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Finding rising stars in bibliometric networks

Author

Listed:
  • Ali Daud

    (University of Jeddah)

  • Min Song

    (Yonsei University)

  • Malik Khizar Hayat

    (IIU)

  • Tehmina Amjad

    (IIU)

  • Rabeeh Ayaz Abbasi

    (QAU)

  • Hassan Dawood

    (University of Engineering and Technology)

  • Anwar Ghani

    (IIU)

Abstract

Finding rising stars (FRS) is a hot research topic investigated recently for diverse application domains. These days, people are more interested in finding people who will become experts shortly to fill junior positions than finding existing experts who can immediately fill senior positions. FRS can increase productivity wherever they join due to their vibrant and energetic behavior. In this paper, we assess the methods to find FRS. The existing methods are classified into ranking-, prediction-, clustering-, and analysis-based methods, and the pros and cons of these methods are discussed. Details of standard datasets and performance-evaluation measures are also provided for this growing area of research. We conclude by discussing open challenges and future directions in this prosperous area of research.

Suggested Citation

  • Ali Daud & Min Song & Malik Khizar Hayat & Tehmina Amjad & Rabeeh Ayaz Abbasi & Hassan Dawood & Anwar Ghani, 2020. "Finding rising stars in bibliometric networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(1), pages 633-661, July.
    • Handle: RePEc:spr:scient:v:124:y:2020:i:1:d:10.1007_s11192-020-03466-w
    DOI: 10.1007/s11192-020-03466-w
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    References listed on IDEAS

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    1. Long T. Le & Chirag Shah, 2018. "Retrieving people: Identifying potential answerers in Community Question‐Answering," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 69(10), pages 1246-1258, October.
    2. Guo Zhang & Ying Ding & Staša Milojević, 2013. "Citation content analysis (CCA): A framework for syntactic and semantic analysis of citation content," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(7), pages 1490-1503, July.
    3. Raf Guns & Ronald Rousseau, 2014. "Recommending research collaborations using link prediction and random forest classifiers," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1461-1473, November.
    4. Hao Wu & Bo Li & Yijian Pei & Jun He, 2014. "Unsupervised author disambiguation using Dempster–Shafer theory," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(3), pages 1955-1972, December.
    5. ., 2017. "Standing on the shoulders of giants," Chapters, in: Endogenous Innovation, chapter 1, pages 3-24, Edward Elgar Publishing.
    6. Amjad, Tehmina & Ding, Ying & Xu, Jian & Zhang, Chenwei & Daud, Ali & Tang, Jie & Song, Min, 2017. "Standing on the shoulders of giants," Journal of Informetrics, Elsevier, vol. 11(1), pages 307-323.
    7. Panagopoulos, George & Tsatsaronis, George & Varlamis, Iraklis, 2017. "Detecting rising stars in dynamic collaborative networks," Journal of Informetrics, Elsevier, vol. 11(1), pages 198-222.
    8. 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.
    9. Guo Zhang & Ying Ding & Staša Milojević, 2013. "Citation content analysis (CCA): A framework for syntactic and semantic analysis of citation content," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 64(7), pages 1490-1503, July.
    10. Lin Zhu & Donghua Zhu & Xuefeng Wang & Scott W. Cunningham & Zhinan Wang, 2019. "An integrated solution for detecting rising technology stars in co-inventor networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(1), pages 137-172, October.
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    Cited by:

    1. Tayyaba Kanwal & Tehmina Amjad, 2024. "Research paper recommendation system based on multiple features from citation network," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(9), pages 5493-5531, September.
    2. Matthias Kuppler, 2022. "Predicting the future impact of Computer Science researchers: Is there a gender bias?," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6695-6732, November.
    3. Tehmina Amjad & Javeria Munir, 2021. "Investigating the impact of collaboration with authority authors: a case study of bibliographic data in field of philosophy," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 4333-4353, May.

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