Embedding Directed Graphs in Potential Fields Using FastMap-D

Proceedings of the International Symposium on Combinatorial Search, 13

Volume

Vol. 13 No. 1 (2020): Thirteenth Annual Symposium on Combinatorial Search

Long Papers

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 to-and-fro 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.

10.1609/socs.v11i1.18534