  Dynamic Graph Neural Networks (DGNNs) have emerged as the predominant
approach for processing dynamic graph-structured data. However, the influence
of temporal information on model performance and robustness remains
insufficiently explored, particularly regarding how models address prediction
tasks with different time granularities. In this paper, we explore the impact
of time granularity when training DGNNs on dynamic graphs through extensive
experiments. We examine graphs derived from various domains and compare three
different DGNNs to the baseline model across four varied time granularities. We
mainly consider the interplay between time granularities, model architectures,
and negative sampling strategies to obtain general conclusions. Our results
reveal that a sophisticated memory mechanism and proper time granularity are
crucial for a DGNN to deliver competitive and robust performance in the dynamic
link prediction task. We also discuss drawbacks in considered models and
datasets and propose promising directions for future research on the time
granularity of temporal graphs.
