  Recent progress in research on Deep Graph Networks (DGNs) has led to a
maturation of the domain of learning on graphs. Despite the growth of this
research field, there are still important challenges that are yet unsolved.
Specifically, there is an urge of making DGNs suitable for predictive tasks on
realworld systems of interconnected entities, which evolve over time. With the
aim of fostering research in the domain of dynamic graphs, at first, we survey
recent advantages in learning both temporal and spatial information, providing
a comprehensive overview of the current state-of-the-art in the domain of
representation learning for dynamic graphs. Secondly, we conduct a fair
performance comparison among the most popular proposed approaches on node and
edge-level tasks, leveraging rigorous model selection and assessment for all
the methods, thus establishing a sound baseline for evaluating new
architectures and approaches
