Analysing educational scientific collaboration through multilayer networks: patterns, impact and network generation model

Abstract

Abstract Scientific collaboration is an essential aspect of the educational field, offering significant reference value in resource sharing and policy making. With the increasing diversity and inter-disciplinary nature of educational research, understanding scientific collaboration within and between various subfields is crucial for its development. This article employs topic modelling to extract educational research topics from publication metadata obtained from 265 scientific journals spanning the period from 2000 to 2021. We construct a multilayer co-authorship network whose layers represent the scientific collaboration in different subfields. The topological properties of the layers are compared, highlighting the differences and common features of scientific collaboration between hot and cold topics, with the main difference being the existence of a significant largest connected component. Further, the cross-layer cooperation behaviour is investigated by studying the structural measures of the multilayer network and reveals authors’ inclination to collaborate with familiar individuals in familiar subfields. Moreover, the relationships between the authors’ features on the network topology and their H-index are investigated. The results emphasize the significance of establishing a clear research direction to enhance the academic reputation of authors, as well as the importance of cross-layer collaboration for expanding their research groups. Finally, based on the above results, we propose a multilayer network generation model of scientific collaboration and verify its validity.