Comparative analysis of university-government-enterprise co-authorship networks in three scientific domains in the region of Madrid
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AutorOlmeda-Gómez, Carlos; Perianes-Rodríguez, Antonio; Ovalle-Perandones, María Antonia; Moya Anegón, Félix de
University of Sheffield. Department of Information Studies
Olmeda-Gómez, C.; et al. Comparative analysis of university-government-enterprise co-authorship networks in three scientific domains in the region of Madrid. Information Research, 13(3): 352 (2008). [http://hdl.handle.net/10481/32831]
PatrocinadorThis research is supported by grants from Comunidad de Madrid (06/HSE/0166).
Introduction: In an economy geared to innovation and competitiveness in research and development activities, inter-relationships between the university, private enterprise and government are of considerable interest. Networking constitutes a priority strategy to attain this strategic objective and a tool in knowledge-based economies. Method: Drawing from a full inventory of co-authored scientific articles, collaborating networks are defined and analysed with the social network analysis method, using Pajek software and graphed with the Kamada-Kawai algorithm for visualization. Analysis: Scientific production involving intraregional collaboration in the Madrid region is analysed across three subject categories. The data used were taken from the Web of Science for the years 1995-2003. The main indicators of social networking obtained were: density average degree, normalized degree and degree centralization, betweenness centralization, closeness centralization and clustering coefficient. Results: Networking led to a moderate rise in the number of links and participating actors, with more Spanish companies and multi-national subsidiaries in the second period. The largest number of links was recorded for public universities located in the Community of Madrid. Conclusions: The data resulting from the social network analysis conducted provided insight into the structural characteristics of the networks generated and their evolution. The visualization methodology used proved to be highly informative for identifying not only the main actors, but clusters and components as well. The analysis afforded a useful perspective for understanding the dynamics of collaborating networks.