  Temporal graphs have gained increasing importance due to their ability to
model dynamically evolving relationships. These graphs can be represented
through either a stream of edge events or a sequence of graph snapshots. Until
now, the development of machine learning methods for both types has occurred
largely in isolation, resulting in limited experimental comparison and
theoretical crosspollination between the two. In this paper, we introduce
Unified Temporal Graph (UTG), a framework that unifies snapshot-based and
event-based machine learning models under a single umbrella, enabling models
developed for one representation to be applied effectively to datasets of the
other. We also propose a novel UTG training procedure to boost the performance
of snapshot-based models in the streaming setting. We comprehensively evaluate
both snapshot and event-based models across both types of temporal graphs on
the temporal link prediction task. Our main findings are threefold: first, when
combined with UTG training, snapshotbased models can perform competitively with
event-based models such as TGN and GraphMixer even on event datasets. Second,
snapshot-based models are at least an order of magnitude faster than most
event-based models during inference. Third, while event-based methods such as
NAT and DyGFormer outperforms snapshotbased methods on both types of temporal
graphs, this is because they leverage joint neighborhood structural features
thus emphasizing the potential to incorporate these features into
snapshot-based models as well. These findings highlight the importance of
comparing model architectures independent of the data format and suggest the
potential of combining the efficiency of snapshot-based models with the
performance of event-based models in the future.
