IDEAS home Printed from https://ideas.repec.org/a/eee/infome/v12y2018i3p757-783.html
   My bibliography  Save this article

Stochastic block model reveals maps of citation patterns and their evolution in time

Author

Listed:
  • Hric, Darko
  • Kaski, Kimmo
  • Kivelä, Mikko

Abstract

In this study we map out the large-scale structure of citation networks of science journals and follow their evolution in time by using stochastic block models (SBMs). The SBM fitting procedures are principled methods that can be used to find hierarchical grouping of journals that show similar incoming and outgoing citations patterns. These methods work directly on the citation network without the need to construct auxiliary networks based on similarity of nodes. We fit the SBMs to the networks of journals we have constructed from the data set of around 630 million citations and find a variety of different types of groups, such as communities, bridges, sources, and sinks. In addition we use a recent generalization of SBMs to determine how much a manually curated classification of journals into subfields of science is related to the group structure of the journal network and how this relationship changes in time. The SBM method tries to find a network of blocks that is the best high-level representation of the network of journals, and we illustrate how these block networks (at various levels of resolution) can be used as maps of science.

Suggested Citation

  • Hric, Darko & Kaski, Kimmo & Kivelä, Mikko, 2018. "Stochastic block model reveals maps of citation patterns and their evolution in time," Journal of Informetrics, Elsevier, vol. 12(3), pages 757-783.
    • Handle: RePEc:eee:infome:v:12:y:2018:i:3:p:757-783
    DOI: 10.1016/j.joi.2018.05.004
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1751157717301499
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.joi.2018.05.004?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

    for a different version of it.

    References listed on IDEAS

    as
    1. D. S. Choi & P. J. Wolfe & E. M. Airoldi, 2012. "Stochastic blockmodels with a growing number of classes," Biometrika, Biometrika Trust, vol. 99(2), pages 273-284.
    2. Wang, Qi & Waltman, Ludo, 2016. "Large-scale analysis of the accuracy of the journal classification systems of Web of Science and Scopus," Journal of Informetrics, Elsevier, vol. 10(2), pages 347-364.
    3. Gergely Palla & Imre Derényi & Illés Farkas & Tamás Vicsek, 2005. "Uncovering the overlapping community structure of complex networks in nature and society," Nature, Nature, vol. 435(7043), pages 814-818, June.
    4. Benjamin M. Althouse & Jevin D. West & Carl T. Bergstrom & Theodore Bergstrom, 2009. "Differences in impact factor across fields and over time," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(1), pages 27-34, January.
    5. Jochen Gläser & Wolfgang Glänzel & Andrea Scharnhorst, 2017. "Same data—different results? Towards a comparative approach to the identification of thematic structures in science," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 981-998, May.
    6. Chen, P. & Redner, S., 2010. "Community structure of the physical review citation network," Journal of Informetrics, Elsevier, vol. 4(3), pages 278-290.
    7. Persson, Olle, 2010. "Identifying research themes with weighted direct citation links," Journal of Informetrics, Elsevier, vol. 4(3), pages 415-422.
    8. Small, Henry & Boyack, Kevin W. & Klavans, Richard, 2014. "Identifying emerging topics in science and technology," Research Policy, Elsevier, vol. 43(8), pages 1450-1467.
    9. Kevin W. Boyack & Richard Klavans, 2014. "Creation of a highly detailed, dynamic, global model and map of science," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(4), pages 670-685, April.
    10. Mark P. Carpenter & Francis Narin, 1973. "Clustering of scientific journals," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 24(6), pages 425-436, November.
    11. Kevin W. Boyack & Richard Klavans & Katy Börner, 2005. "Mapping the backbone of science," Scientometrics, Springer;Akadémiai Kiadó, vol. 64(3), pages 351-374, August.
    12. Loet Leydesdorff & Stephen Carley & Ismael Rafols, 2013. "Global maps of science based on the new Web-of-Science categories," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(2), pages 589-593, February.
    13. Leydesdorff, Loet & Rafols, Ismael, 2012. "Interactive overlays: A new method for generating global journal maps from Web-of-Science data," Journal of Informetrics, Elsevier, vol. 6(2), pages 318-332.
    14. Theresa Velden & Shiyan Yan & Carl Lagoze, 2017. "Mapping the cognitive structure of astrophysics by infomap clustering of the citation network and topic affinity analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 1033-1051, May.
    15. Nees Jan van Eck & Ludo Waltman, 2009. "How to normalize cooccurrence data? An analysis of some well‐known similarity measures," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(8), pages 1635-1651, August.
    16. Loet Leydesdorff & Ismael Rafols, 2009. "A global map of science based on the ISI subject categories," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(2), pages 348-362, February.
    17. Waltman, Ludo & van Eck, Nees Jan & Noyons, Ed C.M., 2010. "A unified approach to mapping and clustering of bibliometric networks," Journal of Informetrics, Elsevier, vol. 4(4), pages 629-635.
    18. Zhang, Lin & Liu, Xinhai & Janssens, Frizo & Liang, Liming & Glänzel, Wolfgang, 2010. "Subject clustering analysis based on ISI category classification," Journal of Informetrics, Elsevier, vol. 4(2), pages 185-193.
    19. van Eck, N.J.P. & Waltman, L., 2009. "How to Normalize Co-Occurrence Data? An Analysis of Some Well-Known Similarity Measures," ERIM Report Series Research in Management ERS-2009-001-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    20. M. M. Kessler, 1963. "Bibliographic coupling between scientific papers," American Documentation, Wiley Blackwell, vol. 14(1), pages 10-25, January.
    21. Loet Leydesdorff, 2006. "Can scientific journals be classified in terms of aggregated journal‐journal citation relations using the Journal Citation Reports?," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(5), pages 601-613, March.
    22. Pieters, R. & Baumgartner, H. & Vermunt, J.K. & Bijmolt, T.H.A., 1999. "Importance and similarity in the evolving citation network of the International Journal of Research in Marketing," Other publications TiSEM e2c16930-bf58-49ae-8cc9-f, Tilburg University, School of Economics and Management.
    23. Lambiotte, R. & Panzarasa, P., 2009. "Communities, knowledge creation, and information diffusion," Journal of Informetrics, Elsevier, vol. 3(3), pages 180-190.
    24. Barucca, Paolo & Lillo, Fabrizio, 2016. "Disentangling bipartite and core-periphery structure in financial networks," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 244-253.
    25. Henry Small, 1973. "Co‐citation in the scientific literature: A new measure of the relationship between two documents," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 24(4), pages 265-269, July.
    26. Shenghui Wang & Rob Koopman, 2017. "Clustering articles based on semantic similarity," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 1017-1031, May.
    27. M. E. J. Newman & Aaron Clauset, 2016. "Structure and inference in annotated networks," Nature Communications, Nature, vol. 7(1), pages 1-11, September.
    28. Henry Small, 1999. "Visualizing science by citation mapping," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 50(9), pages 799-813.
    29. Silva, F.N. & Rodrigues, F.A. & Oliveira, O.N. & da F. Costa, L., 2013. "Quantifying the interdisciplinarity of scientific journals and fields," Journal of Informetrics, Elsevier, vol. 7(2), pages 469-477.
    30. Parolo, Pietro Della Briotta & Pan, Raj Kumar & Ghosh, Rumi & Huberman, Bernardo A. & Kaski, Kimmo & Fortunato, Santo, 2015. "Attention decay in science," Journal of Informetrics, Elsevier, vol. 9(4), pages 734-745.
    31. Alan L. Porter & Ismael Rafols, 2009. "Is science becoming more interdisciplinary? Measuring and mapping six research fields over time," Scientometrics, Springer;Akadémiai Kiadó, vol. 81(3), pages 719-745, December.
    32. Sune Lehmann & Andrew D. Jackson & Benny E. Lautrup, 2008. "A quantitative analysis of indicators of scientific performance," Scientometrics, Springer;Akadémiai Kiadó, vol. 76(2), pages 369-390, August.
    Full references (including those not matched with items on IDEAS)

    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. Ying Huang & Wolfgang Glänzel & Lin Zhang, 2021. "Tracing the development of mapping knowledge domains," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 6201-6224, July.
    2. Waltman, Ludo & van Eck, Nees Jan & Noyons, Ed C.M., 2010. "A unified approach to mapping and clustering of bibliometric networks," Journal of Informetrics, Elsevier, vol. 4(4), pages 629-635.
    3. Jochen Gläser & Wolfgang Glänzel & Andrea Scharnhorst, 2017. "Same data—different results? Towards a comparative approach to the identification of thematic structures in science," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 981-998, May.
    4. Sjögårde, Peter & Ahlgren, Per, 2018. "Granularity of algorithmically constructed publication-level classifications of research publications: Identification of topics," Journal of Informetrics, Elsevier, vol. 12(1), pages 133-152.
    5. Rodolfo Modrigais Strauss Nunes & Susana Carla Farias Pereira, 2022. "Intellectual structure and trends in the humanitarian operations field," Annals of Operations Research, Springer, vol. 319(1), pages 1099-1157, December.
    6. Ismael Rafols & Alan Porter & Loet Leydesdorff, 2009. "Overlay Maps of Science: a New Tool for Research Policy," SPRU Working Paper Series 179, SPRU - Science Policy Research Unit, University of Sussex Business School.
    7. Cobo, M.J. & López-Herrera, A.G. & Herrera-Viedma, E. & Herrera, F., 2011. "An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory field," Journal of Informetrics, Elsevier, vol. 5(1), pages 146-166.
    8. Ismael Rafols, 2025. "Towards multiple ontologies in science mapping. A tribute to Loet Leydesdorff," Scientometrics, Springer;Akadémiai Kiadó, vol. 130(6), pages 3229-3255, June.
    9. Leydesdorff, Loet & Bornmann, Lutz & Zhou, Ping, 2016. "Construction of a pragmatic base line for journal classifications and maps based on aggregated journal-journal citation relations," Journal of Informetrics, Elsevier, vol. 10(4), pages 902-918.
    10. Andrea Bonaccorsi & Nicola Melluso & Francesco Alessandro Massucci, 2022. "Exploring the antecedents of interdisciplinarity at the European Research Council: a topic modeling approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(12), pages 6961-6991, December.
    11. Xiaoguang Wang & Qikai Cheng & Wei Lu, 2014. "Analyzing evolution of research topics with NEViewer: a new method based on dynamic co-word networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1253-1271, November.
    12. Karmen Stopar & Damjana Drobne & Klemen Eler & Tomaz Bartol, 2016. "Citation analysis and mapping of nanoscience and nanotechnology: identifying the scope and interdisciplinarity of research," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(2), pages 563-581, February.
    13. Lilian Cervo Cabrera & Carlos Eduardo Caldarelli & Marcia Regina Gabardo Camara, 2020. "Mapping collaboration in international coffee certification research," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 2597-2618, September.
    14. Perianes-Rodriguez, Antonio & Waltman, Ludo & van Eck, Nees Jan, 2016. "Constructing bibliometric networks: A comparison between full and fractional counting," Journal of Informetrics, Elsevier, vol. 10(4), pages 1178-1195.
    15. Juntao Zheng & Niancai Liu, 2015. "Mapping of important international academic awards," Scientometrics, Springer;Akadémiai Kiadó, vol. 104(3), pages 763-791, September.
    16. Patrick Röhm, 2018. "Exploring the landscape of corporate venture capital: a systematic review of the entrepreneurial and finance literature," Management Review Quarterly, Springer, vol. 68(3), pages 279-319, August.
    17. Ruimin Ma & Erjia Yan, 2016. "Uncovering inter-specialty knowledge communication using author citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(2), pages 839-854, November.
    18. Igors Skute & Kasia Zalewska-Kurek & Isabella Hatak & Petra Weerd-Nederhof, 2019. "Mapping the field: a bibliometric analysis of the literature on university–industry collaborations," The Journal of Technology Transfer, Springer, vol. 44(3), pages 916-947, June.
    19. Silva, Filipi N. & Amancio, Diego R. & Bardosova, Maria & Costa, Luciano da F. & Oliveira, Osvaldo N., 2016. "Using network science and text analytics to produce surveys in a scientific topic," Journal of Informetrics, Elsevier, vol. 10(2), pages 487-502.
    20. Nassiri, Isar & Masoudi-Nejad, Ali & Jalili, Mahdi & Moeini, Ali, 2013. "Normalized Similarity Index: An adjusted index to prioritize article citations," Journal of Informetrics, Elsevier, vol. 7(1), pages 91-98.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:eee:infome:v:12:y:2018:i:3:p:757-783. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/joi .

    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.