GNN4PPM: Graph Neural Networks for Multi-Target Predictive Process Monitoring
A new arXiv paper proposes GNN4PPM, which applies relational graph convolutional networks to predictive process monitoring. The method targets several predictions at once, such as the next event in a running process, the time remaining until a trace finishes, and its eventual outcome. The authors argue that existing techniques typically address only one of these targets; the posted abstract is truncated before any experimental results are described.