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On the banks of Shodhganga: analysis of the academic genealogy graph of an Indian ETD repository

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Abstract

Academic genealogy graphs capture information about the lineage of researchers, encode how knowledge flows from advisors to proteges, and shed light on the birth and evolution of disciplines. In this paper, we study the academic genealogy graph/network (AGN) in Shodhganga which is the Indian Electronic Theses and Dissertations (ETD) database. We have disambiguated the names of the researchers in Shodhganga and constructed the Shodhganga-AGN, which we have analyzed with topological metrics proposed in the literature on general graphs as well as that on genealogy networks. The metrics studied have been able to identify the institutes and researchers that have played a significant role in the development of the Indian higher education system. The largest connected component of Shodhganga-AGN consists of 1356 researchers and 1437 advisor–advisee relationships. The component is dominated by researchers from science and is affiliated primarily with three institutions. We have also studied subgraphs in the genealogy network to identify supervision patterns, and found that most of the subgraph instances connect researchers within a single institution or subject. Thus, our study is a detailed insightful analysis of the academic genealogy of researchers indexed in Shodhganga, and captures the decades-old research ecosystem of India, as expressed through the formal advisor–advisee relationships in Indian universities.

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Notes

  1. https://www.genealogy.math.ndsu.nodak.edu/.

  2. https://academictree.org/.

  3. https://aishe.gov.in/aishe/gotoAisheReports.

  4. https://www.ugc.ac.in/pdfnews/8604044Higher-Education-Brochure.pdf.

  5. Dropout rate is not known, but we assumed it to be more than 2%.

  6. https://neo4j.com/.

  7. https://spacy.io.

  8. In Algorithm 2 the the threshold value is dynamic

  9. Structural variation refers to characters missing, longer-shorter version of names, etc.

  10. https://github.com/Djasingh/Shodhganga-AGN.

  11. https://gephi.org/.

  12. https://www.scopus.com/authid/detail.uri?authorId=7005045135.

  13. https://academictree.org/.

  14. The May 2018 AFT data dump is used.

  15. We have made the assumption that the Researchers have only one doctoral degree.

  16. The involvement of multiple advisors to advise a researcher in AGN.

  17. Note: We have not identified the subgraphs based on their frequency of occurrence in the random graphs and Shodhganga-AGN.

  18. https://neo4j.com/.

  19. Note: Temporal information is not considered for detecting subgraphs.

  20. The advisor–advisee connections available in Shodhganga-AGN following the predefined subgraph structures are referred as instances of the subgraph structures (shown in Fig. 13a).

  21. https://www.nirfindia.org/2021/Ranking.html.

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Authors and Affiliations

  1. Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, 721302, India

    Dhananjay Kumar & Plaban Kumar Bhowmick

  2. Mindtree, Kolkata, West Bengal, 700091, India

    Sumana Dey

  3. Indian Association for the Cultivation of Science, Kolkata, West Bengal, 700032, India

    Debarshi Kumar Sanyal

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  1. Dhananjay Kumar
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  2. Plaban Kumar Bhowmick
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  4. Debarshi Kumar Sanyal
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Correspondence to Dhananjay Kumar.

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Appendix

Appendix

Algorithms flowchart

Fig. 20
figure 20

Algorithm 2-In all the above cases, we have imposed the natural constraint that the advisor’s (who is potentially the same person as the advisee) date of thesis submission to be earlier than the advisee’s thesis submission date. In the case where the number of candidates (advisee) to merge with the advisor is greater than one, we further check them across the available attributes to find the best candidate

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Fig. 21
figure 21

Algorithm 3-Initially students with same names and having same department and institute are assigned the same index even if they are referring to different individuals. In that scenario, if advisor information (advisor thesis) is available, we are using that to find the most likely student among all the students (with the same name) associated with him/her. After that, group the remaining students by thesis title and assign them with different indices (Assumption: very less likely that two students with the same name and in the same institute and department has different thesis titles associated with same advisor)

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Fig. 22
figure 22

Algorithm 4-Applicable if multiple advisors advised the student (multiple records exist, same student name has variation due to human error (check Appendix Table 2)), or duplicate records exist, or wrongly combined thesis title. The function merges students with similar names (with slight variation) having the same thesis title. If student names are different across the same thesis index (similarity value less than threshold value), then there is a chance that two different thesis indices are merged wrongly. Therefore, we will not change the student indices

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Sample of researcher records with few attributes

Table 2 Sample of researcher records (Reference for complete dataset)
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Top researchers in Shodhganga-AGN based on different genealogical metrics

Table 3 Top 5 researchers who have trained maximum advisees (fecundity value)
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Table 4 Top 5 researchers with maximum fertile child’s
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Table 5 Top 5 researchers with maximum lineage size (number of descendants)
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Institute abbreviation, Nirf ranking, and DDC subject code with subject name

Table 6 Top 5 researchers with maximum cousins
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Table 7 Top 5 researchers with maximum lineage depth
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Table 8 Top 5 researchers with maximum advisors
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Table 9 Top 5 researchers based on \(h_m\)-index value
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Table 10 Top 5 researchers based on \(g_m\)-index value
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Table 11 DDC codes
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Table 12 Institute abbreviation and Nirf ranking (2021)
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Extracted subgraphs structures from Shodhganga-AGN

Fig. 23
figure 23

Extracted subgraphs instances from Shodhganga-AGN

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Kumar, D., Bhowmick, P.K., Dey, S. et al. On the banks of Shodhganga: analysis of the academic genealogy graph of an Indian ETD repository. Scientometrics 128, 3879–3914 (2023). https://doi.org/10.1007/s11192-023-04728-z

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