Embedding Directed Graphs in Potential Fields Using FastMap-D
Abstract
Embedding undirected graphs in a Euclidean space has many computational benefits. FastMap is an efficient embedding algorithm that facilitates a geometric interpretation of problems posed on undirected graphs. However, Euclidean distances are inherently symmetric and, thus, Euclidean embeddings cannot be used for directed graphs. In this paper, we present FastMap-D, an efficient generalization of FastMap to directed graphs. FastMap-D embeds vertices using a potential field to capture the asymmetry between the pairwise distances in directed graphs. FastMap-D learns a potential function to define the potential field using a machine learning module. In experiments on various kinds of directed graphs, we demonstrate the advantage of FastMap-D over other approaches.
Cite
@article{arxiv.2006.03112,
title = {Embedding Directed Graphs in Potential Fields Using FastMap-D},
author = {Sriram Gopalakrishnan and Liron Cohen and Sven Koenig and T. K. Satish Kumar},
journal= {arXiv preprint arXiv:2006.03112},
year = {2020}
}
Comments
9 pages, Published in Symposium on Combinatorial Search(SoCS-2020). Erratum with updated Results