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Scientific collaboration patterns vary with scholars’ academic ages

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Abstract

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.

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Notes

  1. http://dblp.uni-trier.de/xml/.

  2. http://journals.aps.org/datasets.

References

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Barabási, A.-L. (2016). Network science. Cambridge: Cambridge University Press.

    MATH  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

  • 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.

    Article  Google Scholar 

  • Granovetter, M. (1985). Economic action and social structure: The problem of embeddedness. American Journal of Sociology, 91(3), 481–510.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Katz, J. S., & Martin, B. R. (1997). What is research collaboration? Research Policy, 26(1), 1–18.

    Article  Google Scholar 

  • Ke, Q., & Ahn, Y.-Y. (2014). Tie strength distribution in scientific collaboration networks. Physical Review E, 90(3), 032804.

    Article  Google Scholar 

  • 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.

  • 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.

    Article  Google Scholar 

  • 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 

  • Lee, S., & Bozeman, B. (2005). The impact of research collaboration on scientific productivity. Social Studies of Science, 35(5), 673–702.

    Article  Google Scholar 

  • 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.

  • Ley, M. (2009). Dblp: Some lessons learned. Proceedings of the VLDB Endowment, 2(2), 1493–1500.

    Article  MathSciNet  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Milojević, S. (2014). Principles of scientific research team formation and evolution. Proceedings of the National Academy of Sciences, 111(11), 3984–3989.

    Article  Google Scholar 

  • Newman, M. E. (2001a). Clustering and preferential attachment in growing networks. Physical Review E, 64(2), 025102.

    Article  Google Scholar 

  • Newman, M. E. (2001b). Scientific collaboration networks. I. Network construction and fundamental results. Physical Review E, 64(1), 016131.

    Article  Google Scholar 

  • Newman, M. E. (2001c). The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences, 98(2), 404–409.

    Article  MathSciNet  MATH  Google Scholar 

  • Newman, M. E. (2004). Coauthorship networks and patterns of scientific collaboration. Proceedings of the National Academy of Sciences, 101(suppl 1), 5200–5205.

    Article  Google Scholar 

  • Opsahl, T., Agneessens, F., & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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).

  • Sinatra, R., Wang, D., Deville, P., Song, C., & Barabási, A.-L. (2016). Quantifying the evolution of individual scientific impact. Science, 354(6312), aaf5239.

    Article  Google Scholar 

  • 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.

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Wuchty, S., Jones, B. F., & Uzzi, B. (2007). The increasing dominance of teams in production of knowledge. Science, 316(5827), 1036–1039.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

  • 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.

    Article  Google Scholar 

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Acknowledgements

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

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Correspondence to Xiangjie Kong.

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Wang, W., Yu, S., Bekele, T.M. et al. Scientific collaboration patterns vary with scholars’ academic ages. Scientometrics 112, 329–343 (2017). https://doi.org/10.1007/s11192-017-2388-9

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