Evolving cohesion metrics of a research network on rare diseases: a longitudinal study over 14 years

Carlos B. Amat () and François Perruchas
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Carlos B. Amat: Institute of Innovation and Knowledge Management (Ingenio), CSIC and Universidad Politécnica de Valencia, Ciudad Politécnica de la Innovación

Scientometrics, 2016, vol. 108, issue 1, No 3, 56 pages

Abstract: Abstract Research collaboration is necessary, rewarding, and beneficial. Cohesion between team members is related to their collective efficiency. To assess collaboration processes and their eventual outcomes, agencies need innovative methods—and social network approaches are emerging as a useful analytical tool. We identified the research output and citation data of a network of 61 research groups formally engaged in publishing rare disease research between 2000 and 2013. We drew the collaboration networks for each year and computed the global and local measures throughout the period. Although global network measures remained steady over the whole period, the local and subgroup metrics revealed a growing cohesion between the teams. Transitivity and density showed little or no variation throughout the period. In contrast the following points indicated an evolution towards greater network cohesion: the emergence of a giant component (which grew from just 30 % to reach 85 % of groups); the decreasing number of communities (following a tripling in the average number of members); the growing number of fully connected subgroups; and increasing average strength. Moreover, assortativity measures reveal that, after an initial period where subject affinity and a common geographical location played some role in favouring the connection between groups, the collaboration was driven in the final stages by other factors and complementarities. The Spanish research network on rare diseases has evolved towards a growing cohesion—as revealed by local and subgroup metrics following social network analysis.

Keywords: Co-authorship networks; Social networks analysis; Longitudinal follow up; Rare diseases; Networked research (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11192-016-1952-z

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