ANEDA: Adaptable Node Embeddings for Shortest Path Distance Approximation

Shortest path distance approximation is a crucial aspect of many graph algorithms, in particular the heuristic-based routing algorithms that make fast, scalable map navigation possible. Past literature has introduced deep learning models which try to approximate these distances by training on graph embeddings (i.e. node2vec, Gra100, ProNE, Poincare). We propose ANEDA, a more lightweight technique than the embedding and graph neural network scheme, which involves training the embeddings directly, using either previous embedding techniques or geographic coordinates as a good initialization. We demonstrate the applications ANEDA to deep A* routing and learned road maps. Through experiments on several road and social networks, we show our model's error reduction of up to 75% against two recent deep learning approaches, and its competitive performance against the larger, state-of-the-art architecture.