ISSN: 2222-6990
Open access
Centrality is an essential concept in network sciences, which evolved from graph theory. The use of centrality measures allows the identification of the dominant elements in a network. The definition of centrality and the first associated interventions were developed for social network analysis and have since been applied to other fields. However, as the number of publications has steadily increased over the years, it is becoming increasingly difficult to maintain the growing volume of scholarly publications. Research and publications are extensively developed and have no proper record. Therefore, this study executes a bibliometric analysis on centrality measures, as well as its popular method: Betweenness Centrality, Closeness Centrality, Eigenvector Centrality, and Degree Centrality, by reviewing a database that makes its research, awareness, global evolution, and potential trend lines available. Data were obtained from the Scopus database arranging from 2014 to 2024. The bibliometric analysis provides a valuable overview of the evolution of centrality measures in terms of the number of publications, most cited publications, most significant collaboration countries, and current trends in centrality. This study of bibliometric analysis on centrality measures have not been carried out yet. Therefore, prediction on the hotspots and current trends within certain research areas and methods would give researchers insight into further its development.
Ameer, F., Hanif, M. K., Talib, R., Sarwar, M. U., Khan, Z., Zulfiqar, K., & Riasat, A. (2019). Techniques, tools and applications of graph analytic. International Journal of Advanced Computer Science and Applications, 10(4), 354–363. https://doi.org/10.14569/ijacsa.2019.0100443
Antiqueira, L., & Zhao, L. (2014). Spatial Neural Networks and their Functional Samples: Similarities and Differences. i, 1–10.
Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
Ben-daya, M., Hassini, E., & Bahroun, Z. (2019). Internet of things and supply chain management?: a literature review. 7543. https://doi.org/10.1080/00207543.2017.1402140
Boldi, P., & Vigna, S. (2014). Axioms for centrality. Internet Mathematics, 10(3–4), 222–262. https://doi.org/10.1080/15427951.2013.865686
Fariduddin Mukhtar, M., Abas, Z. A., Hamzah, A., Rasib, A., Haryanti, S., Anuar, H., Hafizah, N., Zaki, M., Abidin, Z. Z., Asmai, S. A., Fadzli, A., & Rahman, A. (2023). GLOBAL STRUCTURE MODEL MODIFICATION TO IMPROVE INFLUENTIAL NODE DETECTION. 18(3). www.arpnjournals.com
Glynatsi, N. E., & Knight, V. A. (2021). A bibliometric study of research topics, collaboration, and centrality in the iterated prisoner’s dilemma. Humanities and Social Sciences Communications, 8(1). https://doi.org/10.1057/s41599-021-00718-9
Gómez, S. (2019). Centrality in Networks: Finding the Most Important Nodes. In Business and Consumer Analytics: New Ideas. https://doi.org/10.1007/978-3-030-06222-4_8
Hamilton, W. L., Ying, R., & Leskovec, J. (2017). Representation Learning on Graphs: Methods and Applications. ArXiv, 1–23.
Hassani, H., Razavi-Far, R., Saif, M., & Capolino, G.-A. (2020). Regression Models With Graph-Regularization Learning Algorithms for Accurate Fault Location in Smart Grids. IEEE Systems Journal, 1–12. https://doi.org/10.1109/jsyst.2020.3001932
Hussin, M. S. F., Serah, A. M., Azlan, K. A., Abdullah, H. Z., Idris, M. I., Ghazali, I., Shariff,
A. H. M., Huda, N., & Zakaria, A. A. (2021). A bibliometric analysis of the global trend of using alginate, gelatine, and hydroxyapatite for bone tissue regeneration applications. In Polymers (Vol. 13, Issue 4, pp. 1–17). MDPI AG. https://doi.org/10.3390/polym13040647
Lund, B. D., & Maurya, S. K. (2020). The relationship between highly-cited papers and the frequency of citations to other papers within-issue among three top information science journals. Scientometrics, 125(3), 2491–2504. https://doi.org/10.1007/s11192-020-03720-1
Meghanathan, N. (2018). ?-Space for real-world networks: A correlation analysis of decay centrality vs. degree centrality and closeness centrality. Journal of King Saud University - Computer and Information Sciences, 30(3), 391–403.
https://doi.org/10.1016/j.jksuci.2017.04.006
Meo, S. A., Al-Dossary, O. M., Samdani, M. S., & Sami, W. (2021). Bibliometric Analysis, Progress and Prospects of Journal of King Saud University-Science at Global Level. Journal of King Saud University - Science, 33(4), 101440.
https://doi.org/10.1016/j.jksus.2021.101440
Mukhtar, M. F., Abas, Z. A., Rasib, A. H. A., Anuar, S. H. H., Zaki, N. H. M., Rahman, A. F. N. A., Abidin, Z. Z., & Shibghatullah, A. S. (2022). Identifying Influential Nodes with Centrality Indices Combinations using Symbolic Regressions. International Journal of Advanced Computer Science and Applications, 13(5).
https://doi.org/10.14569/IJACSA.2022.0130570
Mumu, J. R., Tahmid, T., & Azad, M. A. K. (2021). Job satisfaction and intention to quit: A bibliometric review of work-family conflict and research agenda. Applied Nursing Research, 59(March 2020), 151334. https://doi.org/10.1016/j.apnr.2020.151334
Radhakrishnan, S., Erbis, S., Isaacs, J. A., & Kamarthi, S. (2017). Correction: Novel keyword co-occurrence network-based methods to foster systematic reviews of scientific literature (PLoS ONE (2017) 12:3 (e0172778) DOI: 10.1371/journal.pone.0172778). PLoS ONE, 12(9), 1–16. https://doi.org/10.1371/journal.pone.0185771
Saeed, S., Yousuf, S., Khan, F., & Rajput, Q. (2021). Social network analysis of Hadith narrators. Journal of King Saud University - Computer and Information Sciences, xxxx. https://doi.org/10.1016/j.jksuci.2021.01.019
Xu, G., Meng, X., Guan, J., Xing, Y., Feng, Z., & Hai, Y. (2021). Systematic review of intervertebral disc repair: a bibliometric analysis of the 100 most-cited articles. Journal of Orthopaedic Surgery and Research, 16(1), 1–12. https://doi.org/10.1186/s13018-021-02303-x
Mukhtar, M. F., Anuar, S. H. H., Abas, Z. A., Hussin, M. S. F., & Sulaiman, S. A. (2024). Bibliometric Analysis of the Global Trend in Centrality Measures. International Journal of Academic Research in Business and Social Sciences, 14(9), 1078–1098.
Copyright: © 2024 The Author(s)
Published by HRMARS (www.hrmars.com)
This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at: http://creativecommons.org/licences/by/4.0/legalcode