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.
@article{arxiv.2002.05257,
title = {Shortest path distance approximation using deep learning techniques},
author = {Fatemeh Salehi Rizi and Joerg Schloetterer and Michael Granitzer},
journal= {arXiv preprint arXiv:2002.05257},
year = {2020}
}