Prediction of new scientific collaborations through multiplex networks

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.

Automated summary

The establishment of collaborations among scientists is crucial for fostering scientific environment and novel discoveries. This study leverages publication records to reconstruct multiplex networks and improve prediction of new scientific collaborations, comparing link prediction algorithms. The research paves the way for a deeper understanding of dynamics driving scientific collaborations and validates a new algorithm for link prediction in multiplex networks.