Abstract
The computation of distance measures between nodes in graphs is inefficient and does
not scale to large graphs. We explore dense
vector representations as an effective way to
approximate the same information: we introduce a simple yet efficient and effective approach for learning graph embeddings. Instead of directly operating on the graph structure, our method takes structural measures of
pairwise node similarities into account and
learns dense node representations reflecting
user-defined graph distance measures, such
as e.g. the shortest path distance or distance
measures that take information beyond the
graph structure into account. We demonstrate a speed-up of several orders of magnitude when predicting word similarity by vector
operations on our embeddings as opposed to
directly computing the respective path-based
measures, while outperforming various other
graph embeddings on semantic similarity and
word sense disambiguation tasks and show
evaluations on the WordNet graph and two
knowledge base graphs.