  Classical network embeddings create a low dimensional representation of the
learned relationships between features across nodes. Such embeddings are
important for tasks such as link prediction and node classification. In the
current paper, we consider low dimensional embeddings of dynamic networks, that
is a family of time varying networks where there exist both temporal and
spatial link relationships between nodes. We present novel embedding methods
for a dynamic network based on higher order tensor decompositions for tensorial
representations of the dynamic network. In one sense, our embeddings are
analogous to spectral embedding methods for static networks. We provide a
rationale for our algorithms via a mathematical analysis of some potential
reasons for their effectiveness. Finally, we demonstrate the power and
efficiency of our approach by comparing our algorithms' performance on the link
prediction task against an array of current baseline methods across three
distinct real-world dynamic networks.
