IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v111y2017i1d10.1007_s11192-017-2262-9.html
   My bibliography  Save this article

A multiple-link, mutually reinforced journal-ranking model to measure the prestige of journals

Author

Listed:
  • Dejian Yu

    (Zhejiang University of Finance and Economics)

  • Wanru Wang

    (Zhejiang University of Finance and Economics)

  • Shuai Zhang

    (Zhejiang University of Finance and Economics)

  • Wenyu Zhang

    (Zhejiang University of Finance and Economics)

  • Rongyu Liu

    (Zhejiang University of Finance and Economics)

Abstract

Important journals usually guide the research and development directions in academic circles. Therefore, it is necessary to find the important journals among a number of academic journals. This study presents a model named the multiple-link, mutually reinforced journal-ranking (MLMRJR) model based on the PageRank and the Hyperlink-Induced Topics Search algorithms that considers not only the quantity and quality of citations in intra-networks, but also the mutual reinforcement in inter-networks. First, the multiple links between four intra-networks and three inter-networks of paper, author, and journal are involved simultaneously. Second, a time factor is added to the paper citation network as the weight of the edges to solve the rank bias problem of the PageRank algorithm. Third, the author citation network and the co-authorship network are considered simultaneously. The results of a case study showed that the proposed MLMRJR model can obtain a reasonable journal ranking based on Spearman’s and Kendall’s ranking correlation coefficient and ROC curve analysis. This study provides a systematic view of such field from the perspective of measuring the prestige of journals, which can help researchers decide where to view publications and publish their papers, and help journal editors and organizations evaluate the quality of other journals and focus on the strengths of their own journals.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:scient:v:111:y:2017:i:1:d:10.1007_s11192-017-2262-9
    DOI: 10.1007/s11192-017-2262-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-017-2262-9
    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/s11192-017-2262-9?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. Ying Ding, 2011. "Applying weighted PageRank to author citation networks," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(2), pages 236-245, February.
    2. Xu, Han & Martin, Eric & Mahidadia, Ashesh, 2014. "Contents and time sensitive document ranking of scientific literature," Journal of Informetrics, Elsevier, vol. 8(3), pages 546-561.
    3. Dengsheng Wu & Minglu Li & Xiaoqian Zhu & Hongfang Song & Jianping Li, 2015. "Ranking the research productivity of business and management institutions in Asia–Pacific region: empirical research in leading ABS journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(2), pages 1253-1272, November.
    4. Chia-Lin Chang & Esfandiar Maasoumi & Michael McAleer, 2016. "Robust Ranking of Journal Quality: An Application to Economics," Econometric Reviews, Taylor & Francis Journals, vol. 35(1), pages 50-97, January.
    5. 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.
    6. Guerrero-Bote, Vicente P. & Moya-Anegón, Félix, 2012. "A further step forward in measuring journals’ scientific prestige: The SJR2 indicator," Journal of Informetrics, Elsevier, vol. 6(4), pages 674-688.
    7. Fiala, Dalibor, 2012. "Time-aware PageRank for bibliographic networks," Journal of Informetrics, Elsevier, vol. 6(3), pages 370-388.
    8. Nykl, Michal & Campr, Michal & Ježek, Karel, 2015. "Author ranking based on personalized PageRank," Journal of Informetrics, Elsevier, vol. 9(4), pages 777-799.
    9. 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.
    10. Brenda Cheang & Samuel Kai Wah Chu & Chongshou Li & Andrew Lim, 2014. "A multidimensional approach to evaluating management journals: Refining pagerank via the differentiation of citation types and identifying the roles that management journals play," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(12), pages 2581-2591, December.
    11. Gianfranco Ennas & Battista Biggio & Maria Chiara Di Guardo, 2015. "Data-driven journal meta-ranking in business and management," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 1911-1929, December.
    12. Ying Ding, 2011. "Applying weighted PageRank to author citation networks," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(2), pages 236-245, February.
    13. Erjia Yan & Ying Ding, 2010. "Weighted citation: An indicator of an article's prestige," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(8), pages 1635-1643, August.
    14. Waltman, Ludo & van Eck, Nees Jan & van Leeuwen, Thed N. & Visser, Martijn S., 2013. "Some modifications to the SNIP journal impact indicator," Journal of Informetrics, Elsevier, vol. 7(2), pages 272-285.
    15. Pajić, Dejan, 2015. "On the stability of citation-based journal rankings," Journal of Informetrics, Elsevier, vol. 9(4), pages 990-1006.
    16. 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.
    17. 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.
    18. Erjia Yan & Ying Ding & Cassidy R. Sugimoto, 2011. "P-Rank: An indicator measuring prestige in heterogeneous scholarly networks," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(3), pages 467-477, March.
    19. Rousseau, Ronald & Xu, Fang & Liu, Wenbin, 2015. "Interpolated sub-impact factor (SIF) sequences for journal rankings," Journal of Informetrics, Elsevier, vol. 9(4), pages 907-914.
    20. Moed, Henk F., 2010. "Measuring contextual citation impact of scientific journals," Journal of Informetrics, Elsevier, vol. 4(3), pages 265-277.
    21. González-Pereira, Borja & Guerrero-Bote, Vicente P. & Moya-Anegón, Félix, 2010. "A new approach to the metric of journals’ scientific prestige: The SJR indicator," Journal of Informetrics, Elsevier, vol. 4(3), pages 379-391.
    22. Fang Xu & Wenbin Liu & Ronald Rousseau, 2015. "Introducing sub-impact factor (SIF-) sequences and an aggregated SIF-indicator for journal ranking," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(2), pages 1577-1593, February.
    23. Xiaorui Jiang & Xiaoping Sun & Zhe Yang & Hai Zhuge & Jianmin Yao, 2016. "Exploiting heterogeneous scientific literature networks to combat ranking bias: Evidence from the computational linguistics area," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(7), pages 1679-1702, July.
    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. Zhang, Fang & Wu, Shengli, 2020. "Predicting future influence of papers, researchers, and venues in a dynamic academic network," Journal of Informetrics, Elsevier, vol. 14(2).
    2. 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.
    3. Shunshun Shi & Wenyu Zhang & Shuai Zhang & Jie Chen, 2018. "Does prestige dimension influence the interdisciplinary performance of scientific entities in knowledge flow? Evidence from the e-government field," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(2), pages 1237-1264, November.
    4. Dejian Yu & Wanru Wang & Shuai Zhang & Wenyu Zhang & Rongyu Liu, 2017. "Hybrid self-optimized clustering model based on citation links and textual features to detect research topics," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-21, October.
    5. Lin Feng & Jian Zhou & Sheng-Lan Liu & Ning Cai & Jie Yang, 2020. "Analysis of journal evaluation indicators: an experimental study based on unsupervised Laplacian score," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(1), pages 233-254, July.
    6. Antonia Ferrer-Sapena & Susana Díaz-Novillo & Enrique A. Sánchez-Pérez, 2017. "Measuring Time-Dynamics and Time-Stability of Journal Rankings in Mathematics and Physics by Means of Fractional p -Variations," Publications, MDPI, vol. 5(3), pages 1-14, September.
    7. Yu, Dejian & Sheng, Libo, 2021. "Influence difference main path analysis: Evidence from DNA and blockchain domain citation networks," Journal of Informetrics, Elsevier, vol. 15(4).
    8. Yu, Dejian & Pan, Tianxing, 2021. "Tracing the main path of interdisciplinary research considering citation preference: A case from blockchain domain," Journal of Informetrics, Elsevier, vol. 15(2).
    9. Wang, Ruby W. & Wei, Shelia X. & Ye, Fred Y., 2021. "Extracting a core structure from heterogeneous information network using h-subnet and meta-path strength," Journal of Informetrics, Elsevier, vol. 15(3).
    10. 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.
    11. Elías Sanz-Casado & Daniela Filippo & Rafael Aleixandre Benavent & Vidar Røeggen & Janne Pölönen, 2021. "Impact and visibility of Norwegian, Finnish and Spanish journals in the fields of humanities," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(11), pages 9031-9049, November.
    12. Polterovich, Victor, 2023. "Авторский Капитал И Реформирование Российской Публикационной Системы [Authorship Capital and reforming The Russian Publication System]," MPRA Paper 117309, University Library of Munich, Germany.
    13. Dejian Yu & Libo Sheng & Shunshun Shi, 2023. "A retrospective analysis of Journal of Forecasting: From 1982 to 2019," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 1008-1035, July.
    14. Xiaolong Xue & Liang Wang & Rebecca J. Yang, 2018. "Exploring the science of resilience: critical review and bibliometric analysis," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 90(1), pages 477-510, January.

    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. Zhang, Fang & Wu, Shengli, 2020. "Predicting future influence of papers, researchers, and venues in a dynamic academic network," Journal of Informetrics, Elsevier, vol. 14(2).
    2. Fang Zhang & Shengli Wu, 2021. "Measuring academic entities’ impact by content-based citation analysis in a heterogeneous academic network," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 7197-7222, August.
    3. 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.
    4. 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.
    5. 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.
    6. Nykl, Michal & Campr, Michal & Ježek, Karel, 2015. "Author ranking based on personalized PageRank," Journal of Informetrics, Elsevier, vol. 9(4), pages 777-799.
    7. Liwei Cai & Jiahao Tian & Jiaying Liu & Xiaomei Bai & Ivan Lee & Xiangjie Kong & Feng Xia, 2019. "Scholarly impact assessment: a survey of citation weighting solutions," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(2), pages 453-478, February.
    8. Walters, William H., 2017. "Do subjective journal ratings represent whole journals or typical articles? Unweighted or weighted citation impact?," Journal of Informetrics, Elsevier, vol. 11(3), pages 730-744.
    9. Yu Zhang & Min Wang & Morteza Saberi & Elizabeth Chang, 2022. "Analysing academic paper ranking algorithms using test data and benchmarks: an investigation," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(7), pages 4045-4074, July.
    10. 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.
    11. Fiala, Dalibor, 2012. "Time-aware PageRank for bibliographic networks," Journal of Informetrics, Elsevier, vol. 6(3), pages 370-388.
    12. Mingers, John & Yang, Liying, 2017. "Evaluating journal quality: A review of journal citation indicators and ranking in business and management," European Journal of Operational Research, Elsevier, vol. 257(1), pages 323-337.
    13. 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.
    14. 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.
    15. Mariani, Manuel Sebastian & Medo, Matúš & Zhang, Yi-Cheng, 2016. "Identification of milestone papers through time-balanced network centrality," Journal of Informetrics, Elsevier, vol. 10(4), pages 1207-1223.
    16. 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).
    17. Ludo Waltman & Erjia Yan & Nees Jan Eck, 2011. "A recursive field-normalized bibliometric performance indicator: an application to the field of library and information science," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(1), pages 301-314, October.
    18. Cristina López-Duarte & Marta M. Vidal-Suárez & Belén González-Díaz & Nuno Rosa Reis, 2016. "Understanding the relevance of national culture in international business research: a quantitative analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(3), pages 1553-1590, September.
    19. Mingers, John & Leydesdorff, Loet, 2015. "A review of theory and practice in scientometrics," European Journal of Operational Research, Elsevier, vol. 246(1), pages 1-19.
    20. 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.

    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:111:y:2017:i:1:d:10.1007_s11192-017-2262-9. 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.