  In recent years, Dynamic Graph (DG) representations have been increasingly
used for modeling dynamic systems due to their ability to integrate both
topological and temporal information in a compact representation. Dynamic
graphs allow to efficiently handle applications such as social network
prediction, recommender systems, traffic forecasting or electroencephalography
analysis, that can not be adressed using standard numeric representations. As a
direct consequence of the emergence of dynamic graph representations, dynamic
graph learning has emerged as a new machine learning problem, combining
challenges from both sequential/temporal data processing and static graph
learning. In this research area, Dynamic Graph Neural Network (DGNN) has became
the state of the art approach and plethora of models have been proposed in the
very recent years. This paper aims at providing a review of problems and models
related to dynamic graph learning. The various dynamic graph supervised
learning settings are analysed and discussed. We identify the similarities and
differences between existing models with respect to the way time information is
modeled. Finally, general guidelines for a DGNN designer when faced with a
dynamic graph learning problem are provided.
