  Many problems such as vertex classification andlink prediction in network
data can be solvedusing graph embeddings, and a number of algo-rithms are known
for constructing such embed-dings. However, it is difficult to use graphs
tocapture non-binary relations such as communitiesof vertices. These kinds of
complex relations areexpressed more naturally as hypergraphs. Whilehypergraphs
are a generalization of graphs, state-of-the-art graph embedding techniques are
notadequate for solving prediction and classificationtasks on large hypergraphs
accurately in reason-able time. In this paper, we introduce NetVec,a novel
multi-level framework for scalable un-supervised hypergraph embedding, that can
becoupled with any graph embedding algorithm toproduce embeddings of
hypergraphs with millionsof nodes and hyperedges in a few minutes.
