  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.
