IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v97y2013i3d10.1007_s11192-013-0988-6.html
   My bibliography  Save this article

Finding topic-level experts in scholarly networks

Author

Listed:
  • Lili Lin

    (Hohai University)

  • Zhuoming Xu

    (Hohai University)

  • Ying Ding

    (Indiana University)

  • Xiaozhong Liu

    (Indiana University)

Abstract

Expert finding is of vital importance for exploring scientific collaborations to increase productivity by sharing and transferring knowledge within and across different research areas. Expert finding methods, including content-based methods, link structure-based methods, and a combination of content-based and link structure-based methods, have been studied in recent years. However, most state-of-the-art expert finding approaches have usually studied candidates’ personal information (e.g. topic relevance and citation counts) and network information (e.g. citation relationship) separately, causing some potential experts to be ignored. In this paper, we propose a topical and weighted factor graph model that simultaneously combines all the possible information in a unified way. In addition, we also design the Loopy Max-Product algorithm and related message-passing schedules to perform approximate inference on our cycle-containing factor graph model. Information Retrieval is chosen as the test field to identify representative authors for different topics within this area. Finally, we compare our approach with three baseline methods in terms of topic sensitivity, coverage rate of SIGIR PC (e.g. Program Committees or Program Chairs) members, and Normalized Discounted Cumulated Gain scores for different rankings on each topic. The experimental results demonstrate that our factor graph-based model can definitely enhance the expert-finding performance.

Suggested Citation

  • Lili Lin & Zhuoming Xu & Ying Ding & Xiaozhong Liu, 2013. "Finding topic-level experts in scholarly networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(3), pages 797-819, December.
  • Handle: RePEc:spr:scient:v:97:y:2013:i:3:d:10.1007_s11192-013-0988-6
    DOI: 10.1007/s11192-013-0988-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-013-0988-6
    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/s11192-013-0988-6?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. Dalibor Fiala & François Rousselot & Karel Ježek, 2008. "PageRank for bibliographic networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 76(1), pages 135-158, July.
    2. Ying Ding, 2011. "Topic-based PageRank on author cocitation networks," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(3), pages 449-466, March.
    3. 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.
    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. Nykl, Michal & Ježek, Karel & Fiala, Dalibor & Dostal, Martin, 2014. "PageRank variants in the evaluation of citation networks," Journal of Informetrics, Elsevier, vol. 8(3), pages 683-692.

    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. Tehmina Amjad & Ying Ding & Ali Daud & Jian Xu & Vincent Malic, 2015. "Topic-based heterogeneous rank," Scientometrics, Springer;Akadémiai Kiadó, vol. 104(1), pages 313-334, July.
    2. Nykl, Michal & Campr, Michal & Ježek, Karel, 2015. "Author ranking based on personalized PageRank," Journal of Informetrics, Elsevier, vol. 9(4), pages 777-799.
    3. Fiala, Dalibor, 2012. "Time-aware PageRank for bibliographic networks," Journal of Informetrics, Elsevier, vol. 6(3), pages 370-388.
    4. Yuanyuan Liu & Qiang Wu & Shijie Wu & Yong Gao, 2021. "Weighted citation based on ranking-related contribution: a new index for evaluating article impact," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(10), pages 8653-8672, October.
    5. Eleni Fragkiadaki & Georgios Evangelidis, 2016. "Three novel indirect indicators for the assessment of papers and authors based on generations of citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(2), pages 657-694, February.
    6. Tehmina Amjad & Yusra Rehmat & Ali Daud & Rabeeh Ayaz Abbasi, 2020. "Scientific impact of an author and role of self-citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(2), pages 915-932, February.
    7. Dinesh Pradhan & Partha Sarathi Paul & Umesh Maheswari & Subrata Nandi & Tanmoy Chakraborty, 2017. "$$C^3$$ C 3 -index: a PageRank based multi-faceted metric for authors’ performance measurement," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(1), pages 253-273, January.
    8. Jianlin Zhou & An Zeng & Ying Fan & Zengru Di, 2016. "Ranking scientific publications with similarity-preferential mechanism," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(2), pages 805-816, February.
    9. Niu, Qikai & Zhou, Jianlin & Zeng, An & Fan, Ying & Di, Zengru, 2016. "Which publication is your representative work?," Journal of Informetrics, Elsevier, vol. 10(3), pages 842-853.
    10. Erjia Yan, 2014. "Topic-based Pagerank: toward a topic-level scientific evaluation," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(2), pages 407-437, August.
    11. Yu Zhang & Min Wang & Morteza Saberi & Elizabeth Chang, 2020. "Knowledge fusion through academic articles: a survey of definitions, techniques, applications and challenges," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2637-2666, December.
    12. Dunaiski, Marcel & Geldenhuys, Jaco & Visser, Willem, 2019. "Globalised vs averaged: Bias and ranking performance on the author level," Journal of Informetrics, Elsevier, vol. 13(1), pages 299-313.
    13. Dunaiski, Marcel & Geldenhuys, Jaco & Visser, Willem, 2018. "Author ranking evaluation at scale," Journal of Informetrics, Elsevier, vol. 12(3), pages 679-702.
    14. Dunaiski, Marcel & Visser, Willem & Geldenhuys, Jaco, 2016. "Evaluating paper and author ranking algorithms using impact and contribution awards," Journal of Informetrics, Elsevier, vol. 10(2), pages 392-407.
    15. Fiala, Dalibor & Šubelj, Lovro & Žitnik, Slavko & Bajec, Marko, 2015. "Do PageRank-based author rankings outperform simple citation counts?," Journal of Informetrics, Elsevier, vol. 9(2), pages 334-348.
    16. Su, Cheng & Pan, YunTao & Zhen, YanNing & Ma, Zheng & Yuan, JunPeng & Guo, Hong & Yu, ZhengLu & Ma, CaiFeng & Wu, YiShan, 2011. "PrestigeRank: A new evaluation method for papers and journals," Journal of Informetrics, Elsevier, vol. 5(1), pages 1-13.
    17. Jiang Wu, 2013. "Geographical knowledge diffusion and spatial diversity citation rank," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(1), pages 181-201, January.
    18. Dunaiski, Marcel & Geldenhuys, Jaco & Visser, Willem, 2019. "On the interplay between normalisation, bias, and performance of paper impact metrics," Journal of Informetrics, Elsevier, vol. 13(1), pages 270-290.
    19. Young-Ho Eom & Dima L Shepelyansky, 2013. "Highlighting Entanglement of Cultures via Ranking of Multilingual Wikipedia Articles," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-10, October.
    20. Dejian Yu & Wanru Wang & Shuai Zhang & Wenyu Zhang & Rongyu Liu, 2017. "A multiple-link, mutually reinforced journal-ranking model to measure the prestige of journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 521-542, April.

    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:97:y:2013:i:3:d:10.1007_s11192-013-0988-6. 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.