Scientific collaboration patterns vary with scholars’ academic ages
- 786 Downloads
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.
KeywordsScientific collaboration Academic age Collaboration pattern
Funding was provided by the Graduate Education Reform Fund of DUT (Grant No. JG2016022).
- 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
- 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.
- 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
- 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
- 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
- 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
- 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