Hugging Face Trending Papers

A Node Embedding Framework for Model-Centric Conformance Checking in Process Mining

Read the original on Hugging Face Trending Papers →

The paper presents a model‑centric evaluation framework for process discovery that uses node embeddings and Procrustes alignment to compare Petri net models across different datasets. By transforming Petri nets into graph representations, the method computes a bounded cosine‑distance metric that unifies fitness and precision assessments and remains robust to noise. Experiments on real and synthetic logs demonstrate that the embedding‑based indicator outperforms traditional conformance metrics and is sensitive to model degradation while providing activity‑level deviation localization.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at Hugging Face Trending Papers.

arXiv Machine Learning
3d ago

Structure-Aware Message Passing on Petri Nets through Directed Walks and Incidence Diffusion

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.

By Geri Skenderi
arXiv Machine Learning
Sep 22

Concurrency-Aware Process Model Forecasting with Causal Nets

The paper introduces a new approach to process model forecasting that uses causal nets instead of traditional directly-follows graphs, enabling explicit representation of concurrency. It forecasts time series of relation and binding counts, reconstructs future process models with AND/XOR semantics, and evaluates them using a protocol that handles partial traces for conformance checking. Experiments on four event logs show that the forecasted models achieve conformance close to re‑mined models and outperform static discovery baselines, though filtering infrequent bindings improves metrics at the cost of losing concurrent behavior.

By Yongbo Yu, Jari Peeperkorn, Johannes De Smedt, Jochen De Weerdt