  Real-world networks are composed of diverse interacting and evolving
entities, while most of existing researches simply characterize them as
particular static networks, without consideration of the evolution trend in
dynamic networks. Recently, significant progresses in tracking the properties
of dynamic networks have been made, which exploit changes of entities and links
in the network to devise network embedding techniques. Compared to widely
proposed static network embedding methods, dynamic network embedding endeavors
to encode nodes as low-dimensional dense representations that effectively
preserve the network structures and the temporal dynamics, which is beneficial
to multifarious downstream machine learning tasks. In this paper, we conduct a
systematical survey on dynamic network embedding. In specific, basic concepts
of dynamic network embedding are described, notably, we propose a novel
taxonomy of existing dynamic network embedding techniques for the first time,
including matrix factorization based, Skip-Gram based, autoencoder based,
neural networks based and other embedding methods. Additionally, we carefully
summarize the commonly used datasets and a wide variety of subsequent tasks
that dynamic network embedding can benefit. Afterwards and primarily, we
suggest several challenges that the existing algorithms faced and outline
possible directions to facilitate the future research, such as dynamic
embedding models, large-scale dynamic networks, heterogeneous dynamic networks,
dynamic attributed networks, task-oriented dynamic network embedding and more
embedding spaces.
