EXPLORING THE STRUCTURE AND EVOLUTION OF THE NOVOSIBIRSK BIOMEDICAL CO-AUTHORSHIP NETWORK
Abstract
The interaction diversity within the communities of living matter, from bacterial colonies to human societies, makes them inherently more complex than ensembles of particles in inanimate nature. Co-authorship networks are a particular case of intra- and inter-group social interactions. In this paper, we analyze the Novosibirsk biomedical scientific community as an example of such a network. Using the PubMed database, we have built a community network and calculated its statistics. The distribution of organizations by scientific activity has a fat tail and obeys the Pareto principle: 83% of publications and 75% of authors belong to the 20% of the most active organizations. A comparison of their networks shows that networks of the universities have a more pronounced core rather than those of research institutions. We have plotted the “demographic” structure of currently active authors and found out two facts: (1) an abundance of authors with short “publication experience” and (2) a deficit of authors whose first publication is dated back to 1991-1997. In general, the network dynamics is non-steady, and the activity tends to increase.
About the Authors
I. I. Titov
Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia
Novosibirsk National Research State University, Novosibirsk, Russia
Russian Federation
Russian Federation
A. A. Blinov
Novosibirsk National Research State University, Novosibirsk, Russia
Russian Federation
Russian Federation
References
1. Титов И.И., Блинов А.А., Рудниченко К.А., Крутов П.В., Казанцев А.Л., Куликов А.И. NETINFERENCE: набор программ для анализа структуры и динамики сетей // Вавиловский журнал генетики и селекции. 2013. Т. 17. № 4/1. С. 615–619.
2. Gazni A., Didegah F. Investigating different types of research collaboration and citation impact: a case study of Harvard University’s publications // Scientometrics. 2011. V. 87. Nо. 2. P. 251–265.
3. Lee S., Bozeman B. The impact of research collaboration on scientific productivity // Social Studies of Science. 2005. V. 35. Nо. 5. P. 673–702.
4. Lotka A.J. The frequency distribution of scientifi c productivity // J. Wash. Acad. Sci. 1926. V. 16. Nо. 12. P. 317–324.
5. Newman M.E.J. The structure and functions of complex networks // SIAM review. 2003. V. 45. Nо. 2. P. 167–256.
6. Newman M.E.J. Coauthorship networks and patterns of scientifi c collaboration // PNAS. 2004. V. 101. Nо. S. 1. P. 5200–5205.
7. Sooryamoorthy R. Do types of collaboration change citation? Collaboration and citation patterns of South African science publications // Scientometrics. 2009. V. 81. No. 1. P. 177–193.
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