  Since many real world networks are evolving over time, such as social
networks and user-item networks, there are increasing research efforts on
dynamic network embedding in recent years. They learn node representations from
a sequence of evolving graphs but not only the latest network, for preserving
both structural and temporal information from the dynamic networks. Due to the
lack of comprehensive investigation of them, we give a survey of dynamic
network embedding in this paper. Our survey inspects the data model,
representation learning technique, evaluation and application of current
related works and derives common patterns from them. Specifically, we present
two basic data models, namely, discrete model and continuous model for dynamic
networks. Correspondingly, we summarize two major categories of dynamic network
embedding techniques, namely, structural-first and temporal-first that are
adopted by most related works. Then we build a taxonomy that refines the
category hierarchy by typical learning models. The popular experimental data
sets and applications are also summarized. Lastly, we have a discussion of
several distinct research topics in dynamic network embedding.
