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The development of Graph Representation Learning methods for heterogeneousgraphs is fundamental in several real-world applications, since in severalcontexts graphs are characterized by different types of nodes and edges. Weintroduce a an algorithmic framework (Het-node2vec) that extends the originalnode2vec node-neighborhood sampling method to heterogeneous multigraphs. Theresulting random walk samples capture both the structural characteristics ofthe graph and the semantics of the different types of nodes and edges. Theproposed algorithms can focus their attention on specific node or edge types,allowing accurate representations also for underrepresented types ofnodes/edges that are of interest for the prediction problem underinvestigation. These rich and well-focused representations can boostunsupervised and supervised learning on heterogeneous graphs.

Het-node2vec: second order random walk sampling for heterogeneous multigraphs embedding