, Volume 89, Issue 1, pp 381–396 | Cite as

Community structure and patterns of scientific collaboration in Business and Management

  • T. S. Evans
  • R. Lambiotte
  • P. Panzarasa


This paper investigates the role of homophily and focus constraint in shaping collaborative scientific research. First, homophily structures collaboration when scientists adhere to a norm of exclusivity in selecting similar partners at a higher rate than dissimilar ones. Two dimensions on which similarity between scientists can be assessed are their research specialties and status positions. Second, focus constraint shapes collaboration when connections among scientists depend on opportunities for social contact. Constraint comes in two forms, depending on whether it originates in institutional or geographic space. Institutional constraint refers to the tendency of scientists to select collaborators within rather than across institutional boundaries. Geographic constraint is the principle that, when collaborations span different institutions, they are more likely to involve scientists that are geographically co-located than dispersed. To study homophily and focus constraint, the paper will argue in favour of an idea of collaboration that moves beyond formal co-authorship to include also other forms of informal intellectual exchange that do not translate into the publication of joint work. A community-detection algorithm for formalising this perspective will be proposed and applied to the co-authorship network of the scientists that submitted to the 2001 Research Assessment Exercise in Business and Management in the UK. While results only partially support research-based homophily, they indicate that scientists use status positions for discriminating between potential partners by selecting collaborators from institutions with a rating similar to their own. Strong support is provided in favour of institutional and geographic constraints. Scientists tend to forge intra-institutional collaborations; yet, when they seek collaborators outside their own institutions, they tend to select those who are in geographic proximity. The implications of this analysis for tie creation in joint scientific endeavours are discussed.


Collaboration networks Community structure Intra- and inter-institutional collaborations Geographic distance Research specialty 


  1. Allen, T. (1977). Managing the flow of technology. Cambridge, MA: MIT Press.Google Scholar
  2. Baker, M. J., & Gabbott, M. (2002). The assessment of research. International Journal of Management Education, 2(3), 3–15.Google Scholar
  3. Ball, D. F., & Butler, J. (2004). The implicit use of business concepts in the UK research assessment exercise. R & D Management, 34(1), 87–97.CrossRefGoogle Scholar
  4. Barabási, A. L., Jeong, H., Neda, Z., Ravasz, E., Schubert, A., & Vicsek, T. (2002). Evolution of the social network of scientific collaborations. Physica, 311, 590–614.MathSciNetzbMATHCrossRefGoogle Scholar
  5. Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks, Journal of Statistical Mechanics, P10008.Google Scholar
  6. Börner, K., Chen, C. M., & Boyack, K. W. (2003). Visualizing knowledge domains. Annual Review of Information Science and Technology, 37, 179–255.CrossRefGoogle Scholar
  7. Boyack, K. W., Klavans, R., & Börner, K. (2005). Mapping the backbone of science. Scientometrics, 64, 351–374.CrossRefGoogle Scholar
  8. Braunerhjelm, P., & Feldman, M. (2006). Cluster genesis: Technology-based industrial development. Oxford: Oxford University Press.Google Scholar
  9. Cairncross, F. (1997). The death of distance. Cambridge MA: Harvard University Press.Google Scholar
  10. Camic, C. (1992). Reputation and predecessor selection: Parsons and the institutionalists. American Sociological Review, 57, 421–445.CrossRefGoogle Scholar
  11. Chen, C. M. (2003). Mapping scientific frontiers: The quest for knowledge visualization. Berlin: Springer.Google Scholar
  12. Chung, S., Singh, H., & Lee, K. (2000). Complementarity, status similarity and social capital as drivers of alliance formation. Strategic Management Journal, 21, 1–22.CrossRefGoogle Scholar
  13. Colizza, V., Flammini, A., Serrano, M. A., & Vespignani, A. (2006). Detecting rich-club ordering in complex networks. Nature Physics, 2, 110–115.CrossRefGoogle Scholar
  14. Cooper, C., & Otley, D. (1998). The 1996 research assessment exercise for business and management. British Journal of Management, 9, 73–89.CrossRefGoogle Scholar
  15. Crane, D. (1972). Invisible colleges. Chicago: University of Chicago Press.Google Scholar
  16. Cummings, J. N., & Kiesler, S. (2007). Coordination costs and project outcomes in multi-university collaborations. Research Policy, 36(10), 138–152.CrossRefGoogle Scholar
  17. De Castro, R., & Grossman, J. W. (1999). Famous trails to Paul Erdös. Mathematical Intelligence, 21, 51–63.MathSciNetzbMATHCrossRefGoogle Scholar
  18. Ding, Y., Foo, S., & Chowdhury, G. (1999). A bibliometric analysis of collaboration in the field of information retrieval. International Information and Library Review, 30, 367–376.CrossRefGoogle Scholar
  19. Expert, P., Evans, T. S., Blondel, V. D., & Lambiotte, R. (2011). Uncovering space-independent communities in spatial networks. Proceedings of the National Academy of Science, 108, 7663–7668.CrossRefGoogle Scholar
  20. Feld, S. L. (1981). The focused organization of social ties. American Journal of Sociology, 86, 1015–1035.CrossRefGoogle Scholar
  21. Fortunato, S. (2010). Community detection in graphs. Physics Reports, 486, 75–174.MathSciNetCrossRefGoogle Scholar
  22. Frenken, K., Hardeman, S., & Hoekman, J. (2009). Spatial scientometrics: Towards a cumulative research program. Journal of Informetrics, 3, 222–232.CrossRefGoogle Scholar
  23. Gertler, M. S. (2003). Tacit knowledge and the economic geography of context or the undefinable tacitness of being (there). Journal of Economic Geography, 3, 75–99.CrossRefGoogle Scholar
  24. Greenfeld, L. (1989). Different worlds: A sociological study of taste, choice and success in art. Cambridge, England: Cambridge University Press.CrossRefGoogle Scholar
  25. Hellsten, I., Lambiotte, R., Scharnhorst, A., & Ausloos, M. (2007). Self-citations, co-authorships and keywords: A new method for detecting scientists’ field mobility? Scientometrics, 72, 469–486.CrossRefGoogle Scholar
  26. Higher Education & Research Opportunities (HERO) in the United Kingdom (2001). A Guide to the 2001 Research Assessment Exercise.Google Scholar
  27. Hidalgo, C. A., Klinger, B., Barabási, A. L., & Hausmann, R. (2007). The product space conditions the development of nations. Science, 317, 482–487.CrossRefGoogle Scholar
  28. Jaffe, A. B., Trajtenberg, M., & Henderson R. (1993). Geographic localization of knowledge spillovers as evidenced by patent citations. Quarterly Journal of Economics, 108(3), 578–598.CrossRefGoogle Scholar
  29. Jones, B. F., Wuchty, S., & Uzzi, B. (2008). Multi-university research teams: Shifting impact, geography, and stratification in science. Science, 322(5905), 1259–1262.CrossRefGoogle Scholar
  30. Katz, J. S., & Martin, B. R. (1997). What is research collaboration?. Research Policy, 26, 1–18.CrossRefGoogle Scholar
  31. Kossinets, G., & Watts, D. J. (2006). Empirical analysis of an evolving social network. Science, 311, 88–90.MathSciNetCrossRefGoogle Scholar
  32. Kraut, R., Egido, C., & Galegher, J. (1990). Patterns of contact and communication in scientific research collaboration. In J. Galegher, R. Kraut, & C. Egido (Eds.) Intellectual teamwork: Social and technological bases of cooperative work (pp. 149–171). Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
  33. Laband, D. N., & Tollison, R. D. (2000). Intellectual collaboration. Journal of Political Economy, 108, 632–662.CrossRefGoogle Scholar
  34. Lambiotte, R., & Panzarasa, P. (2009). Communities, knowledge creation and information diffusion. Journal of Informetrics, 3, 180190.CrossRefGoogle Scholar
  35. Latour, B. (1987). Science in action. Cambridge, MA: Harvard University Press.Google Scholar
  36. Lazarsfeld, P. F., & Merton, R. K. (1954). Friendship as social process: A substantive and methodological analysis. In M. Berger, T. Abel & C. Page (Eds.) Freedom and control in modern society (pp. 18–66). New York, NY: Van Nostrand.Google Scholar
  37. Leydesdorff, L., & Ward, J. (2005). Science shops: Adoscope of science-society collaborations in Europe. Public Understanding of Science, 14, 353–372.CrossRefGoogle Scholar
  38. Leydesdorff, L., & Rafols, I. (2008). A global map of science based on the ISI subject categories. Journal of the American Society for Information Science, 60, 348–362.Google Scholar
  39. Lorange, P., & Roos, J. (1992). Strategic alliances. Cambridge, MA: Blackwell.Google Scholar
  40. McPherson, J. M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27, 415–444.CrossRefGoogle Scholar
  41. Monge, P., Rothman, L., Eisenberg, E., Miller, K., & Kirste, K. (1985). The dynamics of organizational proximity. Management Science, 31, 1129–1141.CrossRefGoogle Scholar
  42. Moody, J. (2004). The structure of social science collaboration network: Disciplinary cohesion from 1963 to 1999. American Sociological Review, 69, 213–238.CrossRefGoogle Scholar
  43. Newman, M. E. J. (2001a). The structure of scientific collaboration networks. Proceedings of the National Academy of Science, 98, 404–409.zbMATHCrossRefGoogle Scholar
  44. Newman, M. E. J. (2001b). Scientific collaboration networks. I. Network construction and fundamental results. Physical Review E, 64, 016131.CrossRefGoogle Scholar
  45. Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69, 026113.CrossRefGoogle Scholar
  46. Opsahl, T., Colizza, V., Panzarasa, P., & Ramasco, J. J. (2008). Prominence and control: The weighted rich-club effect. Physical Review Letters, 101, 168702.CrossRefGoogle Scholar
  47. Podolny, J. M. (1994). Market uncertainty and the social character of economic exchange. Administrative Science Quarterly, 39, 458–483.CrossRefGoogle Scholar
  48. Podolny, J. M., & Stuart, T. E. (1995). A role-based ecology of technological change. American Journal of Sociology, 100, 1224–1260.CrossRefGoogle Scholar
  49. Price, D. J. de S. (1965). Networks of scientific papers. Science, 149, 510–515.CrossRefGoogle Scholar
  50. Reagans, R. (2005). Preferences, identity, and competition: Predicting tie strength from demographic data. Management Science, 51(9), 1374–1383.CrossRefGoogle Scholar
  51. Reichardt, J., & Bornholdt, S. (2004). Detecting fuzzy community structures in complex networks with a Potts model. Physical Review Letters, 93, 218701.CrossRefGoogle Scholar
  52. Rosvall, M., & Bergstrom, C. T. (2008). Maps of random walks on complex networks reveal community structure. Proceedings of the Natonal Academy of Science, 105, 1118.CrossRefGoogle Scholar
  53. Scharnhorst, A., & Ebeling, W. (2005). Evolutionary search agents in complex landscapes—A new model for the role of competence and meta-competence (EVOLINO and other simulation tools), arXiv:0511232.Google Scholar
  54. Traud, A. L., Kelsic, E. D., Mucha, P. J., & Porter, M. A. (2010) Community structure in online collegiate social networks, arXiv:0809.0690.Google Scholar
  55. Wallace, M. L., & Gingras, Y. (2008). A new approach for detecting scientific specialties from raw cocitation networks. Journal of the American Society for Information Science, 60, 240–246.Google Scholar
  56. Whitfield, J. (2008). Group theory. Nature, 455, 720–723.CrossRefGoogle Scholar
  57. Wuchty, S., Jones, B. F., & Uzzi, B. (2007). The increasing dominance of teams in production of knowledge. Science, 316, 1036–1039.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2011

Authors and Affiliations

  1. 1.Physics Department and Complexity & NetworksImperial CollegeLondonUK
  2. 2.Department of Mathematics and NaxysUniversity of NamurNamurBelgium
  3. 3.School of Business and ManagementQueen Mary University of LondonLondonUK

Personalised recommendations