Structure-Aware Message Passing on Petri Nets through Directed Walks and Incidence Diffusion
Read the original on arXiv Machine Learning →The paper introduces two novel techniques for enhancing graph neural networks on Petri nets: a positional encoding derived from directed place‑transition walk return probabilities (PNRWPE) and a diffusion‑kernel‑based message gating using the incidence matrix (DA‑PNGNN). These methods explicitly capture directed cycles and invariants that were previously hidden, leading to significant performance gains. On the Process Discovery Contest 2023 dataset, PNRWPE raises macro balanced accuracy from 0.751 to 0.796, nearly doubling loop detection accuracy, while DA‑PNGNN further improves it to 0.830 and boosts net‑to‑tree retrieval on ProcRosetta by 4.1–5.9 points.
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