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Prediction of new scientific collaborations through multiplex networks

Authors
  • Tuninetti, Marta1
  • Aleta, Alberto1
  • Paolotti, Daniela1
  • Moreno, Yamir1, 2, 2
  • Starnini, Michele1
  • 1 ISI Foundation, via Chisola 5, Torino, 10126, Italy , Torino (Italy)
  • 2 University of Zaragoza, Zaragoza, 50018, Spain , Zaragoza (Spain)
Type
Published Article
Journal
EPJ Data Science
Publisher
Springer Berlin Heidelberg
Publication Date
May 13, 2021
Volume
10
Issue
1
Identifiers
DOI: 10.1140/epjds/s13688-021-00282-x
Source
Springer Nature
Keywords
License
Green

Abstract

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.

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