Prediction of new scientific collaborations through multiplex networks
Resumen: 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. © 2021, The Author(s).
Idioma: Inglés
DOI: 10.1140/epjds/s13688-021-00282-x
Año: 2021
Publicado en: EPJ Data Science 10, 1 (2021), 25 [10 pp]
ISSN: 2193-1127

Factor impacto JCR: 3.63 (2021)
Categ. JCR: MATHEMATICS, INTERDISCIPLINARY APPLICATIONS rank: 17 / 108 = 0.157 (2021) - Q1 - T1
Categ. JCR: SOCIAL SCIENCES, MATHEMATICAL METHODS rank: 11 / 53 = 0.208 (2021) - Q1 - T1

Factor impacto CITESCORE: 6.5 - Mathematics (Q1) - Computer Science (Q1)

Factor impacto SCIMAGO: 1.071 - Computer Science Applications (Q1) - Computational Mathematics (Q1)

Financiación: info:eu-repo/grantAgreement/ES/DGA-FEDER/E36-20R
Financiación: info:eu-repo/grantAgreement/ES/MINECO-FEDER/FIS2017-87519-P
Tipo y forma: Artículo (Versión definitiva)
Área (Departamento): Área Física Teórica (Dpto. Física Teórica)

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