Algorithm for Interpretable Graph Features via Motivic Persistent Cohomology
By: Yoshihiro Maruyama
Potential Business Impact:
Finds patterns in complex shapes, like molecules.
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.
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