Scientometrics

, Volume 85, Issue 1, pp 219–242 | Cite as

A new approach to analyzing patterns of collaboration in co-authorship networks: mesoscopic analysis and interpretation

Article

Abstract

This paper focuses on methods to study patterns of collaboration in co-authorship networks at the mesoscopic level. We combine qualitative methods (participant interviews) with quantitative methods (network analysis) and demonstrate the application and value of our approach in a case study comparing three research fields in chemistry. A mesoscopic level of analysis means that in addition to the basic analytic unit of the individual researcher as node in a co-author network, we base our analysis on the observed modular structure of co-author networks. We interpret the clustering of authors into groups as bibliometric footprints of the basic collective units of knowledge production in a research specialty. We find two types of coauthor-linking patterns between author clusters that we interpret as representing two different forms of cooperative behavior, transfer-type connections due to career migrations or one-off services rendered, and stronger, dedicated inter-group collaboration. Hence the generic coauthor network of a research specialty can be understood as the overlay of two distinct types of cooperative networks between groups of authors publishing in a research specialty. We show how our analytic approach exposes field specific differences in the social organization of research.

Keywords

Network analysis Co-author networks Scientific communication Chemistry 

Notes

Acknowledgments

We are indebted to our field study participants. Further, this research has been made possible through financial support by the National Science Foundation through grants IIS-738543 SGER: Advancing the State of eChemistry, DUE-0840744 NSDL Technical Network Services: A Cyberinfrastructure Platform for STEM Education, and NSF award 0404553. Support also came from Microsoft Corporation for the project ORE-based eChemistry. We are grateful to those that make our work so much more effective by making neat tools and algorithms available on the Web, such as Martin Rosvall (infomap clustering code), Vladimir Batagelj and Andrej Mrvar (pajek), Michael Weseman (plot), and Peter Mcaster (OmniGraffle extensions for pie charts).

Supplementary material

11192_2010_224_MOESM1_ESM.pdf (433 kb)
Supplementary material 1 (PDF 433 kb)
11192_2010_224_MOESM2_ESM.csv (162 kb)
Supplementary material 2 (CSV 162 kb)
11192_2010_224_MOESM3_ESM.zip (1.4 mb)
Supplementary material 1 (ZIP 1,429 kb)

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Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2010

Authors and Affiliations

  1. 1.Computer and Information ScienceCornell UniversityIthacaUSA

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