A network embedding is a representation of a large graph in a low-dimensional
space, where vertices are modeled as vectors. The objective of a good embedding
is to preserve the proximity between vertices in the original graph. This way,
typical search and mining methods can be applied in the embedded space with the
help of off-the-shelf multidimensional indexing approaches. Existing network
embedding techniques focus on homogeneous networks, where all vertices are
considered to belong to a single class.