On minimum spanning tree-like metric spaces
Momoko Hayamizu, Kenji Fukumizu

TL;DR
This paper investigates conditions under which a metric space can be perfectly represented by a minimum spanning tree, introducing a new four-point condition that characterizes tree-like metric spaces without relying on the four-point condition.
Contribution
It introduces a novel four-point condition that characterizes when a metric space can be realized by a spanning tree, providing criteria for measuring how tree-like a metric space is.
Findings
A spanning tree representation, if it exists, is isomorphic to the unique MST of the associated complete graph.
The four-point condition is necessary and sufficient for the existence of a spanning tree representation under unique distances.
The paper provides algorithms to evaluate spanning tree-likeness and path-likeness in polynomial time.
Abstract
We attempt to shed new light on the notion of 'tree-like' metric spaces by focusing on an approach that does not use the four-point condition. Our key question is: Given metric space on points, when does a fully labelled positive-weighted tree exist on the same vertices that precisely realises using its shortest path metric? We prove that if a spanning tree representation, , of exists, then it is isomorphic to the unique minimum spanning tree in the weighted complete graph associated with , and we introduce a fourth-point condition that is necessary and sufficient to ensure the existence of whenever each distance in is unique. In other words, a finite median graph, in which each geodesic distance is distinct, is simply a tree. Provided that the tie-breaking assumption holds, the fourth-point condition serves as a criterion for measuring the…
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
TopicsComputational Geometry and Mesh Generation · Remote Sensing and LiDAR Applications · 3D Shape Modeling and Analysis
