# Masking Neural Networks Using Reachability Graphs to Predict Process Events

arXiv: https://arxiv.org/abs/2108.00404 · HTML: https://mlsanity.com/paper/2108.00404/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 1 Aug 2021.

## Builds on (most cited first, 8 of 8)

- [Searching for Activation Functions](https://mlsanity.com/paper/1710.05941/) (2017)
- [Predictive Business Process Monitoring with LSTM Neural Networks](https://mlsanity.com/paper/1612.02130/) (2016)
- [Predicting Process Behaviour using Deep Learning](https://mlsanity.com/paper/1612.04600/) (2016)
- [Predictive Business Process Monitoring via Generative Adversarial Nets: The Case of Next Event Prediction](https://mlsanity.com/paper/2003.11268/) (2020)
- [Automated Discovery of Process Models from Event Logs: Review and Benchmark](https://mlsanity.com/paper/1705.02288/) (2017)
- [Adversarial System Variant Approximation to Quantify Process Model Generalization](https://mlsanity.com/paper/2003.12168/) (2020)
- [Process Mining of Programmable Logic Controllers: Input/Output Event Logs](https://mlsanity.com/paper/1903.09513/) (2019)
- [Bootstrapping Generalization of Process Models Discovered From Event Data](https://mlsanity.com/paper/2107.03876/) (2021)

## Built on by (most cited first, 1 of 1)

- [Decision-Aware Suffix Prediction and Reasoning of Business Processes](https://mlsanity.com/paper/2609.06169/) (2026)

## Abstract (arXiv)

Decay Replay Mining is a deep learning method that utilizes process model notations to predict the next event. However, this method does not intertwine the neural network with the structure of the process model to its full extent. This paper proposes an approach to further interlock the process model of Decay Replay Mining with its neural network for next event prediction. The approach uses a masking layer which is initialized based on the reachability graph of the process model. Additionally, modifications to the neural network architecture are proposed to increase the predictive performance. Experimental results demonstrate the value of the approach and underscore the importance of discovering precise and generalized process models.