Algorithm for Interpretable Graph Features via Motivic Persistent Cohomology

Fuente: arXiv
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Autore principale: Maruyama, Yoshihiro
Natura: Preprint
Pubblicazione: 2025
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author Maruyama, Yoshihiro
author_facet Maruyama, Yoshihiro
contents We present the Chromatic Persistence Algorithm (CPA), an event-driven method for computing persistent cohomological features of weighted graphs via graphic arrangements, a classical object in computational geometry. We establish rigorous complexity results: CPA is exponential in the worst case, fixed-parameter tractable in treewidth, and nearly linear for common graph families such as trees, cycles, and series-parallel graphs. Finally, we demonstrate its practical applicability through a controlled experiment on molecular-like graph structures.
format Preprint
id arxiv_https___arxiv_org_abs_2512_20311
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Algorithm for Interpretable Graph Features via Motivic Persistent Cohomology
Maruyama, Yoshihiro
Computational Geometry
Discrete Mathematics
Machine Learning
We present the Chromatic Persistence Algorithm (CPA), an event-driven method for computing persistent cohomological features of weighted graphs via graphic arrangements, a classical object in computational geometry. We establish rigorous complexity results: CPA is exponential in the worst case, fixed-parameter tractable in treewidth, and nearly linear for common graph families such as trees, cycles, and series-parallel graphs. Finally, we demonstrate its practical applicability through a controlled experiment on molecular-like graph structures.
title Algorithm for Interpretable Graph Features via Motivic Persistent Cohomology
topic Computational Geometry
Discrete Mathematics
Machine Learning
url https://arxiv.org/abs/2512.20311