The Co-authorship Network of Published Articles in Conferences on Web Research Based on Social Network Analysis

Document Type : Original Article

Authors

1 Information Technology dept.,Tarbiat Modares University, Tehran, Iran

2 Information Technology dept., Tarbiat Modares University,Tehran, Iran

Abstract

Collaboration in writing scientific articles with the growth of academic exchanges and social interactions of researchers is increasingly expanding. Scientific collaboration gives researchers the opportunity to combine the capabilities and abilities of different scientific and research disciplines, which cannot be done individually. Co-authorship is the most formal manifestation of intellectual collaboration between authors in the production of scientific research. On the other hand, the study of the trend of scientific activities and its dynamics in any specialized field is one of the most important concerns of researchers in that field. In recent years, the use of the social network analysis approach has been proposed as a suitable solution to map the scientific structure of specialized fields and the co-authorship network of researchers. In this research, the papers published in six web research conferences have been analyzed to discover the scientific network and the co-authorship based on the social network analysis approach. The results of the analysis show that in the period, concepts such as social network analysis, Internet of Things, cloud computing, and deep learning have the largest share in articles. Also, based on the number of communities formed, the authors of the conference papers were more inclined to form small scientific groups in the form of universities or research institutes of their respective organizations.

Keywords

Main Subjects

References

  • Soheili, M. Cheshme Sohrabi and S. Atashpaykar, “Co-authorship network analysis of Iranian medical science researchers: A social network analysis,” Caspian Journal Of Scientometrics, vol. 2, pp. 24-32, 2015. [In Persian]
  • Li, D. Zhang, P. Luo, and J. Jiang, “Interpreting the formation of co-author networks via utility analysis”, Information Processing & Management, vol. 53, no. 3, pp. 624-639, 2017.
  • Dino, S. Yu, L. Wan, M. Wang, K. Zhang, H. Guo and I. Hussain, “Detecting leaders and key members of scientific teams in co-authorship networks”, Computers & Electrical Engineering, vol. 85, p. 106703, 2020.
  • Assareh and K. Baba'I, “The co-authorship networks of published Articles in the journal of Psychology of Exceptional Individuals Allameh Tabataba'i University and Journal of Exceptional Children”, Knowledge Studies, vol. 1, no. 1, pp.1-17, 2014.[In Persian]
  • Higaki, T. Uetani, S. Ikeda and O. Yamaguchi, “Co-authorship network analysis in cardiovascular research utilizing machine learning (2009–2019)”, International Journal of Medical Informatics, vol. 143, p. 104274, 2020.
  • De Stefano, V. Fuccella, M. P. Vitale and S. Zaccarin, “The use of different data sources in the analysis of co-authorship networks and scientific performance”, Social Networks, vol. 35, no. 3, pp. 370-381, 2013.
  • keywords of high frequency in six conferences
  • trend of keywords
  • information about the five main communities of the scientific network under study.

Community

#1

#2

#3

#4

#5

Percentage of total network

11.4%

7.72%

5.75%

5.48%

5.21%

No. of nodes

127

86

64

61

58

No. of edges

947

612

435

382

350

Ratio of edge to node

7.456

7.116

6.796

6.262

6.034

Community density

0.118

0.167

0.215

0.208

0.211

Keyword (based on betweenness centrality)

Machine

Learning

(69990)

Internet of Things

(125408)

Social Network

(110949)

Cloud Computing

(89007)

Semantic Web

(37326)
  • Heidari, A. Valipour and B. Bakhtiyari, “Marketing research trend in Iran: An analytical review”, Management Research in Iran, vol. 21, no. 3, pp. 97-119, 2017. [In Persian]
  • Alcaide–Muñoz, M. P. Rodríguez–Bolívar, M. J. Cobo and E. Herrera–Viedma, “Analysing the scientific evolution of e-Government using a science mapping approach”, Government information quarterly, vol. 34, no. 3, pp. 545-555, 2017.
  • Rip, and J. Courtial, “Co-word maps of biotechnology: An example of cognitive scientometrics”, Scientometrics, vol. 6, no. 6, pp. 381-400, 1984.
  • Ahmadi and F. Osareh, “Co-word analysis concept, definition and application”, vol. 28, no. 1, pp. 125-145, 2017. [In Persian]
  • Guns, Y. X. Liu, and D. Mahbuba, “Q-measures and betweenness centrality in a collaboration network: a case study of the field of informetrics”, Scientometrics, vol. 87, no. 1, 133-147, 2011.
  • Furht, Handbook of Social Network Technologies and Applications, Springer, 2010, p. 3.
  • Frank, “Using centrality modeling in network surveys”, Social Networks, vol. 24, no. 4, pp. 385-394, 2002.
  • Fahimifar and F. Sahli, “Co-authorship Network in Scientific Knowledge and Information Science Persian Journals”, Research On Information Science And Public Libraries, vol. 21, pp. 127-151, 2015. [In Persian]
  • Tahmasbi, “Study and map co-authorship network of researchers in tuberculosis and lung disease research center of tehran”, Caspian Journal of Scientometrics, vol. 4, pp. 36-44, 2017. [In Persian]
  • Shams Mourkani and M. A. Ghanei Rad, “social net-work anal-ysis of co-authorship of faculty members in science education based on their foreign articles”, Scientometrics Research Journal, vol. 4, pp. 33-56, 2018. [In Persian]
  • Sattarzadeh, G. Galyani Moghaddam and E. Momeni, “The Analysis of the Structure of Scientific Collaboration Networks in Basic Medical Sciences in the Science Citation Indicator from 1996 to 2013”, Knowledge Studies, vol. 2, no. 6, pp. 1-20, 2016. [In Persian]
  • Jafari, R. Farshid and E. Mostafavi, “Co-authoring Patterns and Subject Trends in Iranian and World Scientific Research in the Field of Information and Knowledge Organization (2001-2020)”, Knowledge Studies, vol. 6, no. 22, pp. 25-54, 2020. [In Persian]
  • Fan, G. Li and R. Law, “Analyzing co-authoring communities of tourism research collaboration”, Tourism Management Perspectives, vol. 33, p. 100607, 2020.
  • A. Xu and V. Chang, “Co-authorship network and the correlation with academic performance”, Internet of Things, vol. 12, p. 100307, 2020.
  • Zhai, Y. Zhai, C. Cong, T. Song, R. Xiang, T. Feng, Z. Liang et al., “Research Progress of Coronavirus Based on Bibliometric Analysis”, International Journal of Environmental Research and Public Health, vol. 17, no. 11, p. 3766, 2020.
  • N. Yan, T. S. Lee and T. P. Lee, “Mapping the intellectual structure of the Internet of Things (IoT) field (2000–2014): A co-word analysis”, Scientometrics, vol. 105, no. 2, pp. 1285-1300, 2015.
  • Piñeiro-Chousa, M. Á. López-Cabarcos, N. M. Romero-Castro and A. M. Pérez-Pico, “Innovation, entrepreneurship and knowledge in the business scientific field: Mapping the research front”, Journal of Business Research, vol. 115, pp. 475-485, 2020.
  • Baziyad, S. Shirazi, S. Hosseini and R. Norouzi, “Mapping the intellectual structure of epidemiology with use of co-word analysis”, Journal of Biostatistics and Epidemiology, vol. 5, no. 3, pp. 210-215, 2019.
  • Shahrabi Farahani, M. Alavi, M. Ghasemi and B. Teimourpour, “Scientific Map of Papers Related to Data Mining in Civilica Database Based on Co-Word Analysis”, International Journal of Web Research, vol. 3, no. 1, pp. 11-18, 2020.

 

Meysam Alavi was born in Hamadan, Iran. He received his B.Sc. and an M.Sc. degree in Computer Engineering - Software in 2005 and 2010 respectively. Currently, he is an M.Sc. student in Information Technology at Tarbiat Modares University. His research interests include medical image processing, machine learning, data mining, social network analysis

Sayed Ali Lajevardy received his B.Sc in Mechanic Engineering, Amirkabir University, Tehran, Iran in 2008 and received his M.Sc degree in Information Technology, Tarbiat Modares University, Tehran, Iran in 2014. He is PHD senior in Information Technology, Tarbiat Modares University from 2017. His research interests include machine learning, bioinformatics and data gathering.

 

 

 

 

 

 

 

International Journal of Web Research
Volume 3, Issue 2
Autumn-Winter
December 2020
Pages 9-15

Files

Share

How to cite

Statistics