  Representation learning has overcome the often arduous and manual
featurization of networks through (unsupervised) feature learning as it results
in embeddings that can apply to a variety of downstream learning tasks. The
focus of representation learning on graphs has focused mainly on shallow
(node-centric) or deep (graph-based) learning approaches. While there have been
approaches that work on homogeneous and heterogeneous networks with multi-typed
nodes and edges, there is a gap in learning edge representations. This paper
proposes a novel unsupervised inductive method called AttrE2Vec, which learns a
low-dimensional vector representation for edges in attributed networks. It
systematically captures the topological proximity, attributes affinity, and
feature similarity of edges. Contrary to current advances in edge embedding
research, our proposal extends the body of methods providing representations
for edges, capturing graph attributes in an inductive and unsupervised manner.
Experimental results show that, compared to contemporary approaches, our method
builds more powerful edge vector representations, reflected by higher quality
measures (AUC, accuracy) in downstream tasks as edge classification and edge
clustering. It is also confirmed by analyzing low-dimensional embedding
projections.
