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A hypergraph model for representing scientific output

Author

Listed:
  • Rodica Ioana Lung

    (Babeş-Bolyai University)

  • Noémi Gaskó

    (Babeş-Bolyai University)

  • Mihai Alexandru Suciu

    (Babeş-Bolyai University)

Abstract

Representation and analysis of publication data in the form of a network has become a common method of illustrating and evaluating the scientific output of a group or of a scientific field. Co-authorship networks also reveal patterns and collaboration practices. In this paper we propose the use of a hypergraph model—a generalized network—to represent publication data by considering papers as hypergraph nodes. Hyperedges, connecting the nodes, represent the authors connecting all their papers. We show that this representation is more straightforward than other authorship network models. Using the hypergraph model we propose a collaboration measure of an author that reflects the influence of that author over the collaborations of its co-authors. We illustrate the introduced concepts by analyzing publishing data of computer scientists and mathematicians in Romania over a 10 year period.

Suggested Citation

  • Rodica Ioana Lung & Noémi Gaskó & Mihai Alexandru Suciu, 2018. "A hypergraph model for representing scientific output," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(3), pages 1361-1379, December.
    • Handle: RePEc:spr:scient:v:117:y:2018:i:3:d:10.1007_s11192-018-2908-2
    DOI: 10.1007/s11192-018-2908-2
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    References listed on IDEAS

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    1. Nuša Erman & Ljupčo Todorovski, 2015. "The effects of measurement error in case of scientific network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 104(2), pages 453-473, August.
    2. Gita Ghiasi & Matthew Harsh & Andrea Schiffauerova, 2018. "Inequality and collaboration patterns in Canadian nanotechnology: implications for pro-poor and gender-inclusive policy," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(2), pages 785-815, May.
    3. Raf Guns & Ronald Rousseau, 2014. "Recommending research collaborations using link prediction and random forest classifiers," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1461-1473, November.
    4. Miloš Savić & Mirjana Ivanović & Miloš Radovanović & Zoran Ognjanović & Aleksandar Pejović & Tatjana Jakšić Krüger, 2014. "The structure and evolution of scientific collaboration in Serbian mathematical journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(3), pages 1805-1830, December.
    5. Li, Yongjun & You, Chun, 2013. "What is the difference of research collaboration network under different projections: Topological measurement and analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(15), pages 3248-3259.
    6. Perc, Matjaž, 2010. "Growth and structure of Slovenia’s scientific collaboration network," Journal of Informetrics, Elsevier, vol. 4(4), pages 475-482.
    7. Christian Genest & Carl Thibault, 2001. "Investigating the concentration within a research community using joint publications and co-authorship via intermediaries," Scientometrics, Springer;Akadémiai Kiadó, vol. 51(2), pages 429-440, June.
    8. Jinseok Kim & Jana Diesner, 2015. "Coauthorship networks: A directed network approach considering the order and number of coauthors," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(12), pages 2685-2696, December.
    9. Georg Groh & Christoph Fuchs, 2011. "Multi-modal social networks for modeling scientific fields," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(2), pages 569-590, November.
    10. Jinseok Kim & Jana Diesner, 2014. "A network-based approach to coauthorship credit allocation," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(1), pages 587-602, October.
    11. T. S. Evans & R. Lambiotte & P. Panzarasa, 2011. "Community structure and patterns of scientific collaboration in Business and Management," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(1), pages 381-396, October.
    12. Domenico De Stefano & Giuseppe Giordano & Maria Vitale, 2011. "Issues in the analysis of co-authorship networks," Quality & Quantity: International Journal of Methodology, Springer, vol. 45(5), pages 1091-1107, August.
    13. Noémi Gaskó & Rodica Ioana Lung & Mihai Alexandru Suciu, 2016. "A new network model for the study of scientific collaborations: Romanian computer science and mathematics co-authorship networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(2), pages 613-632, August.
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    Cited by:

    1. Daniela Aguirre-Guerrero & Roberto Bernal-Jaquez, 2023. "A Methodology for the Analysis of Collaboration Networks with Higher-Order Interactions," Mathematics, MDPI, vol. 11(10), pages 1-17, May.

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