IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v114y2018i3d10.1007_s11192-017-2626-1.html
   My bibliography  Save this article

Collective topical PageRank: a model to evaluate the topic-dependent academic impact of scientific papers

Author

Listed:
  • Yongjun Zhang

    (Hohai University
    Huaiyin Institute of Technology)

  • Jialin Ma

    (Huaiyin Institute of Technology)

  • Zijian Wang

    (Hohai University)

  • Bolun Chen

    (Huaiyin Institute of Technology)

  • Yongtao Yu

    (Huaiyin Institute of Technology)

Abstract

With the explosive growth of academic writing, it is difficult for researchers to find significant papers in their area of interest. In this paper, we propose a pipeline model, named collective topical PageRank, to evaluate the topic-dependent impact of scientific papers. First, we fit the model to a correlation topic model based on the textual content of papers to extract scientific topics and correlations. Then, we present a modified PageRank algorithm, which incorporates the venue, the correlations of the scientific topics, and the publication year of each paper into a random walk to evaluate the paper’s topic-dependent academic impact. Our experiments showed that the model can effectively identify significant papers as well as venues for each scientific topic, recommend papers for further reading or citing, explore the evolution of scientific topics, and calculate the venues’ dynamic topic-dependent academic impact.

Suggested Citation

  • Yongjun Zhang & Jialin Ma & Zijian Wang & Bolun Chen & Yongtao Yu, 2018. "Collective topical PageRank: a model to evaluate the topic-dependent academic impact of scientific papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(3), pages 1345-1372, March.
    • Handle: RePEc:spr:scient:v:114:y:2018:i:3:d:10.1007_s11192-017-2626-1
    DOI: 10.1007/s11192-017-2626-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-017-2626-1
    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/s11192-017-2626-1?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. Erjia Yan, 2014. "Topic-based Pagerank: toward a topic-level scientific evaluation," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(2), pages 407-437, August.
    2. Marlie MacLean & Catherine Davies & Grant Lewison & Joe Anderson, 1998. "Evaluating the research activity and impact of funding agencies," Research Evaluation, Oxford University Press, vol. 7(1), pages 7-16, April.
    3. 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.
    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. Yuetong Chen & Hao Wang & Baolong Zhang & Wei Zhang, 2022. "A method of measuring the article discriminative capacity and its distribution," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(6), pages 3317-3341, June.
    2. Xie, Zheng & Lv, Yiqin & Song, Yiping & Wang, Qi, 2024. "Data labeling through the centralities of co-reference networks improves the classification accuracy of scientific papers," Journal of Informetrics, Elsevier, vol. 18(2).
    3. Manika Lamba & Margam Madhusudhan, 2019. "Mapping of topics in DESIDOC Journal of Library and Information Technology, India: a study," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(2), pages 477-505, August.
    4. Heng Huang & Donghua Zhu & Xuefeng Wang, 2022. "Evaluating scientific impact of publications: combining citation polarity and purpose," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5257-5281, September.

    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. Hayashi, Takayuki, 2003. "Effect of R&D programmes on the formation of university-industry-government networks: comparative analysis of Japanese R&D programmes," Research Policy, Elsevier, vol. 32(8), pages 1421-1442, September.
    2. Yi Bu & Binglu Wang & Win-bin Huang & Shangkun Che & Yong Huang, 2018. "Using the appearance of citations in full text on author co-citation analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(1), pages 275-289, July.
    3. Nicola Grassano & Daniele Rotolo & Joshua Hutton & Frédérique Lang & Michael M. Hopkins, 2017. "Funding Data from Publication Acknowledgments: Coverage, Uses, and Limitations," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(4), pages 999-1017, April.
    4. Yanan Wang & An Zeng & Ying Fan & Zengru Di, 2019. "Ranking scientific publications considering the aging characteristics of citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(1), pages 155-166, July.
    5. He, Bing & Ding, Ying & Tang, Jie & Reguramalingam, Vignesh & Bollen, Johan, 2013. "Mining diversity subgraph in multidisciplinary scientific collaboration networks: A meso perspective," Journal of Informetrics, Elsevier, vol. 7(1), pages 117-128.
    6. Beibei Hu & Xianlei Dong & Chenwei Zhang & Timothy D. Bowman & Ying Ding & Staša Milojević & Chaoqun Ni & Erjia Yan & Vincent Larivière, 2015. "A lead-lag analysis of the topic evolution patterns for preprints and publications," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(12), pages 2643-2656, December.
    7. Dzieżyc, Maciej & Kazienko, Przemysław, 2022. "Effectiveness of research grants funded by European Research Council and Polish National Science Centre," Journal of Informetrics, Elsevier, vol. 16(1).
    8. 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.
    9. Dietmar Wolfram, 2015. "The symbiotic relationship between information retrieval and informetrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(3), pages 2201-2214, March.
    10. Mengjiao Qi & An Zeng & Menghui Li & Ying Fan & Zengru Di, 2017. "Standing on the shoulders of giants: the effect of outstanding scientists on young collaborators’ careers," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1839-1850, June.
    11. Xinhai Lu & Yanwei Zhang & Chaoran Lin & Feng Wu, 2021. "Evolutionary Overview and Prediction of Themes in the Field of Land Degradation," Land, MDPI, vol. 10(3), pages 1-23, March.
    12. Nykl, Michal & Campr, Michal & Ježek, Karel, 2015. "Author ranking based on personalized PageRank," Journal of Informetrics, Elsevier, vol. 9(4), pages 777-799.
    13. Erjia Yan, 2014. "Topic-based Pagerank: toward a topic-level scientific evaluation," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(2), pages 407-437, August.
    14. 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.
    15. Kim, Min Sung & Kim, Junghwan & Kim, Seongcheol, 2023. "Korea's leadership in 5G and beyond: Footprints and futures," Telecommunications Policy, Elsevier, vol. 47(8).
    16. Melika Mosleh & Saeed Roshani & Mario Coccia, 2022. "Scientific laws of research funding to support citations and diffusion of knowledge in life science," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 1931-1951, April.
    17. 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.
    18. Yongjun Zhu & Erjia Yan, 2015. "Dynamic subfield analysis of disciplines: an examination of the trading impact and knowledge diffusion patterns of computer science," Scientometrics, Springer;Akadémiai Kiadó, vol. 104(1), pages 335-359, July.
    19. Chaoqun Ni & Cassidy R. Sugimoto & Blaise Cronin, 2013. "Visualizing and comparing four facets of scholarly communication: producers, artifacts, concepts, and gatekeepers," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(3), pages 1161-1173, March.
    20. Bo Wang & Shengbo Liu & Kun Ding & Zeyuan Liu & Jing Xu, 2014. "Identifying technological topics and institution-topic distribution probability for patent competitive intelligence analysis: a case study in LTE technology," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(1), pages 685-704, October.

    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:114:y:2018:i:3:d:10.1007_s11192-017-2626-1. 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.