Algorithm for Interpretable Graph Features via Motivic Persistent Cohomology
Fuente:
arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908730464927744 |
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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 |