  Network embedding techniques are powerful to capture structural regularities
in networks and to identify similarities between their local fabrics. However,
conventional network embedding models are developed for static structures,
commonly consider nodes only and they are seriously challenged when the network
is varying in time. Temporal networks may provide an advantage in the
description of real systems, but they code more complex information, which
could be effectively represented only by a handful of methods so far. Here, we
propose a new method of event embedding of temporal networks, called weg2vec,
which builds on temporal and structural similarities of events to learn a low
dimensional representation of a temporal network. This projection successfully
captures latent structures and similarities between events involving different
nodes at different times and provides ways to predict the final outcome of
spreading processes unfolding on the temporal structure.
