  We present path2vec, a new approach for learning graph embeddings that relies
on structural measures of pairwise node similarities. The model learns
representations for nodes in a dense space that approximate a given
user-defined graph distance measure, such as e.g. the shortest path distance or
distance measures that take information beyond the graph structure into
account. Evaluation of the proposed model on semantic similarity and word sense
disambiguation tasks, using various WordNet-based similarity measures, show
that our approach yields competitive results, outperforming strong graph
embedding baselines. The model is computationally efficient, being orders of
magnitude faster than the direct computation of graph-based distances.
