  Graph embedding, aiming to learn low-dimensional representations (aka.
embeddings) of nodes, has received significant attention recently. Recent years
have witnessed a surge of efforts made on static graphs, among which Graph
Convolutional Network (GCN) has emerged as an effective class of models.
However, these methods mainly focus on the static graph embedding. In this
work, we propose an efficient dynamic graph embedding approach, Dynamic Graph
Convolutional Network (DyGCN), which is an extension of GCN-based methods. We
naturally generalizes the embedding propagation scheme of GCN to dynamic
setting in an efficient manner, which is to propagate the change along the
graph to update node embeddings. The most affected nodes are first updated, and
then their changes are propagated to the further nodes and leads to their
update. Extensive experiments conducted on various dynamic graphs demonstrate
that our model can update the node embeddings in a time-saving and
performance-preserving way.
