IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v107y2016i3d10.1007_s11192-016-1926-1.html
   My bibliography  Save this article

Successful fish go with the flow: citation impact prediction based on centrality measures for term–document networks

Author

Listed:
  • Peter Klimek

    (Medical University of Vienna
    Steinbeis Advanced Risk Technologies)

  • Aleksandar Jovanovic

    (Steinbeis Advanced Risk Technologies
    EU-VRi)

  • Rainer Egloff

    (Swiss Reinsurance Company Ltd)

  • Reto Schneider

    (Swiss Reinsurance Company Ltd)

Abstract

In this work we address the challenge of how to identify those documents from a given set of texts that are most likely to have substantial impact in the future. To this end we develop a purely content-based methodology in order to rank a given set of documents, for example abstracts of scientific publications, according to their potential to generate impact as measured by the numbers of citations that the articles will receive in the future. We construct a bipartite network consisting of documents that are linked to keywords and terms that they contain. We study recursive centrality measures for such networks that quantify how many different terms a document contains and how these terms are related to each other. From this we derive a novel indicator—document centrality—that is shown to be highly predictive of citation impact in six different case studies. We compare these results to findings from a multivariable regression model and from conventional network-based centrality measures to show that document centrality indeed offers a comparably high performance in identifying those articles that contain a large number of high-impact keywords. Our findings suggest that articles which conform to the mainstream within a given research field tend to receive higher numbers of citations than highly original and innovative articles.

Suggested Citation

  • Peter Klimek & Aleksandar Jovanovic & Rainer Egloff & Reto Schneider, 2016. "Successful fish go with the flow: citation impact prediction based on centrality measures for term–document networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(3), pages 1265-1282, June.
    • Handle: RePEc:spr:scient:v:107:y:2016:i:3:d:10.1007_s11192-016-1926-1
    DOI: 10.1007/s11192-016-1926-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-016-1926-1
    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/s11192-016-1926-1?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. Liu, Jian-Guo & Xuan, Zhao-Guo & Dang, Yan-Zhong & Guo, Qiang & Wang, Zhong-Tuo, 2007. "Weighted network properties of Chinese nature science basic research," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 377(1), pages 302-314.
    2. 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.
    3. Cesar A. Hidalgo & Ricardo Hausmann, 2009. "The Building Blocks of Economic Complexity," Papers 0909.3890, arXiv.org.
    4. A.S. Jovanović & O. Renn, 2013. "Search for the 'European way' of taming the risks of new technologies: the EU research project iNTeg-Risk," Journal of Risk Research, Taylor & Francis Journals, vol. 16(3-4), pages 271-274, April.
    5. Mohammadreza Moohebat & Ram Gopal Raj & Sameem Binti Abdul Kareem & Dirk Thorleuchter, 2015. "Identifying ISI-indexed articles by their lexical usage: A text analysis approach," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(3), pages 501-511, March.
    6. Radicchi, Filippo & Castellano, Claudio, 2012. "Testing the fairness of citation indicators for comparison across scientific domains: The case of fractional citation counts," Journal of Informetrics, Elsevier, vol. 6(1), pages 121-130.
    7. Rickard Danell, 2011. "Can the quality of scientific work be predicted using information on the author's track record?," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(1), pages 50-60, January.
    8. Loet Leydesdorff & Lutz Bornmann, 2011. "Integrated impact indicators compared with impact factors: An alternative research design with policy implications," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(11), pages 2133-2146, November.
    9. Fereshteh Didegah & Mike Thelwall, 2013. "Determinants of research citation impact in nanoscience and nanotechnology," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(5), pages 1055-1064, May.
    10. Loet Leydesdorff, 2009. "How are new citation‐based journal indicators adding to the bibliometric toolbox?," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(7), pages 1327-1336, July.
    11. Tian Yu & Guang Yu & Peng-Yu Li & Liang Wang, 2014. "Citation impact prediction for scientific papers using stepwise regression analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1233-1252, November.
    12. Vieira, E.S. & Gomes, J.A.N.F., 2010. "Citations to scientific articles: Its distribution and dependence on the article features," Journal of Informetrics, Elsevier, vol. 4(1), pages 1-13.
    13. 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.
    14. Bornmann, Lutz & Schier, Hermann & Marx, Werner & Daniel, Hans-Dieter, 2012. "What factors determine citation counts of publications in chemistry besides their quality?," Journal of Informetrics, Elsevier, vol. 6(1), pages 11-18.
    15. Loet Leydesdorff, 2007. "Betweenness centrality as an indicator of the interdisciplinarity of scientific journals," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 58(9), pages 1303-1319, July.
    16. Lawrence D. Fu & Constantin F. Aliferis, 2010. "Using content-based and bibliometric features for machine learning models to predict citation counts in the biomedical literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 85(1), pages 257-270, October.
    17. Johan Bollen & Herbert Van de Sompel & Aric Hagberg & Ryan Chute, 2009. "A Principal Component Analysis of 39 Scientific Impact Measures," PLOS ONE, Public Library of Science, vol. 4(6), pages 1-11, June.
    18. Fereshteh Didegah & Mike Thelwall, 2013. "Determinants of research citation impact in nanoscience and nanotechnology," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 64(5), pages 1055-1064, May.
    19. Peder Olesen Larsen & Markus Ins, 2010. "The rate of growth in scientific publication and the decline in coverage provided by Science Citation Index," Scientometrics, Springer;Akadémiai Kiadó, vol. 84(3), pages 575-603, September.
    20. Rickard Danell, 2011. "Can the quality of scientific work be predicted using information on the author's track record?," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(1), pages 50-60, January.
    21. Hendrik P. Van Dalen & Kène Henkens, 2001. "What makes a scientific article influential? The case of demographers," Scientometrics, Springer;Akadémiai Kiadó, vol. 50(3), pages 455-482, 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. Xie, Zheng, 2020. "Predicting publication productivity for researchers: A piecewise Poisson model," Journal of Informetrics, Elsevier, vol. 14(3).
    2. Jung, Hohyun, 2023. "Eliminating the biases of user influence and item popularity in bipartite networks: A case study of Flickr and Netflix," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).
    3. Kumar, Ajay & Singh, Shashank Sheshar & Singh, Kuldeep & Biswas, Bhaskar, 2020. "Link prediction techniques, applications, and performance: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 553(C).
    4. Wanjun Xia & Tianrui Li & Chongshou Li, 2023. "A review of scientific impact prediction: tasks, features and methods," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 543-585, January.
    5. Wumei Du & Zheng Xie & Yiqin Lv, 2021. "Predicting publication productivity for authors: Shallow or deep architecture?," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5855-5879, July.
    6. Guillermo Armando Ronda-Pupo & J. Sylvan Katz, 2017. "The scaling relationship between degree centrality of countries and their citation-based performance on Management Information Systems," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1285-1299, September.
    7. Wenyan Wang & Jun Zhang & Fang Zhou & Peng Chen & Bing Wang, 2021. "Paper acceptance prediction at the institutional level based on the combination of individual and network features," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1581-1597, February.
    8. Choudhury, Nazim & Faisal, Fahim & Khushi, Matloob, 2020. "Mining Temporal Evolution of Knowledge Graphs and Genealogical Features for Literature-based Discovery Prediction," Journal of Informetrics, Elsevier, vol. 14(3).
    9. Cui, Haochuan & Zeng, An & Fan, Ying & Di, Zengru, 2021. "Quantifying the impact of a teamwork publication," Journal of Informetrics, Elsevier, vol. 15(4).
    10. Stefan Thurner & Wenyuan Liu & Peter Klimek & Siew Ann Cheong, 2020. "The role of mainstreamness and interdisciplinarity for the relevance of scientific papers," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-14, April.
    11. Liang, Zhentao & Ba, Zhichao & Mao, Jin & Li, Gang, 2023. "Research complexity increases with scientists’ academic age: Evidence from library and information science," Journal of Informetrics, Elsevier, vol. 17(1).

    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. Tian Yu & Guang Yu & Peng-Yu Li & Liang Wang, 2014. "Citation impact prediction for scientific papers using stepwise regression analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1233-1252, November.
    2. Iman Tahamtan & Askar Safipour Afshar & Khadijeh Ahamdzadeh, 2016. "Factors affecting number of citations: a comprehensive review of the literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(3), pages 1195-1225, June.
    3. Li, Xin & Wen, Yang & Jiang, Jiaojiao & Daim, Tugrul & Huang, Lucheng, 2022. "Identifying potential breakthrough research: A machine learning method using scientific papers and Twitter data," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    4. Mingyang Wang & Zhenyu Wang & Guangsheng Chen, 2019. "Which can better predict the future success of articles? Bibliometric indices or alternative metrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(3), pages 1575-1595, June.
    5. Lindahl, Jonas, 2018. "Predicting research excellence at the individual level: The importance of publication rate, top journal publications, and top 10% publications in the case of early career mathematicians," Journal of Informetrics, Elsevier, vol. 12(2), pages 518-533.
    6. 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.
    7. Tahamtan, Iman & Bornmann, Lutz, 2018. "Core elements in the process of citing publications: Conceptual overview of the literature," Journal of Informetrics, Elsevier, vol. 12(1), pages 203-216.
    8. Waltman, Ludo, 2016. "A review of the literature on citation impact indicators," Journal of Informetrics, Elsevier, vol. 10(2), pages 365-391.
    9. Zhang, Xinyuan & Xie, Qing & Song, Min, 2021. "Measuring the impact of novelty, bibliometric, and academic-network factors on citation count using a neural network," Journal of Informetrics, Elsevier, vol. 15(2).
    10. Elizabeth S. Vieira, 2023. "The influence of research collaboration on citation impact: the countries in the European Innovation Scoreboard," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(6), pages 3555-3579, June.
    11. Ajiferuke, Isola & Famoye, Felix, 2015. "Modelling count response variables in informetric studies: Comparison among count, linear, and lognormal regression models," Journal of Informetrics, Elsevier, vol. 9(3), pages 499-513.
    12. Basma Albanna & Julia Handl & Richard Heeks, 2021. "Publication outperformance among global South researchers: An analysis of individual-level and publication-level predictors of positive deviance," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(10), pages 8375-8431, October.
    13. Didegah, Fereshteh & Thelwall, Mike, 2013. "Which factors help authors produce the highest impact research? Collaboration, journal and document properties," Journal of Informetrics, Elsevier, vol. 7(4), pages 861-873.
    14. Mingyang Wang & Guang Yu & Shuang An & Daren Yu, 2012. "Discovery of factors influencing citation impact based on a soft fuzzy rough set model," Scientometrics, Springer;Akadémiai Kiadó, vol. 93(3), pages 635-644, December.
    15. Martorell Cunil, Onofre & Otero González, Luis & Durán Santomil, Pablo & Mulet Forteza, Carlos, 2023. "How to accomplish a highly cited paper in the tourism, leisure and hospitality field," Journal of Business Research, Elsevier, vol. 157(C).
    16. Wanjun Xia & Tianrui Li & Chongshou Li, 2023. "A review of scientific impact prediction: tasks, features and methods," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 543-585, January.
    17. Liu, Jialin & Chen, Hongkan & Liu, Zhibo & Bu, Yi & Gu, Weiye, 2022. "Non-linearity between referencing behavior and citation impact: A large-scale, discipline-level analysis," Journal of Informetrics, Elsevier, vol. 16(3).
    18. Yezhu Wang & Yundong Xie & Dong Wang & Lu Guo & Rongting Zhou, 2022. "Do cover papers get better citations and usage counts? An analysis of 42 journals in cell biology," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(7), pages 3793-3813, July.
    19. Kaile Gong & Juan Xie & Ying Cheng & Vincent Larivière & Cassidy R. Sugimoto, 2019. "The citation advantage of foreign language references for Chinese social science papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(3), pages 1439-1460, September.
    20. Wang, Mingyang & Yu, Guang & Xu, Jianzhong & He, Huixin & Yu, Daren & An, Shuang, 2012. "Development a case-based classifier for predicting highly cited papers," Journal of Informetrics, Elsevier, vol. 6(4), pages 586-599.

    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:107:y:2016:i:3:d:10.1007_s11192-016-1926-1. 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.