  Dynamic graph learning has gained significant attention as it offers a
powerful means to model intricate interactions among entities across various
real-world and scientific domains. Notably, graphs serve as effective
representations for diverse networks such as transportation, brain, social, and
internet networks. Furthermore, the rapid advancements in machine learning have
expanded the scope of dynamic graph applications beyond the aforementioned
domains. In this paper, we present a review of lesser-explored applications of
dynamic graph learning. This study revealed the potential of machine learning
on dynamic graphs in addressing challenges across diverse domains, including
those with limited levels of association with the field.
