Embedding Directed Graphs in Potential Fields Using FastMap-D

Authors

  • Sriram Gopalakrishnan Arizona State University
  • Liron Cohen University of Southern California
  • Sven Koenig University of Southern California
  • T. K. Kumar University of Southern California

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 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.

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Published

2021-09-01