Auto-ML Graph Neural Network Hypermodels for Outcome Prediction in Event-Sequence Data
It provides a robust benchmark for outcome prediction in complex event-sequence data, though it appears incremental as it builds on earlier work.
This paper tackles outcome prediction in event-sequence data by introducing HGNN(O), an AutoML GNN hypermodel framework, achieving accuracy over 0.98 on the Traffic Fines dataset and weighted F1 scores up to 0.86 on the Patients dataset.
This paper introduces HGNN(O), an AutoML GNN hypermodel framework for outcome prediction on event-sequence data. Building on our earlier work on graph convolutional network hypermodels, HGNN(O) extends four architectures-One Level, Two Level, Two Level Pseudo Embedding, and Two Level Embedding-across six canonical GNN operators. A self-tuning mechanism based on Bayesian optimization with pruning and early stopping enables efficient adaptation over architectures and hyperparameters without manual configuration. Empirical evaluation on both balanced and imbalanced event logs shows that HGNN(O) achieves accuracy exceeding 0.98 on the Traffic Fines dataset and weighted F1 scores up to 0.86 on the Patients dataset without explicit imbalance handling. These results demonstrate that the proposed AutoML-GNN approach provides a robust and generalizable benchmark for outcome prediction in complex event-sequence data.