  The links in many real networks are evolving with time. The task of dynamic
link prediction is to use past connection histories to infer links of the
network at a future time. How to effectively learn the temporal and structural
pattern of the network dynamics is the key. In this paper, we propose a graph
representation learning model based on enhanced structure and temporal
information (GRL\_EnSAT). For structural information, we exploit a combination
of a graph attention network (GAT) and a self-attention network to capture
structural neighborhood. For temporal dynamics, we use a masked self-attention
network to capture the dynamics in the link evolution. In this way, GRL\_EnSAT
not only learns low-dimensional embedding vectors but also preserves the
nonlinear dynamic feature of the evolving network. GRL\_EnSAT is evaluated on
four real datasets, in which GRL\_EnSAT outperforms most advanced baselines.
Benefiting from the dynamic self-attention mechanism, GRL\_EnSAT yields better
performance than approaches based on recursive graph evolution modeling.
