  The development of Graph Representation Learning methods for heterogeneous
graphs is fundamental in several real-world applications, since in several
contexts graphs are characterized by different types of nodes and edges. We
introduce a an algorithmic framework (Het-node2vec) that extends the original
node2vec node-neighborhood sampling method to heterogeneous multigraphs. 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.
