Unraveling higher-order dynamics in collaboration networks

Abstract: The interactions between individuals play a pivotal role in shaping the structure and dynamics of social systems. Complex network models have proven invaluable in uncovering the underlying mechanisms that govern the formation and evolution of these systems. However, conventional network representations primarily emphasize pairwise interactions, represented as edges in the network. In reality, many social interactions occur within groups rather than individual pairs. To capture this crucial aspect, higher-order network representations come into play, especially to describe those complex systems that are inherently composed of agents interacting with group dynamics. Despite recent research advancements in exploring temporal higher-order networks in various systems, our understanding of collaboration networks remains limited. Specifically, there is a lack of knowledge regarding the patterns of group interactions within scientific collaborations. How do groups form and evolve in this context? In this study, we aim to delve into the temporal properties of groups within collaboration networks. Our investigation focuses on uncovering the mechanisms that govern the global, group, and individual-level dynamics, shedding light on how individuals collaborate and how groups form and disband over time. By studying these temporal patterns, we take a significant stride forward in comprehending the intricate dynamics of higher-order interactions within human collaboration systems.