GNN4PPM: Multi-Target Predictive Process Monitoring with Relational Graph Convolutional Networks

Abstract:Predictive Process Monitoring (PPM) aims at predicting at runtime and as early as possible the future states of a process execution. Common tasks include predicting the next event, the time to completion of a trace, and outcomes. Existing approaches typically consider an event from the perspective of the executed activities along with their timestamps and case identifiers. This leads to the disadvantage that in real-life settings, there is much more information recorded in the event log that is not captured or completely ignored when performing prediction tasks. We introduce GNN4PPM, an approach that predicts all next events along with their complete data payload at once. We represent event information in a heterogeneous knowledge graph that captures the event log as an RDF semantics, and train the embeddings with a Relational Graph Convolutional Network (R-GCN). Our approach is promising in comparison to existing solutions, and experiments with state-of-the-art solutions prove the accuracy and applicability of GNN4PPM in complex settings.

Accepted for presentation at the BPM 2026 Forum. The final version will appear in the Lecture Notes in Business Information Processing (LNBIP) post-proceedings

Machine Learning (cs.LG)

arXiv:2609.14534 [cs.LG]

(or arXiv:2609.14534v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.14534 arXiv-issued DOI via DataCite (pending registration)