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Hierarchical networks of scientific journals

Author

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  • Gergely Palla

    (MTA-ELTE Statistical and Biological Physics Research Group, Hungarian Academy of Sciences, Budapest, Hungary
    Regional Knowledge Centre, Eötvös University, Budapest, Hungary)

  • Gergely Tibély

    (Department of Biological Physics, Eötvös University, Budapest, Hungary.)

  • Enys Mones

    (Regional Knowledge Centre, Eötvös University, Budapest, Hungary)

  • Péter Pollner

    (MTA-ELTE Statistical and Biological Physics Research Group, Hungarian Academy of Sciences, Budapest, Hungary
    Regional Knowledge Centre, Eötvös University, Budapest, Hungary)

  • Tamás Vicsek

    (MTA-ELTE Statistical and Biological Physics Research Group, Hungarian Academy of Sciences, Budapest, Hungary
    Department of Biological Physics, Eötvös University, Budapest, Hungary.)

Abstract

Academic journals are the repositories of mankind’s gradually accumulating knowledge of the surrounding world. Just as knowledge is organized into classes ranging from major disciplines, subjects and fields, to increasingly specific topics, journals can also be categorized into groups using various metric. In addition, they can be ranked according to their overall influence. However, according to recent studies, the impact, prestige and novelty of journals cannot be characterized by a single parameter such as, for example, the impact factor. To increase understanding of journal impact, the knowledge gap we set out to explore in our study is the evaluation of journal relevance using complex multi-dimensional measures. Thus, for the first time, our objective is to organize journals into multiple hierarchies based on citation data. The two approaches we use are designed to address this problem from different perspectives. We use a measure related to the notion of m-reaching centrality and find a network that shows a journal’s level of influence in terms of the direction and efficiency with which information spreads through the network. We find we can also obtain an alternative network using a suitably modified nested hierarchy extraction method applied to the same data. In this case, in a self-organized way, the journals become branches according to the major scientific fields, where the local structure of the branches reflect the hierarchy within the given field, with usually the most prominent journal (according to other measures) in the field chosen by the algorithm as the local root, and more specialized journals positioned deeper in the branch. This can make the navigation within different scientific fields and sub-fields very simple, and equivalent to navigating in the different branches of the nested hierarchy. We expect this to be particularly helpful, for example, when choosing the most appropriate journal for a given manuscript. According to our results, the two alternative hierarchies show a somewhat different, but also consistent, picture of the intricate relations between scientific journals, and, as such, they also provide a new perspective on how scientific knowledge is organized into networks.

Suggested Citation

  • Gergely Palla & Gergely Tibély & Enys Mones & Péter Pollner & Tamás Vicsek, 2015. "Hierarchical networks of scientific journals," Palgrave Communications, Palgrave Macmillan, vol. 1(palcomms2), pages 15016-15016, July.
  • Handle: RePEc:pal:palcom:v:2015:y:2015:i:palcomms201516:p:15016-
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    Citations

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    Cited by:

    1. Gergely Tibély & David Sousa-Rodrigues & Péter Pollner & Gergely Palla, 2016. "Comparing the Hierarchy of Keywords in On-Line News Portals," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-15, November.
    2. Gergely Honti & János Abonyi, 2021. "Frequent Itemset Mining and Multi-Layer Network-Based Analysis of RDF Databases," Mathematics, MDPI, vol. 9(4), pages 1-17, February.
    3. Hu, Fei & Zhao, Shangmei & Bing, Tao & Chang, Yiming, 2017. "Hierarchy in industrial structure: The cases of China and the USA," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 871-882.
    4. Yurij L. Katchanov & Yulia V. Markova, 2017. "The “space of physics journals”: topological structure and the Journal Impact Factor," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(1), pages 313-333, October.
    5. Zhang, Min & Wang, Xiaojuan & Jin, Lei & Song, Mei, 2021. "Cascade phenomenon in multilayer networks with dependence groups and hierarchical structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    6. Alejandro Huertas Herrera & Mónica D. R. Toro-Manríquez & Cristian Lorenzo & María Vanessa Lencinas & Guillermo Martínez Pastur, 2023. "Perspectives on socio-ecological studies in the Northern and Southern Hemispheres," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-14, December.
    7. Katchanov, Yurij L. & Markova, Yulia V. & Shmatko, Natalia A., 2019. "The distinction machine: Physics journals from the perspective of the Kolmogorov–Smirnov statistic," Journal of Informetrics, Elsevier, vol. 13(4).

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