Graph Grounded Cross Attention Transformer Neural Network for Structurally Constrained Full Event Sequence Generation in Predictive Process Monitoring

2026-06-17 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, quick

Summary

The Graph Grounded Cross Attention Transformer Neural Network (GGATN) is proposed to address the challenging task of structurally constrained full event sequence generation in Predictive Process Monitoring (PPM). Unlike existing work that focuses on component tasks, GGATN unifies this process by using a global process graph as structured activity memory, contextualizing sequence positions via Transformer self-attention, and injecting process topology through graph grounded cross-attention. GGATN generates activities, timestamps, length, and event/sequence level attributes in a single pass, followed by Viterbi-style graph constrained decoding for feasible paths and explicit termination. Experiments on six benchmark event logs demonstrate GGATN's superior and more reliable generation quality compared to local instruction prompted LLM baselines, achieving strong performance on sequence similarity, Damerau Levenshtein similarity, bigram control flow similarity, and duration distribution, while maintaining zero hallucinated activities and zero sequence level attribute inconsistency.

Key takeaway

For Machine Learning Engineers or AI Scientists developing solutions for predictive process monitoring, GGATN offers a robust approach to generating full event sequences with critical structural constraints. This method ensures transition feasibility, temporal order, and attribute consistency, preventing common issues like hallucinated activities. You should consider GGATN's unified single-pass generation and Viterbi-style decoding as a high-fidelity alternative to autoregressive LLM baselines when structural integrity is paramount in your process modeling tasks.

Key insights

GGATN unifies full event sequence generation in PPM by combining graph-grounded attention with single-pass decoding and Viterbi-style path constraints.

Principles

• Global process graphs provide stable structural priors.
• Graph-grounded cross-attention injects process topology.
• Viterbi-style decoding ensures feasible paths.

Method

GGATN generates activities, timestamps, length, and attributes in a single pass, then uses Viterbi-style graph-constrained decoding for feasible, terminated paths.

In practice

• Apply graph-based memory for sequence generation.
• Use Viterbi decoding for path feasibility.
• Integrate cross-attention for structural context.

Topics

• Predictive Process Monitoring
• Event Sequence Generation
• Transformer Neural Networks
• Graph Neural Networks
• Cross-Attention
• Viterbi Decoding

Best for: Research Scientist, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.