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Genealogical index: A metric to analyze advisor–advisee relationships

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  • Rossi, Luciano
  • Freire, Igor L.
  • Mena-Chalco, Jesús P.

Abstract

Academic genealogy can be defined as the study of intellectual heritage that is undertaken through the relationship between a professor (advisor/mentor) and student (advisee) and on the basis of these ties, it establishes a social framework that is generally represented by an academic genealogy graph. Obtaining relevant knowledge of academic genealogy graphs makes it possible to analyse the academic training of scientific communities, and discover ancestors or forbears who had special skills and talents. The use of metrics for characterizing this kind of graph is an active form of knowledge extraction. In this paper, we set out a formal definition of a metric called ‘genealogical index’, which can be used to assess how far researchers have affected advisor–advisee relationships. This metric is based on the bibliometrics h-index and its definition can be broadened to measure the effect of researchers on several generations of scientists. A case study is employed that includes an academic genealogy graph consisting of more than 190,000 Ph.D.s registered in the Mathematics Genealogy Project. Additionally, we compare the genealogical indices obtained from both the Fields Medal and Wolf Prize winners, and found that the latter has had a greater impact than the former.

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  • Rossi, Luciano & Freire, Igor L. & Mena-Chalco, Jesús P., 2017. "Genealogical index: A metric to analyze advisor–advisee relationships," Journal of Informetrics, Elsevier, vol. 11(2), pages 564-582.
    • Handle: RePEc:eee:infome:v:11:y:2017:i:2:p:564-582
    DOI: 10.1016/j.joi.2017.04.001
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    References listed on IDEAS

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    2. Dhananjay Kumar & Plaban Kumar Bhowmick & Sumana Dey & Debarshi Kumar Sanyal, 2023. "On the banks of Shodhganga: analysis of the academic genealogy graph of an Indian ETD repository," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(7), pages 3879-3914, July.
    3. Meijun Liu & Sijie Yang & Yi Bu & Ning Zhang, 2023. "Female early-career scientists have conducted less interdisciplinary research in the past six decades: evidence from doctoral theses," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-16, December.
    4. Rafael J. P. Damaceno & Luciano Rossi & Rogério Mugnaini & Jesús P. Mena-Chalco, 2019. "The Brazilian academic genealogy: evidence of advisor–advisee relationships through quantitative analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(1), pages 303-333, April.
    5. Jianhua Hou & Bili Zheng & Yang Zhang & Chaomei Chen, 2021. "How do Price medalists’ scholarly impact change before and after their awards?," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5945-5981, July.
    6. Zhu, Wanying & Jin, Ching & Ma, Yifang & Xu, Cong, 2023. "Earlier recognition of scientific excellence enhances future achievements and promotes persistence," Journal of Informetrics, Elsevier, vol. 17(2).
    7. Wuestman, Mignon & Wanzenböck, Iris & Frenken, Koen, 2023. "Local peer communities and future academic success of Ph.D. candidates," Research Policy, Elsevier, vol. 52(8).
    8. Dominik P. Heinisch & Guido Buenstorf, 2018. "The next generation (plus one): an analysis of doctoral students’ academic fecundity based on a novel approach to advisor identification," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 351-380, October.
    9. Fu, Zhongmeng & Cao, Yuan & Zhao, Yong, 2024. "Identifying knowledge evolution in computer science from the perspective of academic genealogy," Journal of Informetrics, Elsevier, vol. 18(2).
    10. Debarshi Kumar Sanyal & Sumana Dey & Partha Pratim Das, 2020. "gm-index: a new mentorship index for researchers," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(1), pages 71-102, April.
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    13. Kumar, Dhananjay & Bhowmick, Plaban Kumar & Paik, Jiaul H, 2023. "Researcher influence prediction (ResIP) using academic genealogy network," Journal of Informetrics, Elsevier, vol. 17(2).

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