  Network embedding methods aim at learning low-dimensional latent
representation of nodes in a network. While achieving competitive performance
on a variety of network inference tasks such as node classification and link
prediction, these methods treat the relations between nodes as a binary
variable and ignore the rich semantics of edges. In this work, we attempt to
learn network embeddings which simultaneously preserve network structure and
relations between nodes. Experiments on several real-world networks illustrate
that by considering different relations between different node pairs, our
method is capable of producing node embeddings of higher quality than a number
of state-of-the-art network embedding methods, as evaluated on a challenging
multi-label node classification task.
