  Network embedding aims to learn low-dimensional representations of nodes
while capturing structure information of networks. It has achieved great
success on many tasks of network analysis such as link prediction and node
classification. Most of existing network embedding algorithms focus on how to
learn static homogeneous networks effectively. However, networks in the real
world are more complex, e.g., networks may consist of several types of nodes
and edges (called heterogeneous information) and may vary over time in terms of
dynamic nodes and edges (called evolutionary patterns). Limited work has been
done for network embedding of dynamic heterogeneous networks as it is
challenging to learn both evolutionary and heterogeneous information
simultaneously. In this paper, we propose a novel dynamic heterogeneous network
embedding method, termed as DyHATR, which uses hierarchical attention to learn
heterogeneous information and incorporates recurrent neural networks with
temporal attention to capture evolutionary patterns. We benchmark our method on
four real-world datasets for the task of link prediction. Experimental results
show that DyHATR significantly outperforms several state-of-the-art baselines.
