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Community Structure and the Evolution of Interdisciplinarity in Slovenia's Scientific Collaboration Network

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Journal PLoS One
Date 2014 Apr 15
PMID 24728345
Citations 13
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Abstract

Interaction among the scientific disciplines is of vital importance in modern science. Focusing on the case of Slovenia, we study the dynamics of interdisciplinary sciences from 1960 to 2010. Our approach relies on quantifying the interdisciplinarity of research communities detected in the coauthorship network of Slovenian scientists over time. Examining the evolution of the community structure, we find that the frequency of interdisciplinary research is only proportional with the overall growth of the network. Although marginal improvements in favor of interdisciplinarity are inferable during the 70s and 80s, the overall trends during the past 20 years are constant and indicative of stalemate. We conclude that the flow of knowledge between different fields of research in Slovenia is in need of further stimulation.

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References
1.
Chessa A, Morescalchi A, Pammolli F, Penner O, Petersen A, Riccaboni M . European policy. Is Europe evolving toward an integrated research area?. Science. 2013; 339(6120):650-1. DOI: 10.1126/science.1227970. View

2.
Palla G, Derenyi I, Farkas I, Vicsek T . Uncovering the overlapping community structure of complex networks in nature and society. Nature. 2005; 435(7043):814-8. DOI: 10.1038/nature03607. View

3.
Girvan M, Newman M . Community structure in social and biological networks. Proc Natl Acad Sci U S A. 2002; 99(12):7821-6. PMC: 122977. DOI: 10.1073/pnas.122653799. View

4.
Newman M, Girvan M . Finding and evaluating community structure in networks. Phys Rev E Stat Nonlin Soft Matter Phys. 2004; 69(2 Pt 2):026113. DOI: 10.1103/PhysRevE.69.026113. View

5.
Fortunato S, Barthelemy M . Resolution limit in community detection. Proc Natl Acad Sci U S A. 2006; 104(1):36-41. PMC: 1765466. DOI: 10.1073/pnas.0605965104. View