  In recent years, the prevalent online services generate a sheer volume of
user activity data. Service providers collect these data in order to perform
client behavior analysis, and offer better and more customized services.
Majority of these data can be modeled and stored as graph, such as the social
graph in Facebook, user-video interaction graph in Youtube. These graphs need
to evolve over time to capture the dynamics in the real world, leading to the
invention of dynamic graphs. However, the temporal information embedded in the
dynamic graphs brings new challenges in analyzing and deploying them. Events
staleness, temporal information learning and explicit time dimension usage are
some example challenges in dynamic graph learning. In order to offer a
convenient reference to both the industry and academia, this survey presents
the Three Stages Recurrent Temporal Learning Framework based on dynamic graph
evolution theories, so as to interpret the learning of temporal information
with a generalized framework. Under this framework, this survey categories and
reviews different learnable encoder-decoder architectures for supervised
dynamic graph learning. We believe that this survey could supply useful
guidelines to researchers and engineers in finding suitable graph structures
for their dynamic learning tasks.
