IDEAS home Printed from https://ideas.repec.org/a/eee/infome/v17y2023i2s1751157723000172.html
   My bibliography  Save this article

Researcher influence prediction (ResIP) using academic genealogy network

Author

Listed:
  • Kumar, Dhananjay
  • Bhowmick, Plaban Kumar
  • Paik, Jiaul H

Abstract

In academia researchers join a research community over time and contribute to the advancement of a field in a variety of ways. One of the most established ways to contribute to the field is by passing on knowledge to the future generations through academic advising. Many academic scholars have more influence, while others fail to make an impact. Typically, academic influence refers to the ability of a researcher to pass on her/his “academic gene” in future researchers. In this article, we propose the task of Researcher Influence Prediction (ResIP) to predict researchers’ future influence in an academic field through the analysis of the corresponding academic genealogy network. Researcher influence prediction has got several implications as far as different academic outcomes are concerned (e.g. funding, awards, career progression, collaboration, identifying prolific researchers etc.).

Suggested Citation

  • Kumar, Dhananjay & Bhowmick, Plaban Kumar & Paik, Jiaul H, 2023. "Researcher influence prediction (ResIP) using academic genealogy network," Journal of Informetrics, Elsevier, vol. 17(2).
  • Handle: RePEc:eee:infome:v:17:y:2023:i:2:s1751157723000172
    DOI: 10.1016/j.joi.2023.101392
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1751157723000172
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.joi.2023.101392?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zhao, Qihang & Feng, Xiaodong, 2022. "Utilizing citation network structure to predict paper citation counts: A Deep learning approach," Journal of Informetrics, Elsevier, vol. 16(1).
    2. Ruan, Xuanmin & Zhu, Yuanyang & Li, Jiang & Cheng, Ying, 2020. "Predicting the citation counts of individual papers via a BP neural network," Journal of Informetrics, Elsevier, vol. 14(3).
    3. 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.
    4. Chen, P. & Xie, H. & Maslov, S. & Redner, S., 2007. "Finding scientific gems with Google’s PageRank algorithm," Journal of Informetrics, Elsevier, vol. 1(1), pages 8-15.
    5. J. Scott Armstrong, 1980. "Unintelligible Management Research and Academic Prestige," Interfaces, INFORMS, vol. 10(2), pages 80-86, April.
    6. Jorge A. V. Tohalino & Laura V. C. Quispe & Diego R. Amancio, 2021. "Analyzing the relationship between text features and grants productivity," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 4255-4275, May.
    7. Ali Daud & Muhammad Ahmad & M. S. I. Malik & Dunren Che, 2015. "Using machine learning techniques for rising star prediction in co-author network," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(2), pages 1687-1711, February.
    8. Amancio, Diego Raphael & Oliveira, Osvaldo Novais & da Fontoura Costa, Luciano, 2012. "Three-feature model to reproduce the topology of citation networks and the effects from authors’ visibility on their h-index," Journal of Informetrics, Elsevier, vol. 6(3), pages 427-434.
    9. 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.
    10. Tian Yu & Guang Yu & Peng-Yu Li & Liang Wang, 2014. "Citation impact prediction for scientific papers using stepwise regression analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1233-1252, November.
    11. Stephen V David & Benjamin Y Hayden, 2012. "Neurotree: A Collaborative, Graphical Database of the Academic Genealogy of Neuroscience," PLOS ONE, Public Library of Science, vol. 7(10), pages 1-12, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhao, Qihang & Feng, Xiaodong, 2022. "Utilizing citation network structure to predict paper citation counts: A Deep learning approach," Journal of Informetrics, Elsevier, vol. 16(1).
    2. Wanjun Xia & Tianrui Li & Chongshou Li, 2023. "A review of scientific impact prediction: tasks, features and methods," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 543-585, January.
    3. Jorge A. V. Tohalino & Laura V. C. Quispe & Diego R. Amancio, 2021. "Analyzing the relationship between text features and grants productivity," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 4255-4275, May.
    4. 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.
    5. Chen, Ying & Koch, Thorsten & Zakiyeva, Nazgul & Liu, Kailiang & Xu, Zhitong & Chen, Chun-houh & Nakano, Junji & Honda, Keisuke, 2023. "Article’s scientific prestige: Measuring the impact of individual articles in the web of science," Journal of Informetrics, Elsevier, vol. 17(1).
    6. Yubing Nie & Yifan Zhu & Qika Lin & Sifan Zhang & Pengfei Shi & Zhendong Niu, 2019. "Academic rising star prediction via scholar’s evaluation model and machine learning techniques," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(2), pages 461-476, August.
    7. Martorell Cunil, Onofre & Otero González, Luis & Durán Santomil, Pablo & Mulet Forteza, Carlos, 2023. "How to accomplish a highly cited paper in the tourism, leisure and hospitality field," Journal of Business Research, Elsevier, vol. 157(C).
    8. Shengzhi Huang & Jiajia Qian & Yong Huang & Wei Lu & Yi Bu & Jinqing Yang & Qikai Cheng, 2022. "Disclosing the relationship between citation structure and future impact of a publication," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(7), pages 1025-1042, July.
    9. 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.
    10. Fenghua Wang & Ying Fan & An Zeng & Zengru Di, 2019. "Can we predict ESI highly cited publications?," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(1), pages 109-125, January.
    11. 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.
    12. Xiaomei Bai & Fuli Zhang & Jinzhou Li & Zhong Xu & Zeeshan Patoli & Ivan Lee, 2021. "Quantifying scientific collaboration impact by exploiting collaboration-citation network," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(9), pages 7993-8008, September.
    13. 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.
    14. Wumei Du & Zheng Xie & Yiqin Lv, 2021. "Predicting publication productivity for authors: Shallow or deep architecture?," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5855-5879, July.
    15. Yuhao Zhou & Ruijie Wang & An Zeng, 2022. "Predicting the impact and publication date of individual scientists’ future papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 1867-1882, April.
    16. Tehmina Amjad & Nafeesa Shahid & Ali Daud & Asma Khatoon, 2022. "Citation burst prediction in a bibliometric network," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(5), pages 2773-2790, May.
    17. Peter Klimek & Aleksandar Jovanovic & Rainer Egloff & Reto Schneider, 2016. "Successful fish go with the flow: citation impact prediction based on centrality measures for term–document networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(3), pages 1265-1282, June.
    18. 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.
    19. Rossi, Luciano & Damaceno, Rafael J.P. & Freire, Igor L. & Bechara, Etelvino J.H. & Mena-Chalco, Jesús P., 2018. "Topological metrics in academic genealogy graphs," Journal of Informetrics, Elsevier, vol. 12(4), pages 1042-1058.
    20. Akella, Akhil Pandey & Alhoori, Hamed & Kondamudi, Pavan Ravikanth & Freeman, Cole & Zhou, Haiming, 2021. "Early indicators of scientific impact: Predicting citations with altmetrics," Journal of Informetrics, Elsevier, vol. 15(2).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:infome:v:17:y:2023:i:2:s1751157723000172. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/joi .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.