https://doi.org/10.1140/epjds/s13688-021-00282-x
Regular Article
Prediction of new scientific collaborations through multiplex networks
1
ISI Foundation, via Chisola 5, 10126, Torino, Italy
2
Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, 50018, Zaragoza, Spain
3
Department of Theoretical Physics, University of Zaragoza, 50018, Zaragoza, Spain
Received:
26
January
2021
Accepted:
4
May
2021
Published online:
13
May
2021
The establishment of new collaborations among scientists fertilizes the scientific environment, fostering novel discoveries. Understanding the dynamics driving the development of scientific collaborations is thus crucial to characterize the structure and evolution of science. In this work, we leverage the information included in publication records and reconstruct a categorical multiplex networks to improve the prediction of new scientific collaborations. Specifically, we merge different bibliographic sources to quantify the prediction potential of scientific credit, represented by citations, and common interests, measured by the usage of common keywords. We compare several link prediction algorithms based on different dyadic and triadic interactions among scientists, including a recently proposed metric that fully exploits the multiplex representation of scientific networks. Our work paves the way for a deeper understanding of the dynamics driving scientific collaborations, and validates a new algorithm that can be readily applied to link prediction in systems represented as multiplex networks.
Key words: Scientific collaboration networks / Computational social science / Link prediction
Supplementary Information The online version contains supplementary material available at https://doi.org/10.1140/epjds/s13688-021-00282-x.
Marta Tuninetti and Alberto Aleta contributed equally to this work.
© The Author(s) 2021
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