# A Comparison of Deep-Learning Methods for Analysing and Predicting Business Processes

arXiv: https://arxiv.org/abs/2102.07838 · HTML: https://mlsanity.com/paper/2102.07838/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 35 references that are in the map.
- First posted on arXiv 11 Feb 2021.

## Builds on (most cited first, 12 of 12)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://mlsanity.com/paper/1806.01973/) (2018)
- [Link Prediction Based on Graph Neural Networks](https://mlsanity.com/paper/1802.09691/) (2018)
- [Predictive Business Process Monitoring with LSTM Neural Networks](https://mlsanity.com/paper/1612.02130/) (2016)
- [Predictive Monitoring of Business Processes](https://mlsanity.com/paper/1312.4874/) (2013)
- [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)
- [Lifelong Learning of Graph Neural Networks for Open-World Node Classification](https://mlsanity.com/paper/2006.14422/) (2020)
- [Using Deep Learning for Title-Based Semantic Subject Indexing to Reach Competitive Performance to Full-Text](https://mlsanity.com/paper/1801.06717/) (2018)
- [Multi-Modal Adversarial Autoencoders for Recommendations of Citations and Subject Labels](https://mlsanity.com/paper/1907.12366/) (2019)

## Built on by (most cited first, 11 of 11)

- [Bag-of-Words vs. Graph vs. Sequence in Text Classification: Questioning the Necessity of Text-Graphs and the Surprising Strength of a Wide MLP](https://mlsanity.com/paper/2109.03777/) (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)
- [SNAP: Semantic Stories for Next Activity Prediction](https://mlsanity.com/paper/2401.15621/) (2024)
- [Trace Encoding in Process Mining: a survey and benchmarking](https://mlsanity.com/paper/2301.02167/) (2023)
- [A Framework for Extracting and Encoding Features from Object-Centric Event Data](https://mlsanity.com/paper/2209.01219/) (2022)
- [RLHGNN: Reinforcement Learning-driven Heterogeneous Graph Neural Network for Next Activity Prediction in Business Processes](https://mlsanity.com/paper/2507.02690/) (2025)
- [Graph Summarization with Graph Neural Networks](https://mlsanity.com/paper/2203.05919/) (2022)
- [Encoder-Decoder Model for Suffix Prediction in Predictive Monitoring](https://mlsanity.com/paper/2211.16106/) (2022)
- [Semiconductor Industry Trend Prediction with Event Intervention Based on LSTM Model in Sentiment-Enhanced Time Series Data](https://mlsanity.com/paper/2511.15112/) (2025)
- [GNN4PPM: Multi-Target Predictive Process Monitoring with Relational Graph Convolutional Networks](https://mlsanity.com/paper/2609.14534/) (2026)

## Abstract (arXiv)

Deep-learning models such as Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) have been successfully used for process-mining tasks. They have achieved better performance for different predictive tasks than traditional approaches. We extend the existing body of research by testing four different variants of Graph Neural Networks (GNN) and a fully connected Multi-layer Perceptron (MLP) with dropout for the tasks of predicting the nature and timestamp of the next process activity. In contrast to existing studies, we evaluate our models' performance at different stages of a process, determined by quartiles of the number of events and normalized quarters of the case duration. This provides new insights into the performance of a prediction model, as they behave differently at different stages of a business-process. Interestingly, our experiments show that the simple MLP often outperforms more sophisticated deep-learning models in both prediction tasks. We argue that care needs to be taken when applying automated process-prediction techniques at different stages of a process. We further argue that researchers should reflect their results with strong baselines methods like simple MLPs.