  We introduce a set of algorithms (Het-node2vec) that extend the original
node2vec node-neighborhood sampling method to heterogeneous multigraphs, i.e.
networks characterized by multiple types of nodes and edges. The resulting
random walk samples capture both the structural characteristics of the graph
and the semantics of the different types of nodes and edges. The proposed
algorithms can focus their attention on specific node or edge types, allowing
accurate representations also for underrepresented types of nodes/edges that
are of interest for the prediction problem under investigation. These rich and
well-focused representations can boost unsupervised and supervised learning on
heterogeneous graphs.
