, Volume 112, Issue 1, pp 329–343 | Cite as

Scientific collaboration patterns vary with scholars’ academic ages

  • Wei Wang
  • Shuo Yu
  • Teshome Megersa Bekele
  • Xiangjie Kong
  • Feng Xia


Scientists may encounter many collaborators of different academic ages throughout their careers. Thus, they are required to make essential decisions to commence or end a creative partnership. This process can be influenced by strategic motivations because young scholars are pursuers while senior scholars are normally attractors during new collaborative opportunities. While previous works have mainly focused on cross-sectional collaboration patterns, this work investigates scientific collaboration networks from scholars’ local perspectives based on their academic ages. We aim to harness the power of big scholarly data to investigate scholars’ academic-age-aware collaboration patterns. From more than 621,493 scholars and 2,646,941 collaboration records in Physics and Computer Science, we discover several interesting academic-age-aware behaviors. First, in a given time period, the academic age distribution follows the long-tail distribution, where more than 80% scholars are of young age. Second, with the increasing of academic age, the degree centrality of scholars goes up accordingly, which means that senior scholars tend to have more collaborators. Third, based on the collaboration frequency and distribution between scholars of different academic ages, we observe an obvious homophily phenomenon in scientific collaborations. Fourth, the scientific collaboration triads are mostly consisted with beginning scholars. Furthermore, the differences in collaboration patterns between these two fields in terms of academic age are discussed.


Scientific collaboration Academic age Collaboration pattern 



Funding was provided by the Graduate Education Reform Fund of DUT (Grant No. JG2016022).


  1. Badar, K., Frantz, T. L., & Jabeen, M. (2016). Research performance and degree centrality in co-authorship networks: The moderating role of homophily. Aslib Journal of Information Management, 68(6), 756–771.CrossRefGoogle Scholar
  2. Badar, K., Hite, J. M., & Ashraf, N. (2015). Knowledge network centrality, formal rank and research performance: Evidence for curvilinear and interaction effects. Scientometrics, 105(3), 1553–1576.CrossRefGoogle Scholar
  3. Badar, K., Hite, M. J., & Badir, F. Y. (2014). The moderating roles of academic age and institutional sector on the relationship between co-authorship network centrality and academic research performance. Aslib Journal of Information Management, 66(1), 38–53.CrossRefGoogle Scholar
  4. Barabási, A.-L. (2016). Network science. Cambridge: Cambridge University Press.zbMATHGoogle Scholar
  5. Borrett, S. R., Moody, J., & Edelmann, A. (2014). The rise of network ecology: Maps of the topic diversity and scientific collaboration. Ecological Modelling, 293, 111–127.CrossRefGoogle Scholar
  6. Çavuşoğlu, A., & Türker, İ. (2014). Patterns of collaboration in four scientific disciplines of the turkish collaboration network. Physica A: Statistical Mechanics and its Applications, 413, 220–229.CrossRefGoogle Scholar
  7. Dong, Y., Yang, Y., Tang, J., Yang, Y., & Chawla, N. V. (2014). Inferring user demographics and social strategies in mobile social networks. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 15–24). ACM.Google Scholar
  8. Ferreira, A. A., Gonçalves, M. A., & Laender, A. H. (2012). A brief survey of automatic methods for author name disambiguation. Acm Sigmod Record, 41(2), 15–26.CrossRefGoogle Scholar
  9. Granovetter, M. (1985). Economic action and social structure: The problem of embeddedness. American Journal of Sociology, 91(3), 481–510.CrossRefGoogle Scholar
  10. Guimera, R., Uzzi, B., Spiro, J., & Amaral, L. A. N. (2005). Team assembly mechanisms determine collaboration network structure and team performance. Science, 308(5722), 697–702.CrossRefGoogle Scholar
  11. Katz, J. S., & Martin, B. R. (1997). What is research collaboration? Research Policy, 26(1), 1–18.CrossRefGoogle Scholar
  12. Ke, Q., & Ahn, Y.-Y. (2014). Tie strength distribution in scientific collaboration networks. Physical Review E, 90(3), 032804.CrossRefGoogle Scholar
  13. King, M. M., Bergstrom, C. T., Correll, S. J., Jacquet, J. & West, J. D. (2016). Men set their own cites high: Gender and self-citation across fields and over time. arXiv preprint arXiv:1607.00376.
  14. Kong, X., Jiang, H., Yang, Z., Xu, Z., Xia, F., & Tolba, A. (2016). Exploiting publication contents and collaboration networks for collaborator recommendation. PloS ONE, 11(2), e0148492.CrossRefGoogle Scholar
  15. Lazarsfeld, P. F., Merton, R. K., et al. (1954). Friendship as a social process: A substantive and methodological analysis. Freedom and Control in Modern Society, 18(1), 18–66.Google Scholar
  16. Lee, S., & Bozeman, B. (2005). The impact of research collaboration on scientific productivity. Social Studies of Science, 35(5), 673–702.CrossRefGoogle Scholar
  17. Leskovec, J., Backstrom, L., Kumar, R., & Tomkins, A. (2008). Microscopic evolution of social networks. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 462–470). ACM.Google Scholar
  18. Ley, M. (2009). Dblp: Some lessons learned. Proceedings of the VLDB Endowment, 2(2), 1493–1500.MathSciNetCrossRefGoogle Scholar
  19. Lou, T., Tang, J., Hopcroft, J., Fang, Z., & Ding, X. (2013). Learning to predict reciprocity and triadic closure in social networks. TKDD, 7(2), 5.CrossRefGoogle Scholar
  20. Milojević, S. (2014). Principles of scientific research team formation and evolution. Proceedings of the National Academy of Sciences, 111(11), 3984–3989.CrossRefGoogle Scholar
  21. Newman, M. E. (2001a). Clustering and preferential attachment in growing networks. Physical Review E, 64(2), 025102.CrossRefGoogle Scholar
  22. Newman, M. E. (2001b). Scientific collaboration networks. I. Network construction and fundamental results. Physical Review E, 64(1), 016131.CrossRefGoogle Scholar
  23. Newman, M. E. (2001c). The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences, 98(2), 404–409.MathSciNetCrossRefzbMATHGoogle Scholar
  24. Newman, M. E. (2004). Coauthorship networks and patterns of scientific collaboration. Proceedings of the National Academy of Sciences, 101(suppl 1), 5200–5205.CrossRefGoogle Scholar
  25. Opsahl, T., Agneessens, F., & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251.CrossRefGoogle Scholar
  26. Ortega, J. L. (2014). Influence of co-authorship networks in the research impact: Ego network analyses from microsoft academic search. Journal of Informetrics, 8(3), 728–737.CrossRefGoogle Scholar
  27. Petersen, A. M. (2015). Quantifying the impact of weak, strong, and super ties in scientific careers. Proceedings of the National Academy of Sciences, 112(34), E4671–E4680.CrossRefGoogle Scholar
  28. Petersen, A. M., Fortunato, S., Pan, R. K., Kaski, K., Penner, O., Rungi, A., et al. (2014). Reputation and impact in academic careers. Proceedings of the National Academy of Sciences, 111(43), 15316–15321.CrossRefGoogle Scholar
  29. Schult, D. A., & Swart, P. (2008). Exploring network structure, dynamics, and function using networkx. In Proceedings of the 7th python in science conferences (SciPy 2008) (Vol. 2008, pp. 11–16).Google Scholar
  30. Sinatra, R., Wang, D., Deville, P., Song, C., & Barabási, A.-L. (2016). Quantifying the evolution of individual scientific impact. Science, 354(6312), aaf5239.CrossRefGoogle Scholar
  31. Sinha, A., Shen, Z., Song, Y., Ma, H., Eide, D., Hsu, B.-J. P., & Wang, K. (2015). An overview of microsoft academic service (mas) and applications. In Proceedings of the 24th international conference on World Wide Web (pp. 243–246). ACM.Google Scholar
  32. Sugimoto, C. R., Sugimoto, T. J., Tsou, A., Milojević, S., & Larivière, V. (2016). Age stratification and cohort effects in scholarly communication: A study of social sciences. Scientometrics, 109(2), 997–1016. doi: 10.1007/s11192-016-2087-y.CrossRefGoogle Scholar
  33. Tang, J., Fong, A. C., Wang, B., & Zhang, J. (2012). A unified probabilistic framework for name disambiguation in digital library. IEEE Transactions on Knowledge and Data Engineering, 24(6), 975–987.CrossRefGoogle Scholar
  34. Türker, İ., & Çavuşoğlu, A. (2016). Detailing the co-authorship networks in degree coupling, edge weight and academic age perspective. Chaos, Solitons and Fractals, 91, 386–392.CrossRefGoogle Scholar
  35. Wuchty, S., Jones, B. F., & Uzzi, B. (2007). The increasing dominance of teams in production of knowledge. Science, 316(5827), 1036–1039.CrossRefGoogle Scholar
  36. Xia, F., Chen, Z., Wang, W., Li, J., & Yang, L. T. (2014). Mvcwalker: Random walk-based most valuable collaborators recommendation exploiting academic factors. IEEE Transactions on Emerging Topics in Computing, 2(3), 364–375.CrossRefGoogle Scholar
  37. Zhao, Y., Wang, G., Yu, P. S., Liu, S. & Zhang, S. (2013). Inferring social roles and statuses in social networks. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 695–703). ACM.Google Scholar
  38. Zoëga, H., Valdimarsdóttir, U. A., & Hernández-Díaz, S. (2012). Age, academic performance, and stimulant prescribing for adhd: A nationwide cohort study. Pediatrics, 130(6), 1012–1018.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2017

Authors and Affiliations

  1. 1.School of SoftwareDalian University of TechnologyDalianChina
  2. 2.Key Laboratory for Ubiquitous Network and Service Software of Liaoning ProvinceDalianChina

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