  Representation learning of static and more recently dynamically evolving
graphs has gained noticeable attention. Existing approaches for modelling graph
dynamics focus extensively on the evolution of individual nodes independently
of the evolution of mesoscale community structures. As a result, current
methods do not provide useful tools to study and cannot explicitly capture
temporal community dynamics. To address this challenge, we propose GRADE - a
probabilistic model that learns to generate evolving node and community
representations by imposing a random walk prior over their trajectories. Our
model also learns node community membership which is updated between time steps
via a transition matrix. At each time step link generation is performed by
first assigning node membership from a distribution over the communities, and
then sampling a neighbor from a distribution over the nodes for the assigned
community. We parametrize the node and community distributions with neural
networks and learn their parameters via variational inference. Experiments
demonstrate GRADE outperforms baselines in dynamic link prediction, shows
favourable performance on dynamic community detection, and identifies coherent
and interpretable evolving communities.
