Prediction of new scientific collaborations through multiplex networks

EPJ Data Science

Daniela Paolotti¹  Michele Starnini¹  Alberto Aleta¹  Marta Tuninetti¹  Yamir Moreno² 

[1] ISI Foundation, via Chisola 5, 10126, Torino, Italy;ISI Foundation, via Chisola 5, 10126, Torino, Italy;Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, 50018, Zaragoza, Spain;Department of Theoretical Physics, University of Zaragoza, 50018, Zaragoza, Spain;

关键词: Scientific collaboration networks;    Computational social science;    Link prediction;   

DOI  :  10.1140/epjds/s13688-021-00282-x

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY