000110651 001__ 110651
000110651 005__ 20230519145520.0
000110651 0247_ $$2doi$$a10.1140/epjds/s13688-021-00282-x
000110651 0248_ $$2sideral$$a126807
000110651 037__ $$aART-2021-126807
000110651 041__ $$aeng
000110651 100__ $$aTuninetti, M.
000110651 245__ $$aPrediction of new scientific collaborations through multiplex networks
000110651 260__ $$c2021
000110651 5060_ $$aAccess copy available to the general public$$fUnrestricted
000110651 5203_ $$aThe 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).
000110651 536__ $$9info:eu-repo/grantAgreement/ES/DGA-FEDER/E36-20R$$9info:eu-repo/grantAgreement/ES/MINECO-FEDER/FIS2017-87519-P
000110651 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000110651 590__ $$a3.63$$b2021
000110651 592__ $$a1.071$$b2021
000110651 594__ $$a6.5$$b2021
000110651 591__ $$aMATHEMATICS, INTERDISCIPLINARY APPLICATIONS$$b17 / 108 = 0.157$$c2021$$dQ1$$eT1
000110651 593__ $$aComputer Science Applications$$c2021$$dQ1
000110651 591__ $$aSOCIAL SCIENCES, MATHEMATICAL METHODS$$b11 / 53 = 0.208$$c2021$$dQ1$$eT1
000110651 593__ $$aComputational Mathematics$$c2021$$dQ1
000110651 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000110651 700__ $$aAleta, A.
000110651 700__ $$aPaolotti, D.
000110651 700__ $$0(orcid)0000-0002-0895-1893$$aMoreno, Y.$$uUniversidad de Zaragoza
000110651 700__ $$aStarnini, M.
000110651 7102_ $$12004$$2405$$aUniversidad de Zaragoza$$bDpto. Física Teórica$$cÁrea Física Teórica
000110651 773__ $$g10, 1 (2021), 25 [10 pp]$$pEPJ data sci.$$tEPJ Data Science$$x2193-1127
000110651 8564_ $$s1503612$$uhttps://zaguan.unizar.es/record/110651/files/texto_completo.pdf$$yVersión publicada
000110651 8564_ $$s2283823$$uhttps://zaguan.unizar.es/record/110651/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000110651 909CO $$ooai:zaguan.unizar.es:110651$$particulos$$pdriver
000110651 951__ $$a2023-05-18-15:22:26
000110651 980__ $$aARTICLE