mlsanityData to 7 Oct 2026

Algorithm for Interpretable Graph Features via Motivic Persistent Cohomology

arXiv 2512.20311 ↗First posted 23 Dec 2025cs.CG cs.DM cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 4 of its 36 references that are in the map. First posted on arXiv 23 Dec 2025.

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References · 4

  1. 2018How Powerful are Graph Neural Networks?cited by 4,355
  2. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  3. 2018Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networkscited by 878
  4. 2015Convolutional Networks on Graphs for Learning Molecular Fingerprintscited by 751

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