110651 20251113150202.0 doi 10.1140/epjds/s13688-021-00282-x sideral 126807 ART-2021-126807 eng Tuninetti, M. Prediction of new scientific collaborations through multiplex networks 2021 Access copy available to the general public Unrestricted 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). info:eu-repo/grantAgreement/ES/DGA-FEDER/E36-20R info:eu-repo/grantAgreement/ES/MINECO-FEDER/FIS2017-87519-P info:eu-repo/semantics/openAccess by https://creativecommons.org/licenses/by/4.0/deed.es 3.63 2021 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS 17 / 108 = 0.157 2021 Q1 T1 SOCIAL SCIENCES, MATHEMATICAL METHODS 11 / 53 = 0.208 2021 Q1 T1 1.071 2021 Computer Science Applications 2021 Q1 Computational Mathematics 2021 Q1 6.5 2021 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Aleta, A. (orcid)0000-0002-1192-8707 Paolotti, D. Moreno, Y. Universidad de Zaragoza (orcid)0000-0002-0895-1893 Starnini, M. 2004 405 Universidad de Zaragoza Dpto. Física Teórica Área Física Teórica 10 (2021), 25 [10 pp.] EPJ data sci. EPJ Data Science 2193-1127 1503612 http://zaguan.unizar.es/record/110651/files/texto_completo.pdf Versión publicada 2283823 http://zaguan.unizar.es/record/110651/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:110651 articulos driver 2025-11-13-15:00:40 ARTICLE