  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.
