  Network embedding, which aims to learn low-dimensional representations of
nodes, has been used for various graph related tasks including visualization,
link prediction and node classification. Most existing embedding methods rely
solely on network structure. However, in practice we often have auxiliary
information about the nodes and/or their interactions, e.g., content of
scientific papers in co-authorship networks, or topics of communication in
Twitter mention networks. Here we propose a novel embedding method that uses
both network structure and edge attributes to learn better network
representations. Our method jointly minimizes the reconstruction error for
higher-order node neighborhood, social roles and edge attributes using a deep
architecture that can adequately capture highly non-linear interactions. We
demonstrate the efficacy of our model over existing state-of-the-art methods on
a variety of real-world networks including collaboration networks, and social
networks. We also observe that using edge attributes to inform network
embedding yields better performance in downstream tasks such as link prediction
and node classification.
