Quasimetric embeddings and their applications
Facundo M\'emoli, Anastasios Sidiropoulos, Vijay Sridhar

TL;DR
This paper extends metric embedding techniques to quasimetric spaces, especially those from directed graphs, and applies these results to improve approximation algorithms for graph partitioning problems.
Contribution
It introduces random quasipartitions for quasimetric spaces and provides bounds on embeddings into quasiultrametrics, with applications to graph algorithms.
Findings
Embedding quasimetric spaces into quasiultrametrics with low distortion is possible for spaces supported on graphs with bounded treewidth.
New approximation algorithms for Directed Non-Bipartite Sparsest-Cut and Multicut problems with polynomial runtime.
Lower bounds on embedding distortions highlight fundamental differences between metric and quasimetric spaces.
Abstract
We study generalizations of classical metric embedding results to the case of quasimetric spaces; that is, spaces that do not necessarily satisfy symmetry. Quasimetric spaces arise naturally from the shortest-path distances on directed graphs. Perhaps surprisingly, very little is known about low-distortion embeddings for quasimetric spaces. Random embeddings into ultrametric spaces are arguably one of the most successful geometric tools in the context of algorithm design. We extend this to the quasimetric case as follows. We show that any -point quasimetric space supported on a graph of treewidth admits a random embedding into quasiultrametric spaces with distortion , where quasiultrametrics are a natural generalization of ultrametrics. This result allows us to obtain -approximation algorithms for the Directed Non-Bipartite Sparsest-Cut and the…
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