HyperSteiner: Computing Heuristic Hyperbolic Steiner Minimal Trees

Fuente: arXiv
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Hauptverfasser: García-Castellanos, Alejandro, Medbouhi, Aniss Aiman, Marchetti, Giovanni Luca, Bekkers, Erik J., Kragic, Danica
Format: Preprint
Veröffentlicht: 2024
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author García-Castellanos, Alejandro
Medbouhi, Aniss Aiman
Marchetti, Giovanni Luca
Bekkers, Erik J.
Kragic, Danica
author_facet García-Castellanos, Alejandro
Medbouhi, Aniss Aiman
Marchetti, Giovanni Luca
Bekkers, Erik J.
Kragic, Danica
contents We propose HyperSteiner -- an efficient heuristic algorithm for computing Steiner minimal trees in the hyperbolic space. HyperSteiner extends the Euclidean Smith-Lee-Liebman algorithm, which is grounded in a divide-and-conquer approach involving the Delaunay triangulation. The central idea is rephrasing Steiner tree problems with three terminals as a system of equations in the Klein-Beltrami model. Motivated by the fact that hyperbolic geometry is well-suited for representing hierarchies, we explore applications to hierarchy discovery in data. Results show that HyperSteiner infers more realistic hierarchies than the Minimum Spanning Tree and is more scalable to large datasets than Neighbor Joining.
format Preprint
id arxiv_https___arxiv_org_abs_2409_05671
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle HyperSteiner: Computing Heuristic Hyperbolic Steiner Minimal Trees
García-Castellanos, Alejandro
Medbouhi, Aniss Aiman
Marchetti, Giovanni Luca
Bekkers, Erik J.
Kragic, Danica
Computational Geometry
We propose HyperSteiner -- an efficient heuristic algorithm for computing Steiner minimal trees in the hyperbolic space. HyperSteiner extends the Euclidean Smith-Lee-Liebman algorithm, which is grounded in a divide-and-conquer approach involving the Delaunay triangulation. The central idea is rephrasing Steiner tree problems with three terminals as a system of equations in the Klein-Beltrami model. Motivated by the fact that hyperbolic geometry is well-suited for representing hierarchies, we explore applications to hierarchy discovery in data. Results show that HyperSteiner infers more realistic hierarchies than the Minimum Spanning Tree and is more scalable to large datasets than Neighbor Joining.
title HyperSteiner: Computing Heuristic Hyperbolic Steiner Minimal Trees
topic Computational Geometry
url https://arxiv.org/abs/2409.05671