mlsanityData to 14 Oct 2026

HGCN(O): A Self-Tuning GCN HyperModel Toolkit for Outcome Prediction in Event-Sequence Data

arXiv 2507.22524 ↗First posted 30 Jul 2025cs.LGcited by 4

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Abstract (arXiv)

We propose HGCN(O), a self-tuning toolkit using Graph Convolutional Network (GCN) models for event sequence prediction. Featuring four GCN architectures (O-GCN, T-GCN, TP-GCN, TE-GCN) across the GCNConv and GraphConv layers, our toolkit integrates multiple graph representations of event sequences with different choices of node- and graph-level attributes and in temporal dependencies via edge weights, optimising prediction accuracy and stability for balanced and unbalanced datasets. Extensive experiments show that GCNConv models excel on unbalanced data, while all models perform consistently on balanced data. Experiments also confirm the superior performance of HGCN(O) over traditional approaches. Applications include Predictive Business Process Monitoring (PBPM), which predicts future events or states of a business process based on event logs.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 28 of its 63 references that are in the map. First posted on arXiv 30 Jul 2025.

Builds on

28 of 63 references.

References · 28

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16 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.

  1. 2025Time-Aware and Transition-Semantic Graph Neural Networks for Interpretable Predictive Business Process Monitoringcited by 2
  2. 2025Leveraging Duration Pseudo-Embeddings in Multilevel LSTM and GCN Hypermodels for Outcome-Oriented PPMcited by 0
  3. 2025Auto-ML Graph Neural Network Hypermodels for Outcome Prediction in Event-Sequence Datacited by 0
  4. 2026Graph Grounded Cross Attention Transformer Neural Network for Structurally Constrained Full Event Sequence Generation in Predictive Process Monitoringcited by 0