  Existing network embedding approaches tackle the problem of learning
low-dimensional node representations. However, networks can also be seen in the
light of edges interlinking pairs of nodes. The broad goal of this paper is to
introduce edge-centric network embeddings. We present an approach called ECNE,
which instead of computing node embeddings directly, computes edge embeddings
by relying on the notion of line graph coupled with an edge weighting mechanism
to preserve the dynamic of the original graph in the line graph. We also
present a link prediction framework called ECNE-LP, which given a target link
(u,v) first collects paths between nodes u and v, then directly embeds the
edges in these paths, and finally aggregates them toward predicting the
existence of a link. We show that both ECNE and ECNE-LP bring benefit wrt the
state-of-the-art.
