Libraries at University of Nebraska-Lincoln


Date of this Version

Winter 1-7-2018



1. Badar, K., J. Hite, and Y. Badir, Examining the relationship of co-authorship network centrality and gender on academic research performance: the case of chemistry researchers in Pakistan. Scientometrics, 2012. 94(2): p. 755-775.

2. Hudson, J., Trends in multi-authored papers in economics. Journal of Economics Perspectives, 1996. 10(3): p. 153–158.

3. Cheong, F. and B.J. Corbitt. A Social network analysis of the co-authorship network of the Pacific Asia Conference on Information System (PACIS) from 1993 to 2008. in Pacific Asia Conference on Information Systems 2009.

4. Gossart, C. and M. Özman, Co-authorship networks in social sciences: The case of Turkey. Scientometrics, 2009. 78(2): p. 323-345.

5. Nagpaul, P., Visualizing cooperation networks of elite institutions in India. Scientometrics, 2002. 54(2): p. 213-228.

6. Valderrama-Zurián, J.C., et al., Coauthorship networks and institutional collaboration in Revista Española de Cardiología publications. Revista Española de Cardiología (English Edition), 2007. 60(2): p. 117-130.

7. Ding, Y., Scientific collaboration and endorsement: Network analysis of coauthorship and citation networks. Journal of informetrics, 2011. 5(1): p. 187-203.

8. Liu, X., et al., Co-authorship networks in the digital library research community. Information Processing and Management: an International Journal, 2005. 41(6): p. 1462–1480.

9. Otte, E. and R. Rousseau, Social network analysis: a powerful strategy, also for the information sciences. Journal of Information Science 2002. 28(6): p. 441-453.

10. Abbasi, A. and J. Altmann. On the correlation between research performance and social network analysis measures applied to research collaboration

networks. in 44th annual Hawaii international conference on system sciences. 2011. Waikoloa, HI: IEEE.

11. Wang, T., et al., Collaborations Patterns and Productivity Analysis for IEEE T-ITS Between 2010 and 2013. Intelligent Transportation Systems, IEEE Transactions on, 2014. 15(6): p. 2360-2367.

12. Prell, C., Social network analysis: History, theory and methodology. 2011, London, England: Sage Publications Ltd.

13. Abbasi, A., K.S.K. Chung, and L. Hossain, Egocentric analysis of co-authorship network structure, position and performance. Information Processing & Management, 2011. 48(4): p. 671–679.

14. Zemljic, B. and V. Hlebec, Reliability of measures of centrality and prominence. Social Networks, 2005. 27: p. 73–88.

15. Bonacich, P., Power and centrality: A family of measures. The American Journal of Sociology, 1987. 92(5): p. 1170–1182.

16. Bozeman, B. and J. Youtie, Trouble in Paradise: Problems in Academic Research Co-authoring. Science and Engineering Ethics, 2016. 22(6): p. 1717-1743.

17. Kumar, S. and K. Ratnavelu, Perceptions of scholars in the field of economics on co-authorship associations: Evidence from an international survey. PLoS ONE, 2016. 11(6).

18. McConnell, D., Narrative self-constitution and vulnerability to co-authoring. Theoretical Medicine and Bioethics, 2016. 37(1): p. 29-43.

19. Ludvigsson, J., What responsibility does co-authors have? Läkartidningen, 2016. 113.

20. Bender, M.E., et al., Using Co-authorship Networks to Map and Analyse Global Neglected Tropical Disease Research with an Affiliation to Germany. PLoS Neglected Tropical Diseases, 2015. 9(12).

21. Mayrose, I. and S. Freilich, The interplay between scientific overlap and cooperation and the resulting gain in co-authorship interactions. PLoS ONE, 2015. 10(9).

22. Bader, G.D., et al., Social Network: A Cytoscape app for visualizing co-authorship networks. F1000Research, 2015. 4.

23. Lancaster, F.W. and M.J. Joncich, The measurement and evaluation of library services. 1977, Washington: Information Resources Press. xii, 395 p.

24. Albert, R. and A.L. Barabasi, Statistical mechanics of complex networks. Review of Modern Physics, 2002. 74(1): p. 47-97.

25. Borgatti, S.P., M.G. Everett, and L.C. Freeman, Ucinet for Windows: Software for social network analysis. 2002.

26. Persson, O., Bibexcel. A tool-box programme for bibliometric analysis. 2008.

27. Reuters, T., Histcite. 2014.

28. Barrios, M., et al., A bibliometric study of psychological research on tourism. Scientometrics, 2008. 77(3): p. 453-467.

29. Nikzad, M., H.R. Jamali, and N. Hariri, Patterns of Iranian co-authorship networks in social sciences: A comparative study. Library & Information Science Research, 2011. 33(4): p. 313-319.

30. Acedo, F.J., et al., Co-authorship in management and organizational Studies: An empirical and network analysis. Journal of Management Studies, 2006. 43(5): p. 957-983.

31. Newman, M.E., S.H. Strogatz, and D.J. Watts, Random graphs with arbitrary degree distributions and their applications. Physical review E, 2001. 64(2): p. 026118.

32. Watts, D.J. and S.H. Strogatz, Collective dynamics of ‘small-world’networks. nature, 1998. 393(6684): p. 440-442.

33. Watts, D.J., Small Worlds: The Dynamics of Networks between Order and Randomness. 2003: Princeton University Press.

34. Newman, M.E.J., Coauthorship networks and patterns of scientific collaboration. PNAS, 2004. 101(Suppl_1): p. 5200–5205.

35. Newman, M.E., Who is the best connected scientist? A study of scientific coauthorship networks, in Complex networks. 2004, Springer. p. 337-370.

36. Yan, E. and Y. Ding, Applying centrality measures to impact analysis: A coauthorship network analysis. Journal of the American Society for Information Science and Technology, 2009. 60(10): p. 2107-2118.

37. Barabási, A.-L. and R. Albert, Emergence of scaling in random networks. science, 1999. 286(5439): p. 509-512.

38. Egghe, L. and R. Rousseau, Average and global impact of a set of journals. Scientometrics, 1996. 36(1): p. 97-107.

39. Abbasi, A., L. Hossain, and L. Leydesdorff, Betweenness centrality as a driver of preferential attachment in the evolution of research collaboration networks. Journal of Informetrics, 2012. 6(3): p. 403-412.

40. Network analysis: Methodological foundations. Lecture Notes in Computer Science, ed. U. Brandes and T. Erlebach. Vol. 3418. 2005: Springer.


Ali Sadatmoosavi is corresponding Author



Introduction: The purpose of this research is to evaluate the co-authorship network of researchers of Kerman University of Medical Sciences. This assessment includes a look at the co-authorship, patterns of co-writing, researchers' outputs, authors ranking, map drawing of the co-authorship network, comparing the network of co-writing of the medical field with other domains, main component and key researchers, review The fit of the network of the co-writing of medical researchers with the small world theory, as well as person-centered indicators such as degree centrality, between centrality, closeness centrality Eigenvector, vector centrality, beta centrality, and interstitial centrality. Method: This research was carried out using scientific methods and network analysis techniques. The statistical population of this research is all articles of the faculty members and other researchers of Kerman University of Medical Sciences, indexed at the ISI database (the Science of Science Web site) during the period from 1978 to 2015, which consists of 1710 articles. The data were analyzed by Bibexcel, Histcite and Net drive softwares after pre-processing. Findings: The review of the articles showed that the pattern of four and five writers had the highest percentage of the co-written articles. The co-authorship network of this university is lower un an index such as the number of papers for each author from many other areas, and in the index of authors for each article is higher than most of the areas. The density index of this network is 0/003, its clustering coefficient is 0/64 and the percentage of the co-written articles in companion with the single-written articles is 97%. The researchers of this university have a high degree of collaboration in writing their articles, Iran University of Medical Sciences , Shahid Bahonar Kerman University and Shahid Beheshti University of Medical Sciences, and the United States, Australia and England have the most scientific cooperation with Kerman University of Medical Sciences. Studies show that most of the articles published at Kerman University of Medical Sciences have been produced by a small number of researchers of this university, and the ratio of national-to-international collaboration at this university has been. 2/9. Conclusion: The co-authorship network of the researchers of this university is characterized by the average length trajectory and relatively high clustering coefficient, which is a small world network. The study of the distribution of the degree centrality of the central and key researchers of the network shows that the principle of "success breeds success", which was proposed by Age and Rousseau in 1996, is also valid in the surveyed network, and the researchers with high centrality play a very important role in the development and The evolution of co-writing networks



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