# Embedding Graph Convolutional Networks in Recurrent Neural Networks for Predictive Monitoring

arXiv: https://arxiv.org/abs/2112.09641 · HTML: https://mlsanity.com/paper/2112.09641/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 48 references that are in the map.
- First posted on arXiv 17 Dec 2021.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Language Modeling with Gated Convolutional Networks](https://mlsanity.com/paper/1612.08083/) (2016)
- [Predictive Business Process Monitoring with LSTM Neural Networks](https://mlsanity.com/paper/1612.02130/) (2016)
- [Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis](https://mlsanity.com/paper/1606.04316/) (2016)
- [Predicting Process Behaviour using Deep Learning](https://mlsanity.com/paper/1612.04600/) (2016)
- [Gated Graph Recurrent Neural Networks](https://mlsanity.com/paper/2002.01038/) (2020)
- [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)
- [Process Mining for Python (PM4Py): Bridging the Gap Between Process- and Data Science](https://mlsanity.com/paper/1905.06169/) (2019)
- [Predictive Business Process Monitoring via Generative Adversarial Nets: The Case of Next Event Prediction](https://mlsanity.com/paper/2003.11268/) (2020)
- [Frustratingly Short Attention Spans in Neural Language Modeling](https://mlsanity.com/paper/1702.04521/) (2017)
- [An Interdisciplinary Comparison of Sequence Modeling Methods for Next-Element Prediction](https://mlsanity.com/paper/1811.00062/) (2018)
- [A Comparison of Deep-Learning Methods for Analysing and Predicting Business Processes](https://mlsanity.com/paper/2102.07838/) (2021)
- [Exploiting Event Log Event Attributes in RNN Based Prediction](https://mlsanity.com/paper/1904.06895/) (2019)

## Built on by (most cited first, 12 of 12)

- [SNAP: Semantic Stories for Next Activity Prediction](https://mlsanity.com/paper/2401.15621/) (2024)
- [ADEdgeDrop: Adversarial Edge Dropping for Robust Graph Neural Networks](https://mlsanity.com/paper/2403.09171/) (2024)
- [Self-Explaining Neural Networks for Business Process Monitoring](https://mlsanity.com/paper/2503.18067/) (2025)
- [HGCN(O): A Self-Tuning GCN HyperModel Toolkit for Outcome Prediction in Event-Sequence Data](https://mlsanity.com/paper/2507.22524/) (2025)
- [Can recurrent neural networks learn process model structure?](https://mlsanity.com/paper/2212.06430/) (2022)
- [Time-Aware and Transition-Semantic Graph Neural Networks for Interpretable Predictive Business Process Monitoring](https://mlsanity.com/paper/2508.09527/) (2025)
- [RLHGNN: Reinforcement Learning-driven Heterogeneous Graph Neural Network for Next Activity Prediction in Business Processes](https://mlsanity.com/paper/2507.02690/) (2025)
- [Predictive Process Monitoring Using Object-centric Graph Embeddings](https://mlsanity.com/paper/2507.15411/) (2025)
- [Encoder-Decoder Model for Suffix Prediction in Predictive Monitoring](https://mlsanity.com/paper/2211.16106/) (2022)
- [Promoting Simple Agents: Ensemble Methods for Event-Log Prediction](https://mlsanity.com/paper/2604.21629/) (2026)
- [EHHN: An Event-driven Heterogeneous Hypergraph Network for Object-Centric Next Activity Prediction](https://mlsanity.com/paper/2607.01785/) (2026)
- [GNN4PPM: Multi-Target Predictive Process Monitoring with Relational Graph Convolutional Networks](https://mlsanity.com/paper/2609.14534/) (2026)

## Abstract (arXiv)

Predictive monitoring of business processes is a subfield of process mining that aims to predict, among other things, the characteristics of the next event or the sequence of next events. Although multiple approaches based on deep learning have been proposed, mainly recurrent neural networks and convolutional neural networks, none of them really exploit the structural information available in process models. This paper proposes an approach based on graph convolutional networks and recurrent neural networks that uses information directly from the process model. An experimental evaluation on real-life event logs shows that our approach is more consistent and outperforms the current state-of-the-art approaches.