IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v124y2020i1d10.1007_s11192-020-03466-w.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-020-03466-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11192-020-03466-w?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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. Panagopoulos, George & Tsatsaronis, George & Varlamis, Iraklis, 2017. "Detecting rising stars in dynamic collaborative networks," Journal of Informetrics, Elsevier, vol. 11(1), pages 198-222.
    6. 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.
    7. 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.
    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. ., 2017. "Standing on the shoulders of giants," Chapters, in: Endogenous Innovation, chapter 1, pages 3-24, Edward Elgar Publishing.
    10. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    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. Chung, Jaemin & Ko, Namuk & Kim, Hyeonsu & Yoon, Janghyeok, 2021. "Inventor profile mining approach for prospective human resource scouting," Journal of Informetrics, Elsevier, vol. 15(1).
    2. Aftab Nawaz & MSI Malik, 2022. "Rising stars prediction in reviewer network," Electronic Commerce Research, Springer, vol. 22(1), pages 53-75, March.
    3. Lin Zhu & Junjie Zhang & Scott W. Cunningham, 2022. "Domain expertise extraction for finding rising stars," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5475-5495, September.
    4. 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.
    5. 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.
    6. Lutz Bornmann & Robin Haunschild & Sven E. Hug, 2018. "Visualizing the context of citations referencing papers published by Eugene Garfield: a new type of keyword co-occurrence analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(2), pages 427-437, February.
    7. Liu, Xiaojuan & Wang, Chenlin & Chen, Dar-Zen & Huang, Mu-Hsuan, 2022. "Exploring perception of retraction based on mentioned status in post-retraction citations," Journal of Informetrics, Elsevier, vol. 16(3).
    8. Kim, Ha Jin & Jeong, Yoo Kyung & Song, Min, 2016. "Content- and proximity-based author co-citation analysis using citation sentences," Journal of Informetrics, Elsevier, vol. 10(4), pages 954-966.
    9. Luca Cagliero & Paolo Garza & Mohammad Reza Kavoosifar & Elena Baralis, 2018. "Discovering cross-topic collaborations among researchers by exploiting weighted association rules," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 1273-1301, August.
    10. Lin, Yiling & Evans, James A. & Wu, Lingfei, 2022. "New directions in science emerge from disconnection and discord," Journal of Informetrics, Elsevier, vol. 16(1).
    11. Li, Kai & Chen, Pei-Ying & Yan, Erjia, 2019. "Challenges of measuring software impact through citations: An examination of the lme4 R package," Journal of Informetrics, Elsevier, vol. 13(1), pages 449-461.
    12. 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.
    13. Shen, Hongquan & Cheng, Ying & Ju, Xiufang & Xie, Juan, 2022. "Rethinking the effect of inter-gender collaboration on research performance for scholars," Journal of Informetrics, Elsevier, vol. 16(4).
    14. Chao Lu & Ying Ding & Chengzhi Zhang, 2017. "Understanding the impact change of a highly cited article: a content-based citation analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(2), pages 927-945, August.
    15. Jun Zhang & Yan Hu & Zhaolong Ning & Amr Tolba & Elsayed Elashkar & Feng Xia, 2018. "AIRank: Author Impact Ranking through Positions in Collaboration Networks," Complexity, Hindawi, vol. 2018, pages 1-16, June.
    16. Zhang, Chengzhi & Liu, Lifan & Wang, Yuzhuo, 2021. "Characterizing references from different disciplines: A perspective of citation content analysis," Journal of Informetrics, Elsevier, vol. 15(2).
    17. Samreen Ayaz & Nayyer Masood & Muhammad Arshad Islam, 2018. "Predicting scientific impact based on h-index," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(3), pages 993-1010, March.
    18. Lyu, Haihua & Bu, Yi & Zhao, Zhenyue & Zhang, Jiarong & Li, Jiang, 2022. "Citation bias in measuring knowledge flow: Evidence from the web of science at the discipline level," Journal of Informetrics, Elsevier, vol. 16(4).
    19. Adilson Vital & Diego R. Amancio, 2022. "A comparative analysis of local similarity metrics and machine learning approaches: application to link prediction in author citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(10), pages 6011-6028, October.
    20. Shiyun Wang & Yaxue Ma & Jin Mao & Yun Bai & Zhentao Liang & Gang Li, 2023. "Quantifying scientific breakthroughs by a novel disruption indicator based on knowledge entities," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(2), pages 150-167, February.

    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:spr:scient:v:124:y:2020:i:1:d:10.1007_s11192-020-03466-w. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.