Abstract
Advising and mentoring Ph.D. students is an increasingly important aspect of the academic profession. We define and interpret a family of metrics (collectively referred to as “a-indices”) that can be applied to “ranking academic advisors” using the academic genealogical records of scientists, with the emphasis on taking into account not only the number of students advised by an individual, but also subsequent academic advising records of those students. We also define and calculate the extensions of the proposed indices that account for student co-advising (referred to as “adjusted a-indices”). Finally, we extend the proposed metrics to ranking universities and countries with respect to their “collective” advising impacts. To illustrate the proposed metrics, we consider the social network of over 200,000 mathematicians (as of July 2018) constructed using the Mathematics Genealogy Project data: the network nodes represent the mathematicians who have completed Ph.D. degrees, and the directed edges connect advisors with their students.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
Of course, the out-degree of a node, that is, the number of advised students, is the simplest measure that assesses the more immediate advising impact; however, it is way too myopic. Indeed, the graduated students placed into an academic environment continue to boost the academic prowess of their alma-mater, compared, e.g., to the students who leave academia for the industry for good.
References
Arslan, E., Gunes, M.H., Yuksel, M.: Analysis of academic ties: a case study of mathematics genealogy. In: GLOBECOM Workshops (GC Wkshps), pp. 125–129. IEEE (2011)
Boldi, P., Vigna, S.: Axioms for centrality. Internet Math. 10(3–4), 222–262 (2014)
Broido, A.D., Clauset, A.: Scale-free networks are rare (2018). arXiv preprint arXiv:1801.03400
Gargiulo, F., Caen, A., Lambiotte, R., Carletti, T.: The classical origin of modern mathematics. EPJ Data Sci. 5(1), 26 (2016)
Jackson, M.O.: Social and Economic Networks. Princeton University Press, Princeton (2010)
Malmgren, R.D., Ottino, J.M., Amaral, L.A.N.: The role of mentorship in protégé performance. Nature 465(7298), 622 (2010)
Marchiori, M., Latora, V.: Harmony in the small-world. Phys. A: Stat. Mech. Appl. 285(3–4), 539–546 (2000)
Myers, S.A., Mucha, P.J., Porter, M.A.: Mathematical genealogy and department prestige. Chaos Interdiscip. J. Nonlinear Sci. 21(4), 041104 (2011)
Rossi, L., Freire, I.L., Mena-Chalco, J.P.: Genealogical index: a metric to analyze advisor-advisee relationships. J. Inf. 11(2), 564–582 (2017)
Taylor, D., Myers, S.A., Clauset, A., Porter, M.A., Mucha, P.J.: Eigenvector-based centrality measures for temporal networks. Multiscale Model. Simul. 15(1), 537–574 (2017)
Tsakas, N.: On decay centrality. BE J. Theor. Econ. (2016)
Acknowledgements
Work of A. Semenov was funded in part by the AFRL European Office of Aerospace Research and Development (grant no. FA9550-17-1-0030). This material is based upon work supported by the AFRL Mathematical Modeling and Optimization Institute.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Semenov, A., Veremyev, A., Nikolaev, A., Pasiliao, E.L., Boginski, V. (2018). Ranking Academic Advisors: Analyzing Scientific Advising Impact Using MathGenealogy Social Network. In: Chen, X., Sen, A., Li, W., Thai, M. (eds) Computational Data and Social Networks. CSoNet 2018. Lecture Notes in Computer Science(), vol 11280. Springer, Cham. https://doi.org/10.1007/978-3-030-04648-4_37
Download citation
DOI: https://doi.org/10.1007/978-3-030-04648-4_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04647-7
Online ISBN: 978-3-030-04648-4
eBook Packages: Computer ScienceComputer Science (R0)