Mixing Patterns in Interdisciplinary Co-Authorship Networks at Multiple Scales

There are inherent challenges to interdisciplinary research collaboration, such as bridging cognitive gaps and balancing transaction costs with collaborative benefits. This raises the question: Does interdisciplinary research collaboration necessarily result in disciplinary diversity among collaborators? We aim to explore this question by assessing collaborative preferences in interdisciplinary research at multiple scales through the examinination of disciplinary mixing patterns at the individual, dyadic, and team level in a coauthor network from the field of artificial intelligence in education, an emerging interdisciplinary area. Our key finding is that disciplinary diversity is reflected by diverse research experiences of individual researchers rather than diversity within pairs or groups of researchers. We also examine intergroup mixing by applying a novel approach to classify the active and non-active researchers in the collaboration network based on participation in multiple teams. We find a significant difference in indicators of academic performance and experience between the clusters of active and non-active researchers, suggesting intergroup mixing as a key factor in academic success. Our results shed light on the nature of team formation in interdisciplinary research, as well as highlight the importance of interdisciplinary training.