Quadratic Forms for Measuring Geometric Trees in 3-dimensional Space
Abstract
Tree-like structures appear in many areas of science, and their shapes can help understand the underlying processes they drive or that give rise to them. By thinking of these structures as geometric graphs in $\mathbb{R}^3$, we gain access to tools from computational geometry and topology to study them. In this paper, we adopt the theory of quadratic forms to measure the directional spread of geometric graphs, and we introduce the hexplot model -- equipped with a metric derived from the Fish...
Description / Details
Tree-like structures appear in many areas of science, and their shapes can help understand the underlying processes they drive or that give rise to them. By thinking of these structures as geometric graphs in , we gain access to tools from computational geometry and topology to study them. In this paper, we adopt the theory of quadratic forms to measure the directional spread of geometric graphs, and we introduce the hexplot model -- equipped with a metric derived from the Fisher metric on the standard triangle -- to visualize, measure, and collect statistics.
Source: arXiv:2606.20096v1 - http://arxiv.org/abs/2606.20096v1 PDF: https://arxiv.org/pdf/2606.20096v1 Original Link: http://arxiv.org/abs/2606.20096v1
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Jun 19, 2026
Neuroscience
Neuroscience
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