  Computing shortest path distances between nodes lies at the heart of many
graph algorithms and applications. Traditional exact methods such as
breadth-first-search (BFS) do not scale up to contemporary, rapidly evolving
today's massive networks. Therefore, it is required to find approximation
methods to enable scalable graph processing with a significant speedup. In this
paper, we utilize vector embeddings learnt by deep learning techniques to
approximate the shortest paths distances in large graphs. We show that a
feedforward neural network fed with embeddings can approximate distances with
relatively low distortion error. The suggested method is evaluated on the
Facebook, BlogCatalog, Youtube and Flickr social networks.
