- Abstract:
-
Scientific collaboration networks are an important component of scientific output and contribute significantly to expanding our knowledge and to the economy and gross domestic product of nations. Here we examine a dataset from the Mendeley scientific collaboration network. We analyze this data using a combination of machine learning techniques and dynamical models. We find interesting clusters of countries with different characteristics of collaboration. Some of these clusters are dominated b...
Expand abstract - Publication status:
- Published
- Peer review status:
- Peer reviewed
- Version:
- Accepted manuscript
- Files:
-
-
(pdf, 486.2KB)
-
- Publisher copy:
- 10.1007/978-3-319-45901-1_7
-
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- Publisher:
- Springer International Publishing Publisher's website
- Pages:
- 85-90
- Series:
- Springer Proceedings in Complexity
- Publication date:
- 2016-12-26
- Acceptance date:
- 2016-12-01
- DOI:
- ISSN:
-
2213-8684
- Pubs id:
-
pubs:668957
- URN:
-
uri:e29e24b2-339e-4d76-95ab-ce38e8b418a4
- UUID:
-
uuid:e29e24b2-339e-4d76-95ab-ce38e8b418a4
- Local pid:
- pubs:668957
- ISBN:
- 978-3-319-45901-1
- Copyright holder:
- © Springer International Publishing Switzerland 2017
- Copyright date:
- 2016
- Notes:
- This is the author accepted manuscript following peer review version of the article. The final version is available online from Springer International Publishing at: 10.1007/978-3-319-45901-1_7
Conference item
Analysis of a planetary scale scientific collaboration dataset reveals novel patterns
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