  Networks are one of the most powerful structures for modeling problems in the
real world. Downstream machine learning tasks defined on networks have the
potential to solve a variety of problems. With link prediction, for instance,
one can predict whether two persons will become friends on a social network.
Many machine learning algorithms, however, require that each input example is a
real vector. Network embedding encompasses various methods for unsupervised,
and sometimes supervised, learning of feature representations of nodes and
links in a network. Typically, embedding methods are based on the assumption
that the similarity between nodes in the network should be reflected in the
learned feature representations. In this paper, we review significant
contributions to network embedding in the last decade. In particular, we look
at four methods: Spectral Clustering, DeepWalk, Large-scale Information Network
Embedding (LINE), and node2vec. We describe each method and list its advantages
and shortcomings. In addition, we give examples of real-world machine learning
problems on networks in which the embedding is critical in order to maximize
the predictive performance of the machine learning task. Finally, we take a
look at research trends and state-of-the art methods in the research on network
embedding.
