GNN4PPM: Multi-Target Predictive Process Monitoring with Relational Graph Convolutional Networks
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 40 references that are in the map. First posted on arXiv 13 Sep 2026.
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References · 15
- 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,375
- 2016node2vec: Scalable Feature Learning for Networkscited by 1,918
- 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,694
- 2020Knowledge Graphscited by 621
- 2016Predictive Business Process Monitoring with LSTM Neural Networkscited by 104
- 2022Evaluating Explainability for Graph Neural Networkscited by 87
- 2016Predicting Process Behaviour using Deep Learningcited by 60
- 2020Deep Learning for Predictive Business Process Monitoring: Review and Benchmarkcited by 45
- 2013Predictive Monitoring of Business Processescited by 37
- 2021ProcessTransformer: Predictive Business Process Monitoring with Transformer Networkcited by 37
- 2024PGTNet: A Process Graph Transformer Network for Remaining Time Prediction of Business Process Instancescited by 14
- 2021Embedding Graph Convolutional Networks in Recurrent Neural Networks for Predictive Monitoringcited by 12
3 more
- 2021A Comparison of Deep-Learning Methods for Analysing and Predicting Business Processescited by 11
- 2023Interpretable and Explainable Machine Learning Methods for Predictive Process Monitoring: A Systematic Literature Reviewcited by 4
- 2024Markov Process-Based Graph Convolutional Networks for Entity Classification in Knowledge Graphscited by 1
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