist lead a research group. In a scientific collaboration community, different scientists perform different roles while they implement the project, and some scientists may be more active than others; these active scientists often undertake the role of leadership or key coordinator in the project. Obviously, we should assign the role of principle scientists onto those active actors in the communities. In this paper, we present the model and algorithms for locating active actors in the community based on the analyses of scientists’ interaction topology, the actors with high connection degrees in the interaction topology can be considered as active ones. Finally, we make some case studies for our model and algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Leclerc, M., Gagné, J., International scientific cooperation: The continentalization of science, Scientometrics, 31(3) (1994): 261–292.

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Kretschmer, H., Cooperation structure, group size and productivity in research groups, Scientometrics, 7(1–2) (1985): 39–53.

    Article  Google Scholar 

  4. Newman, M. E. J., The structure of scientific collaboration networks, PNAS, 98(2) (2001): 404–409.

    Article  MATH  Google Scholar 

  5. Newman, M. E. J., Coauthorship networks and patterns of scientific collaboration, PNAS, (101) (1) (2004): 5200–5205.

  6. Profile of 973 Program. http://www.973.gov.cn/English/Index.aspx

  7. Girvan, M., Newman, M. E. J., Community structure in social and biological networks, Proc. Natl. Acad. Sci, 99 (2002): 8271–8276.

    Article  MathSciNet  Google Scholar 

  8. Wagner, C. S., L. Leydesdorff, Mapping the network of global science: comparing international co-authorships from 1990 to 2000. International Journal of Technology and Globalisation, 1(2) (2005): 185–208

    Article  Google Scholar 

  9. Jiang, Y. C., Xia, Z. Y., Zhang, S. Y., An adaptive adjusting mechanism for agents distributed blackboard architecture, Microprocessors and Microsystems, (Elsevier Science), 29(1) (2005): 9–20.

    Article  Google Scholar 

  10. Freeman, L., A set of measures of centrality based upon betweenness. Sociometry, 40 (1977): 35–41.

    Article  Google Scholar 

  11. Granovetter, M. S., The strength of weak ties, The American Journal of Sociology, 78(6) (May, 1973): 1360–1380.

    Article  Google Scholar 

  12. Burt, R. S., Structural Holes:The Social Structure of Competition. Cambridge, Massachusetts, Harvard University Press, 1992.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yichuan Jiang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Jiang, Y. Locating active actors in the scientific collaboration communities based on interaction topology analyses. Scientometrics 74, 471–482 (2008). https://doi.org/10.1007/s11192-007-1587-1

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-007-1587-1

Keywords

Navigation