  Representation learning in dynamic graphs is a challenging problem because
the topology of graph and node features vary at different time. This requires
the model to be able to effectively capture both graph topology information and
temporal information. Most existing works are built on recurrent neural
networks (RNNs), which are used to exact temporal information of dynamic
graphs, and thus they inherit the same drawbacks of RNNs. In this paper, we
propose Learning to Evolve on Dynamic Graphs (LEDG) - a novel algorithm that
jointly learns graph information and time information. Specifically, our
approach utilizes gradient-based meta-learning to learn updating strategies
that have better generalization ability than RNN on snapshots. It is
model-agnostic and thus can train any message passing based graph neural
network (GNN) on dynamic graphs. To enhance the representation power, we
disentangle the embeddings into time embeddings and graph intrinsic embeddings.
We conduct experiments on various datasets and down-stream tasks, and the
experimental results validate the effectiveness of our method.
