, Volume 102, Issue 1, pp 285–306 | Cite as

Embedded information structures and functions of co-authorship networks: evidence from cancer research collaboration in India

  • Avinash Kshitij
  • Jaideep Ghosh
  • Brij Mohan Gupta


In this exploratory study, we analyze co-authorship networks of collaborative cancer research in India. The complete network is constructed from bibliometric data on published scholarly articles indexed in two well-known electronic databases covering two 6-year windows from 2000 to 2005 and 2006 to 2011 inclusive. Employing a number of important metrics pertaining to the underlying topological structures of the network, we discusses implications for effective policies to enhance knowledge generation and sharing in cancer research in the country. With some modifications, our methods can be applied without difficulty to examine policy structure of related disciplines in other countries of the world.


Cancer research Collaboration network Network analysis 



The authors wish to acknowledge two anonymous reviewers of Scientometrics for suggesting a few improvements in the paper. Thanks are also due to Dr. P. Banerjee, Director of CSIR – NISTADS, for helpful comments on an early version of the paper. Jaideep Ghosh would like to thank the Department of Science & Technology, Government of India, for financial support to carry out this work.


  1. Acedo, F. J., Barroso, C., Casanueva, C., & Galán, J. L. (2006). Co-authorship in management and organizational Studies: An empirical and network analysis. Journal of Management Studies, 43(4), 957–983.CrossRefGoogle Scholar
  2. Albert, R., & Barabási, A.-L. (2002). Statistical mechanics of complex networks. Reviews of Modern Physics, 74(1), 47–97.CrossRefMathSciNetMATHGoogle Scholar
  3. Barabási, A.-L., Jeong, H., Ravasz, R., Néda, Z., Vicsek, T., & Schubert, A. (2002). On the topology of the scientific collaboration networks. Physica, 311, 590–614.CrossRefMathSciNetMATHGoogle Scholar
  4. Batagelj, V., Mrvar, A., & Zaveršnik, M. (2012). Obtained from Accessed 2 Jan 2014.
  5. Bozeman, B., & Corley, E. (2004). Scientists’ collaboration strategies: Implications for scientific and technical human capital. Research Policy, 33(4), 599–616.CrossRefGoogle Scholar
  6. Burt, R. S. (2004). Structural holes and good ideas. American Journal of Sociology, 110(2), 349–399.CrossRefGoogle Scholar
  7. Eckhouse, S., Lewison, G., & Sullivan, R. (2008). Trends in the global funding and activity of cancer research. Molecular Oncology, 2(1), 20–32.CrossRefGoogle Scholar
  8. Ghosh, J., & Kshitij, A. (2014). An integrated examination of collaboration co-authorship networks through structural cohesion, holes, hierarchy, and percolating clusters. Journal of the American Society for Information Science and Technology. Early view doi: 10.1002/asi.23058. Accessed 3 April 2014.
  9. Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology, 78(6), 1360–1380.CrossRefGoogle Scholar
  10. Hara, N., Solomon, P., Kim, S.-L., & Sonnenwald, D. H. (2003). An emerging view of scientific collaboration: Scientists’ perspectives on collaboration and factors that impact collaboration. Journal of the American Society for Information Science and Technology, 54(10), 952–965.CrossRefGoogle Scholar
  11. Hofstede, G. (1983). National culture in four dimensions: A research-based theory of cultural differences among nations. International Studies of Management and Organization, 13(1–2), 46–74.Google Scholar
  12. Lewison, G., Purushotham, A., Mason, M., McVie, G., & Sullivan, R. (2010). Understanding the impact of public policy on cancer research: A bibliometric approach. European Journal of Cancer, 46(5), 912–919.CrossRefGoogle Scholar
  13. Leydesdorff, L., & Wagner, C. S. (2008). International collaboration in science and the formation of a core group. Journal of Informetrics, 2(4), 317–332.CrossRefGoogle Scholar
  14. Lotka, A. J. (1926). The frequency distribution of scientific productivity. Journal of the Washington Academy of Science, 16(2), 317–323.Google Scholar
  15. Melin, G., & Persson, O. (1996). Studying research collaboration through co-authorships. Scientometrics, 36(3), 363–377.CrossRefGoogle Scholar
  16. Milgram, S. (1967). The small-world problem. Psychology Today, 1(1), 61–67.MathSciNetGoogle Scholar
  17. Milojević, S. (2013). Accuracy of simple, initials-based methods for author name disambiguation. Journal of Informetrics, 7(4), 767–773.CrossRefGoogle Scholar
  18. Newman, M. E. J. (2001a). The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences, 98(2), 404–409.CrossRefMathSciNetMATHGoogle Scholar
  19. Newman, M. E. J. (2001b). Scientific collaboration networks. I. Network construction and fundamental results. Physical Review E, 64 (016131), 016131-1–016131-8.Google Scholar
  20. Newman, M. E. J. (2003). The structure and function of complex networks. SIAM Review, 45(2), 167–256.CrossRefMathSciNetMATHGoogle Scholar
  21. Scott, J. (2000). Social network analysis: A handbook. London: Sage Publications.Google Scholar
  22. Shrum, W., Genuth, J., & Chompalov, I. (2007). Structures of scientific collaboration. Cambridge, Massachusetts: MIT Press.Google Scholar
  23. Wasserman, S., & Faust, K. (1994). Social network analysis. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
  24. Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393, 440–442.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2014

Authors and Affiliations

  • Avinash Kshitij
    • 1
    • 2
  • Jaideep Ghosh
    • 2
  • Brij Mohan Gupta
    • 2
  1. 1.Centre for Studies in Science Policy, School of Social SciencesJawaharlal Nehru UniversityNew DelhiIndia
  2. 2.Council of Scientific and Industrial Research – National Institute of ScienceTechnology and Development Studies (CSIR – NISTADS)New DelhiIndia

Personalised recommendations