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.
@article{arxiv.2512.20311,
title = {Algorithm for Interpretable Graph Features via Motivic Persistent Cohomology},
author = {Yoshihiro Maruyama},
journal= {arXiv preprint arXiv:2512.20311},
year = {2025}
}