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

arXiv: https://arxiv.org/abs/2609.14534 · HTML: https://mlsanity.com/paper/2609.14534/

## 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.

## Builds on (most cited first, 15 of 15)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [Predictive Business Process Monitoring with LSTM Neural Networks](https://mlsanity.com/paper/1612.02130/) (2016)
- [Evaluating Explainability for Graph Neural Networks](https://mlsanity.com/paper/2208.09339/) (2022)
- [Predicting Process Behaviour using Deep Learning](https://mlsanity.com/paper/1612.04600/) (2016)
- [Deep Learning for Predictive Business Process Monitoring: Review and Benchmark](https://mlsanity.com/paper/2009.13251/) (2020)
- [Predictive Monitoring of Business Processes](https://mlsanity.com/paper/1312.4874/) (2013)
- [ProcessTransformer: Predictive Business Process Monitoring with Transformer Network](https://mlsanity.com/paper/2104.00721/) (2021)
- [PGTNet: A Process Graph Transformer Network for Remaining Time Prediction of Business Process Instances](https://mlsanity.com/paper/2404.06267/) (2024)
- [Embedding Graph Convolutional Networks in Recurrent Neural Networks for Predictive Monitoring](https://mlsanity.com/paper/2112.09641/) (2021)
- [A Comparison of Deep-Learning Methods for Analysing and Predicting Business Processes](https://mlsanity.com/paper/2102.07838/) (2021)
- [Interpretable and Explainable Machine Learning Methods for Predictive Process Monitoring: A Systematic Literature Review](https://mlsanity.com/paper/2312.17584/) (2023)
- [Markov Process-Based Graph Convolutional Networks for Entity Classification in Knowledge Graphs](https://mlsanity.com/paper/2412.17438/) (2024)

## 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.