Methodology for Analyzing Thematic Co-Authorship Networks
Abstract
An interesting feature that networks present is the community structure property, under which a graph topology is organized into modules called communities. This paper proposes a co-authorship graph-based methodology for studying thematic communities. The graph is divided into thematic communities to identify their basic characteristics, such as research thematic direction, researcher count, community count and relationship between researchers. This information could be used to make incentives policies that would support pertinent research areas. The proposed methodology was tested using data retrieved from mathematical portal Math-Net.Ru. The findings of the tests indicate that more research in robots and robotic systems, combustion/explosion, and data protection techniques/systems need to be promoted. It was demonstrated that the mathematical models employed were adequate and applicable to different research domains. However, this would need complete and reliable basic bibliographic data on research co-authorship within the relevant discipline over a long enough period.
Full Text:
PDFReferences
S. L. Yang, Q. L. Yuan, and J. H. Dong, Are Scientometrics, Informetrics, and Bibliometrics Different?, Data Science and Informetrics, vol. 1, pp. 50 –72, 2020. https://doi.org/10.4236/dsi.2020.11003
J. Qiu, R. Zhao, S. Yang, and K. Dong, Informetrics: Theory, Methods and Applications, first ed., Springer, Singapore, 2017.
P. Kalachikhin, Combined Methods for Forecasting Scientific Achievements, Scientific and Technical Information Processing, vol. 48, no. 4., pp. 231–238, 2021. https://doi.org/10.3103/S014768822104002X
A. F. Repko, R. Szostak, and Buchberger M. P., Introduction to Interdisciplinary Studies, third ed., SAGE Publications, 2019, 448 p.
H. O. Witteman, and J. E. Stahl, Facilitating interdisciplinary collaboration to tackle complex problems in health care: Report from an exploratory workshop, Health Systems, vol. 2, no. 3, pp. 162–170, 2013. https://doi.org/10.1057/hs.2013.3
M. Ullah, A. Shahid, I. Din, M. Roman, M. Assam, M. Fayaz, Y. Ghadi, and H. Aljuaid, Analyzing Interdisciplinary Research Using Co-Authorship Networks, Complexity, vol. 2022, no. 2524491, 13 pp, 2022. https://doi.org/10.1155/2022/2524491
S.A. Lebedev, The main models of development of scientific knowledge, Herald of the Russian Academy of Sciences, vol. 84, pp. 201-207, 2014. https://doi.org/10.1134/S1019331614030095.
R. Hazra, M. Singh, P. Goyal, B. Adhikari, and A. Mukherjee, Modeling interdisciplinary interactions among physics, mathematics and computer science, Journal of Physics: Complexity, vol. 4, no. 4, 2023. https://doi.org/10.1088/2632-072X/ad0017
Q. Li, Overview of Data Visualization. Embodying Data: Chinese Aesthetics, Interactive Visualization and Gaming Technologies, pp. 17–47, 2020. https://doi.org/10.1007/978-981-15-5069-0_2
K. Eberhard, The effects of visualization on judgment and decision-making: a systematic literature review, Management Review Quarterly, vol. 73, pp. 167–214, 2023. https://doi.org/10.1007/s11301-021-00235-8
L. Leydesdorff and I. Rafols, A global map of science based on the ISI subject categories, Journal of the American Society for Information Science and Technology, vol. 60, no. 2, pp. 348–362, 2009.
B. Milman and I. Zhurkovich, Analytics and bioanalytics on the maps of science, Analytics, vol. 2, pp. 34–41, 2013 (In Russian).
B. L. Milman and Y.A. Gavrilova, Analysis of citation and co-citation in chemical engineering, Scientometrics, vol. 27, pp. 53–74, 1993.
A. J. Seltzer and S. H. Daniel, Co-authorship in economic history and economics: Are we any different?, Explorations in Economic History, vol. 69, pp. 102–109, 2018.
D. E. Chebukov, A. M. Nwohiri, A. A. Pechnikov, and E.A. Znamenskaya, Analysis of Co-Authorship Pattern in Mathematics-Related Fields, Journal of Harbin Engineering University, vol. 44, no. 12, pp. 851–861, 2023.
J. Johal, M. Loukas, R. J. Oskouian and R. S. Tubbs, Political co-authorships in medical science journals, Clinical Anatomy, vol. 30, no. 6, pp. 831–834, 2017. https://doi.org/10.1002/ca.22932.
M. A. Basarab, E. V. Glinskaya, I. P. Ivanov, A. V. Kolesnikov and V. I. Kuzovlev, Study into the Structure of the Scientific Coathorship Graph Using Social Network Analysis, Cybersecurity Issues, vol. 1, no. 19, pp. 31–36, 2017. (In Russian., abstract in English).
M. A. Brandão and M. M. Moro, The strength of co-authorship ties through different topological properties, Journal of the Brazilian Computer Society, vol. 23, no. 5, 2017. https://doi.org/10.1186/s13173-017-0055-x
O. Gerasimova and I. Makarov, Higher School of Economics Co-Authorship Network Study, 2019 2nd International Conference on Computer Applications & Information Security (ICCAIS), Riyadh, Saudi Arabia, May 01-03, 2019, pp. 1–4, https://doi.org/10.1109/CAIS.2019.8769556.
O. I. Ivanov, A. M. Kovalenko, A. V. Kolobov, V. V. Koroleva, A. V. Leonidov, and E. E. Serebryannikova, Topology of the Co-Authorship Graph in the Field of Physics in Russia, Bulletin of the Lebedev Physics Institute, vol. 47, pp. 223–227, 2020. https://doi.org/10.3103/S1068335620080060
S. V. Bredikhin, V. M. Lyapunov, N. G. Scherbakova, "The structure and parameters of the unweighted co-authorship network based on DB REPEC data," Problems of Informatics, vol. 3, no. 52, pp. 56–67, 2021, (In Russian). https://doi.org/0.24412/2073-0667-2021-3-56-67.
A. M. Jaramillo, H.T.P. Williams, N. Perra, R. Menezes, The structure of segregation in co-authorship networks and its impact on scientific production, EPJ Data Science, vol. 12, no. 47, 2023. https://doi.org/10.1140/epjds/s13688-023-00411-8
M.E.J. Newman, The structure of scientific collaboration networks, Proceedings of the National Academy of Sciences of the USA, vol. 98, no. 2, pp. 404–409, 2001.
F. D. Malliaros and M. Vazirgiannis, Clustering and community detection in directed networks: A survey, Physics Reports, vol. 533, no. 4, pp. 95–142, 2013. https://doi.org/10.1016/j.physrep.2013.08.002.
P. Bedi and C. Sharma, Community detection in social networks, Wiley interdisciplinary reviews: Data mining and knowledge discovery, vol. 6, no. 3, pp. 115–135, 2016. https://doi.org/10.1002/widm.1178
J. Zhang, X. Yang, X. Hu and T. Li, Author Cooperation Network in Biology and Chemistry Literature during 2014-2018: Construction and Structural Characteristics, Information, vol. 10, no. 7, 236, 2019. https://doi.org/10.3390/info10070236
M. E. Newman and M. Girvan, Finding and evaluating community structure in networks," Physical Review E, vol. 69, no. 2, 2004. https://link.aps.org/doi/10.1103/PhysRevE.69.026113
E. A. Znamenskaya, A. A. Pechnikov, and D. E. Chebukov, Analysis of co-authorship in mathematical journals of Math-Net.Ru, Proceedings of the 24th Conference on Scientific Services & Internet (SSI-2022), Russia, September 19-22, 2022, pp. 190–202 (In Russian but with English abstract). https://doi.org/10.20948/abrau-2022-5.
Refbacks
- There are currently no refbacks.
Abava Кибербезопасность IT Congress 2024
ISSN: 2307-8162