mlsanityData to 7 Oct 2026

Embedding Graph Convolutional Networks in Recurrent Neural Networks for Predictive Monitoring

arXiv 2112.09641 ↗First posted 17 Dec 2021cs.LGcited by 12

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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.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 20 of its 48 references that are in the map. First posted on arXiv 17 Dec 2021.

Builds on

20 of 48 references.

References · 20

  1. 2017Attention Is All You Needcited by 59,932
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  3. 2018How Powerful are Graph Neural Networks?cited by 4,355
  4. 2014Going Deeper with Convolutionscited by 4,231
  5. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
  6. 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,684
  7. 2016Language Modeling with Gated Convolutional Networkscited by 688
  8. 2016Predictive Business Process Monitoring with LSTM Neural Networkscited by 103
  9. 2016Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysiscited by 92
  10. 2016Predicting Process Behaviour using Deep Learningcited by 59
  11. 2020Gated Graph Recurrent Neural Networkscited by 57
  12. 2020Deep Learning for Predictive Business Process Monitoring: Review and Benchmarkcited by 45
8 more
  1. 2013Predictive Monitoring of Business Processescited by 37
  2. 2021ProcessTransformer: Predictive Business Process Monitoring with Transformer Networkcited by 34
  3. 2019Process Mining for Python (PM4Py): Bridging the Gap Between Process- and Data Sciencecited by 32
  4. 2020Predictive Business Process Monitoring via Generative Adversarial Nets: The Case of Next Event Predictioncited by 31
  5. 2017Frustratingly Short Attention Spans in Neural Language Modelingcited by 18
  6. 2018An Interdisciplinary Comparison of Sequence Modeling Methods for Next-Element Predictioncited by 14
  7. 2021A Comparison of Deep-Learning Methods for Analysing and Predicting Business Processescited by 11
  8. 2019Exploiting Event Log Event Attributes in RNN Based Predictioncited by 8

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 0; 2024: 2; 2025: 5; 2026: 3.

12 papers.

  1. 2024SNAP: Semantic Stories for Next Activity Predictioncited by 8
  2. 2024ADEdgeDrop: Adversarial Edge Dropping for Robust Graph Neural Networkscited by 7
  3. 2025Self-Explaining Neural Networks for Business Process Monitoringcited by 5
  4. 2022Can recurrent neural networks learn process model structure?cited by 3
  5. 2025HGCN(O): A Self-Tuning GCN HyperModel Toolkit for Outcome Prediction in Event-Sequence Datacited by 3
  6. 2025Time-Aware and Transition-Semantic Graph Neural Networks for Interpretable Predictive Business Process Monitoringcited by 2
  7. 2025RLHGNN: Reinforcement Learning-driven Heterogeneous Graph Neural Network for Next Activity Prediction in Business Processescited by 1
  8. 2025Predictive Process Monitoring Using Object-centric Graph Embeddingscited by 1
  9. 2022Encoder-Decoder Model for Suffix Prediction in Predictive Monitoringcited by 0
  10. 2026Promoting Simple Agents: Ensemble Methods for Event-Log Predictioncited by 0
  11. 2026EHHN: An Event-driven Heterogeneous Hypergraph Network for Object-Centric Next Activity Predictioncited by 0
  12. 2026GNN4PPM: Multi-Target Predictive Process Monitoring with Relational Graph Convolutional Networkscited by 0