Analysis of Iranian Researchers’ Co-Authorship Networks in Asthma Research: Based on Social Network Analysis

Document Type : Original Article

Authors
Roghaye Khasha 1
Nasrin Taherkhani 2
1 Assistant Professor, Department of Industrial Engineering & Management Systems, Amirkabir University of Technology, Tehran, Iran.
2 Assistant Professor, Department of Information Technology Engineering, Payame Noor University (PNU), Tehran, Iran.
10.48305/him.2025.45208.1294
Abstract
Introduction: Asthma is a health hazard for all societies. Scientific research on asthma can make a significant contribution to offering new therapeutic approaches to improve patients’ conditions. The aim of this study is to examine the structure of Iranian researchers’ co-authorship network in the field of asthma.

Methods: The study was conducted in three stages. In the first stage, PubMed-indexed articles published by Iranian researchers in the field of asthma between 2015 and 2025 were collected. In the second stage, the co-authorship network was drawn based on the extracted information. In the third stage, both macro- and micro-level indicators were used to analyze the authors’ co-authorship networks. NetworkX and Gephi were utilized for analysis.

Results: The results indicate a density of 0.01002, a clustering coefficient of 0.916, a modularity of 0.735, and a network diameter of 14. Based on micro-level indicators, Masjedi had the highest degree centrality and Katz centrality. Masjedi and Mahdaviani had the highest betweenness centrality, Ansarian had the highest eigenvector centrality.

Conclusion: The low density, high clustering coefficient, and high modularity suggest that within this dispersed network, researchers in small, interconnected groups collaborate with one another.

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1. Amaral-Machado, L. et al., (2020). “Use of natural products in asthma treatment”. Evidence‐Based Complementary and Alternative Medicine.2020(1): 1021258.
2. Katz JS, Martin BR. What is research collaboration?. Research policy. 1997 Mar 1;26(1):1-8.
3. Dino H, Yu S, Wan L, Wang M, Zhang K, Guo H, Hussain I. Detecting leaders and key members of scientific teams in co-authorship networks.Computers & Electrical Engineering. 2020 Jul 1;85:106703.
4. Newman ME. The structure of scientific collaboration networks. Proceedings of the national academy of sciences. 2001 Jan 16;98(2):404-9.
5. Higaki A, Uetani T, Ikeda S, Yamaguchi O. Co-authorship network analysis in cardiovascular research utilizing machine learning (2009–2019).International Journal of Medical Informatics. 2020 Nov 1;143:104274.
6. Abbasi A, Chung KS, Hossain L. Egocentric analysis of co-authorship network structure, position and performance. Information Processing & Management. 2012 Jul 1;48(4):671-9.
7. Badar K, Hite JM, Badir YF. Examining the relationship of co-authorship network centrality and gender on academic research performance: the case of chemistry researchers in Pakistan. Scientometrics. 2013 Feb;94(2):755-75.
8. Bailie J, Matous P, Bailie R, Passey ME. Patterns of collaboration and knowledge generated by an Australian rural research centre over 20 years: a co-authorship network analysis. Health Research Policy and Systems. 2023 Aug 30;21(1):87.
9. Alavi M, Lajevardy SA. The Co-authorship Network of Published Articles in Conferences on Web Research Based on Social Network Analysis.International Journal of Web Research. 2020 Dec 1;3(2):9-15.
10. Cimenler O, Reeves KA, Skvoretz J. A regression analysis of researchers’ social network metrics on their citation performance in a college of engineering. Journal of Informetrics. 2014 Jul 1;8(3):667-82.
11. Fonseca BD, Silva MV, Araújo KM, Sampaio RB, Moraes MO. Network analysis for science and technology management: evidence from tuberculosis research in Fiocruz, Brazil. PLoS One. 2017 Aug 8;12(8):e0181870.
12. Fonseca BD, Silva MV, Araújo KM, Sampaio RB, Moraes MO. Network analysis for science and technology management: evidence from tuberculosis research in Fiocruz, Brazil. PLoS One. 2017 Aug 8;12(8):e0181870.
13. Kianmehr S, Mohammadesmaeil S. Thematic Content Analysis and Visualization of Co-Authorship of Scientometrics Journal Articles During 2015-2020s. Journal of healthcare management. 2023: 21;14(1):67-82. [In Persian]
14. Zandian F, Moradian A, Hassanzadeh M. Analyzing scientific collaboration among Iranian medical researchers using social network indicators. Scientometrics Research Journal. 2019 : 21;5(9):99-116.. [In Persian]
15. Knyazev B, De Vries H, Cangea C, Taylor GW, Courville A, Belilovsky E. Graph density-aware losses for novel compositions in scene graph generation. arXiv preprint arXiv:2005.08230. 2020 May 17.
16. Li Y, Shang Y, Yang Y. Clustering coefficients of large networks. Information Sciences. 2017 Mar 1; 382:350-8.
17. Lu Y, Yang W, Zhang Y, Chen Z, Chen J, Xuan Q, Wang Z, Yang X. Understanding the dynamics of dnns using graph modularity. InEuropean Conference on Computer Vision 2022 Oct 23 (pp. 225-242). Cham: Springer Nature Switzerland.
18. Noche Nasar HR, Shams GR, Ghanei Rad MA. Analysis of the social network of co-authorship of internal articles of faculty members in the field
of educational sciences of governmental universities in Tehran. Scientometrics Research Journal. 2022 Sep 23;8(2, Autumn & Winter)):31-52. [In Persian]
19. Zhang Y, Bao Y, Zhao S, Chen J, Tang J. Identifying node importance by combining betweenness centrality and katz centrality. In2015 International Conference on Cloud Computing and Big Data (CCBD) 2015 Nov 4 (pp. 354-357). IEEE.
20. Roshani S, Ghazinoori S, Tabatabaeian SH. A co-authorship network analysis of Iranian researchers in technology policy and management. networks. 2014;18(1):69-89. [In Persian]
21. Erfanmanesh M, Arshadi H. Co-authorship network of institutions in Iranian knowledge and information science papers. Academic Librarianship and Information Research. 2015 Mar 21;49(1):79-99. [In Persian]