Embedding Metrics into Ultrametrics and Graphs into Spanning Trees with Constant Average Distortion
Ittai Abraham, Yair Bartal, Ofer Neiman

TL;DR
This paper introduces new embedding techniques that achieve constant average distortion for metric spaces and graphs, with bounds that are tight and applicable to ultrametrics and spanning trees, improving approximation quality.
Contribution
It presents the first scaling distortion embeddings with constant average distortion and tight bounds for ultrametrics and spanning trees, advancing the understanding of metric and graph approximations.
Findings
Embedding into ultrametrics with $O( oot{1/ ext{epsilon}})$ distortion
Existence of spanning trees with $O( oot{1/ ext{epsilon}})$ scaling distortion
Probabilistic embeddings into spanning trees with $ ilde{O}( ext{log}^2(1/ ext{epsilon}))$ distortion
Abstract
This paper addresses the basic question of how well can a tree approximate distances of a metric space or a graph. Given a graph, the problem of constructing a spanning tree in a graph which strongly preserves distances in the graph is a fundamental problem in network design. We present scaling distortion embeddings where the distortion scales as a function of , with the guarantee that for each the distortion of a fraction of all pairs is bounded accordingly. Such a bound implies, in particular, that the \emph{average distortion} and -distortions are small. Specifically, our embeddings have \emph{constant} average distortion and -distortion. This follows from the following results: we prove that any metric space embeds into an ultrametric with scaling distortion . For the graph setting we prove…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsGraph theory and applications · Topological and Geometric Data Analysis · Advanced Graph Theory Research
