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A pilot model of the scientific journal network in Russia: An intersection graph analysis

https://doi.org/10.26907/2541-7746.2025.2.311-328

Abstract

Scientific journals play an important role as a key tool for advancing science and encouraging knowledge exchange during research collaboration. The relationships among them are extremely diverse and cluster together into a complex network of connections. Developing adequate mathematical models of such self-organizing systems is a serious problem that requires in-depth investigation. This article introduces an approach to analyze the journal network based on a new bibliographic graph in which the relationships among journals are defined by shared authorship, i.e., through the binary intersection of author sets. Two levels of journal interaction can be distinguished using consistent source data processing techniques and methods. At the lower level, the relationships among multiple journals are grouped by scientific topics. At the upper level, the interactions between these topics are established. The approach was validated through a pilot study of eLIBRARY.RU data. The findings demonstrate its feasibility and scalability potential.

About the Author

A. A. Pechnikov
Karelian Research Centre, Russian Academy of Sciences
Russian Federation

Andrey A. Pechnikov, Dr. Sci. (Engineering), Associate Professor, Leading Researcher

Petrozavodsk



References

1. Novikov D.A. Who is winning the H-index race? (Reflections on scientometrics). Vyssh. Obraz. Ross., 2015, no. 2, pp. 5–13. (In Russian)

2. Semenov E.V. On the revival of the national network of academic journals. Upr. Naukoi: Teor. Prakt., 2023, vol. 5, no. 4, pp. 10–13. (In Russian)

3. Von Bertalanffy L. The history and status of general systems theory. In: Sistemnye issledovaniya. Metodologicheskie problemy. Ezhegodnik [Systemic Studies. Methodological Problems. Yearbook]. Moscow, Nauka, 1973, pp. 20–37. (In Russian)

4. State Standard 7.0.60-2020. System of standards on information, librarianship, and publishing. Publications. Basic types. Terms and definitions. Moscow, Standartinform, 2020. 46 p. (In Russian)

5. Bredikhin S.V., Lyapunov V.M., Shcherbakova N.G. Bibliometricheskie seti nauchnykh statei i zhurnalov [Bibliometric Networks of Scientific Articles and Journals]. Novosibirsk, IVMi Sib. Otd. Ross. Akad. Nauk, 2021. 334 p. (In Russian)

6. Russian State Library. Printing statistics. (In Russian) URL: https://www.rsl.ru/ru/rkp/gos-bbu/statistika-pechati-1i-spravochnaya-rabota.

7. Scientific Electronic Library eLIBRARY. URL: https://www.elibrary.ru. (In Russian)

8. Novikov A.M., Novikov D.A. Metodologiya [Methodology]. Moscow, Sinteg, 2007. 668 p. (In Russian)

9. Fortunato S., Bergstrom C.T., Evans J.A., Helbing D., Milojevi´c S., Petersen A.M., Radicchi F., Sinatra R., Uzzi B., Vespignani A., Waltman L., Wang D., Barab´asi A.-L. Science of science. Science, 2018, vol. 359, no. 6379, art. eaao0185. https://doi.org/10.1126/science.aao0185.

10. Perianes-Rodriguez A., Waltman L., van Eck N.J. Constructing bibliometric networks: A comparison between full and fractional counting. J. Inform., 2016, vol. 10, no. 4, pp. 1178–1195. https://doi.org/10.1016/j.joi.2016.10.006.

11. Malliaros F.D., Vazirgiannis M. Clustering and community detection in directed networks: A survey. Phys. Rep., 2013, vol. 533, no. 4, pp. 95–142. https://doi.org/10.1016/j.physrep.2013.08.002.

12. Leydesdorff L., Rafols I. Indicators of the interdisciplinarity of journals: Diversity, centrality, and citations. J. Inform., 2011, vol. 5, no. 1, pp. 87–100. https://doi.org/10.1016/j.joi.2010.09.002.

13. Bredikhin S.V., Lyapunov V.M., Shcherbakova N.G. The structure of the citation network of scientific journals. Probl. Inf., 2017, no. 2, pp. 38–52. (In Russian)

14. RePEc. General principles. URL: http://repec.org.

15. Math-Net.Ru: All-Russian mathematical portal. URL: https://www.mathnet.ru. (In Russian)

16. Znamenskaya E.A., Pechnikov A.A., Chebukov D.E. Analysis of the Russian Science Citation Index according to Math-Net.ru data. Electron. Bibl., 2023, vol. 26, no. 6, pp. 778–795. (In Russian)

17. Pechnikov A.A. Journal intersection graph: Definition, modifications, and a meaningful example. UBS, 2025, no. 114. pp. 122–137. (In Russian)

18. Levandowsky M., Winter D. Distance between sets. Nature, 1971, vol. 234, no. 5323, pp. 34–35. https://doi.org/10.1038/234034a0.

19. Journal Catalogue. URL: https://www.elibrary.ru/titles.asp. (In Russian)

20. RSCI list of journals. URL: https://elibrary.ru/project_rsci.asp. (In Russian)

21. About the new journals ranking system SCIENCE INDEX. (In Russian) URL: https://elibrary.ru/projects/science_index/ranking_info.asp.

22. The “white list” of scientific journals. URL: https://journalrank.rcsi.science/ru/. (In Russian)

23. Newman M.E.J. Modularity and community structure in networks. Proc. Natl. Acad. Sci. U.S.A., 2006, vol. 103, no. 23, pp. 8577–8582. https://doi.org/10.1073/pnas.0601602103.

24. Ward J.H., Jr. Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc., 1963, vol. 58, no. 301, pp. 236–244. https://doi.org/10.1080/01621459.1963.10500845.

25. Bonacich P. Factoring and weighting approaches to status scores and clique identification. J. Math. Sociol., 1972, vol. 2, no. 1, pp. 113–120. https://doi.org/10.1080/0022250X.1972.9989806.

26. Freeman L.C. A set of measures of centrality based on betweenness. Sociometry, 1977, vol. 40, no. 1, pp. 35–41. https://doi.org/10.2307/3033543.

27. Krackhardt D. Assessing the political landscape: Structure, cognition, and power in organizations. Adm. Sci. Q., 1990, vol. 35, no. 2, pp. 342–369. https://doi.org/10.2307/2393394.

28. Azarova I.N., Kuchkina A.Yu., Baram G.I., Goldberg E.L. Prediction of peptide retention volumes in gradient reversed phase HPLC. Russ. J. Bioorg. Chem., 2008, vol. 34, no. 2, pp. 156–161. https://doi.org/10.1134/S1068162008020039.

29. Goldberg E.L., Kuper K.E., Slyusarenko I.Yu. Preliminary results on the use of computational X-ray tomography for the analysis of archaeological wooden products. PAEASST, 2010, vol. 16, pp. 176–180. (In Russian)

30. Olenchenko V.V., Tsibizov L.V., Osipova P.S., Chargynov T.T., Viola B.T., Kolobova K.A., Krivoshapkin A.I. Application of 2D electrical resistivity tomography in caves. AEAE, 2020, vol. 48, no. 4, pp. 67–74. (In Russian)

31. Marinenko A.V., Epov M.I., Olenchenko V.V. Solving direct problems of electrical resistivity tomography for media with high-conductivity irregular-shaped heterogeneities by an example of a multiple well platform. J. Appl. Ind. Math., 2019, vol. 13. no. 1, pp. 93–102. https://doi.org/10.1134/S1990478919010113.

32. Semenov E.V. Developing a network of scientific journals in Russia: Strategic, technological, and organizational problems. Sotsiol. Nauka Sots. Prakt., 2023, vol. 11, no. 3, pp. 116—140. (In Russian)


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For citations:


Pechnikov A.A. A pilot model of the scientific journal network in Russia: An intersection graph analysis. Uchenye Zapiski Kazanskogo Universiteta. Seriya Fiziko-Matematicheskie Nauki. 2025;167(2):311-328. (In Russ.) https://doi.org/10.26907/2541-7746.2025.2.311-328

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ISSN 2541-7746 (Print)
ISSN 2500-2198 (Online)