%AGopalakrishnan, S.%ACohen, L.%AKoenig, S.%AKumar, S.%D2020%I %K %MOSTI ID: 10179965 %PMedium: X %TEmbedding Directed Graphs in Potential Fields Using FastMap-D %XEmbedding 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. Errata: This version of the paper corrects a programming mistake, resulting in even better experimental results than those reported in the original paper. Country unknown/Code not availableOSTI-MSA