  Network embedding methods aim at learning low-dimensional latent
representation of nodes in a network. These representations can be used as
features for a wide range of tasks on graphs such as classification,
clustering, link prediction, and visualization. In this survey, we give an
overview of network embeddings by summarizing and categorizing recent
advancements in this research field. We first discuss the desirable properties
of network embeddings and briefly introduce the history of network embedding
algorithms. Then, we discuss network embedding methods under different
scenarios, such as supervised versus unsupervised learning, learning embeddings
for homogeneous networks versus for heterogeneous networks, etc. We further
demonstrate the applications of network embeddings, and conclude the survey
with future work in this area.
