# PGTNet: A Process Graph Transformer Network for Remaining Time Prediction of Business Process Instances

arXiv: https://arxiv.org/abs/2404.06267 · HTML: https://mlsanity.com/paper/2404.06267/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 25 references that are in the map.
- First posted on arXiv 9 Apr 2024.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017)
- [Strategies for Pre-training Graph Neural Networks](https://mlsanity.com/paper/1905.12265/) (2019)
- [Recipe for a General, Powerful, Scalable Graph Transformer](https://mlsanity.com/paper/2205.12454/) (2022)
- [Rethinking Graph Transformers with Spectral Attention](https://mlsanity.com/paper/2106.03893/) (2021)
- [Bag of Tricks for Image Classification with Convolutional Neural Networks](https://mlsanity.com/paper/1812.01187/) (2018)
- [Sign and Basis Invariant Networks for Spectral Graph Representation Learning](https://mlsanity.com/paper/2202.13013/) (2022)
- [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)
- [Deep Learning for Predictive Business Process Monitoring: Review and Benchmark](https://mlsanity.com/paper/2009.13251/) (2020)
- [Survey and cross-benchmark comparison of remaining time prediction methods in business process monitoring](https://mlsanity.com/paper/1805.02896/) (2018)
- [ProcessTransformer: Predictive Business Process Monitoring with Transformer Network](https://mlsanity.com/paper/2104.00721/) (2021)
- [LSTM Networks for Data-Aware Remaining Time Prediction of Business Process Instances](https://mlsanity.com/paper/1711.03822/) (2017)
- [Creating Unbiased Public Benchmark Datasets with Data Leakage Prevention for Predictive Process Monitoring](https://mlsanity.com/paper/2107.01905/) (2021)
- [A Comparison of Deep-Learning Methods for Analysing and Predicting Business Processes](https://mlsanity.com/paper/2102.07838/) (2021)

## Built on by (most cited first, 14 of 14)

- [Directly Follows Graphs Go Predictive Process Monitoring With Graph Neural Networks](https://mlsanity.com/paper/2503.03197/) (2025)
- [Time-Aware and Transition-Semantic Graph Neural Networks for Interpretable Predictive Business Process Monitoring](https://mlsanity.com/paper/2508.09527/) (2025)
- [Log Optimization Simplification Method for Predicting Remaining Time](https://mlsanity.com/paper/2503.07683/) (2025)
- [Predictive Process Monitoring Using Object-centric Graph Embeddings](https://mlsanity.com/paper/2507.15411/) (2025)
- [Compliance-Aware Predictive Process Monitoring: A Neuro-Symbolic Approach](https://mlsanity.com/paper/2603.26948/) (2026)
- [On the Simplification of Neural Network Architectures for Predictive Process Monitoring](https://mlsanity.com/paper/2509.17145/) (2025)
- [Remaining Time Prediction in Outbound Warehouse Processes: A Case Study (Short Paper)](https://mlsanity.com/paper/2509.18986/) (2025)
- [Exploring LLM Features in Predictive Process Monitoring for Small-Scale Event-Logs](https://mlsanity.com/paper/2601.11468/) (2026)
- [Promoting Simple Agents: Ensemble Methods for Event-Log Prediction](https://mlsanity.com/paper/2604.21629/) (2026)
- [Mind the Long Tail: Understanding the Difficulty of Delay Detection in Business Processes](https://mlsanity.com/paper/2608.14367/) (2026)
- [D-TAIA: Domain-Aware LLM Adaptation for Multi-Task Predictive Process Monitoring](https://mlsanity.com/paper/2608.28236/) (2026)
- [GNN4PPM: Multi-Target Predictive Process Monitoring with Relational Graph Convolutional Networks](https://mlsanity.com/paper/2609.14534/) (2026)
- [Probabilistic Forecasting of Business Process Executions with Neural Temporal Point Processes](https://mlsanity.com/paper/2609.21382/) (2026)
- [A Node Embedding Framework for Model-Centric Conformance Checking in Process Mining](https://mlsanity.com/paper/2610.17702/) (2026)

## Abstract (arXiv)

We present PGTNet, an approach that transforms event logs into graph datasets and leverages graph-oriented data for training Process Graph Transformer Networks to predict the remaining time of business process instances. PGTNet consistently outperforms state-of-the-art deep learning approaches across a diverse range of 20 publicly available real-world event logs. Notably, our approach is most promising for highly complex processes, where existing deep learning approaches encounter difficulties stemming from their limited ability to learn control-flow relationships among process activities and capture long-range dependencies. PGTNet addresses these challenges, while also being able to consider multiple process perspectives during the learning process.