MHG-Predictor: A Multi-Layer Heterogeneous Graph-based Predictor for Next Activity in Complex Business Processes
Year: 2025
Type: conference-paper
Abstract: Accurate prediction of the next activity in business processes is crucial for operational efficiency and decision making in modern enterprise systems. Existing methods often fail to capture the multi-layered structural dependencies inherent in event logs. To address this limitation, we propose the Multi-layer Heterogeneous Gaph-based Predictor (MHG-Predictor), where the key innovation lies in our multi-layer heterogeneous graph that decomposes process knowledge into four interconnected layers: global process dynamics, instance-level patterns, activities, and contextual attributes, thereby capturing both temporal and structural dependencies in process execution. To leverage this rich structural information, we develop a multi-layer graph convolutional network (GCN) that combines GatedGraphConv and HeteroSAGE, unified through the multi-layer perceptrons (MLPs) for next activity prediction. Extensive experiments on real-world datasets demonstrate that MHG-Predictor outperforms existing methods, with an average improvement of more than 2% across all evaluation metrics compared to the best baseline method. Furthermore, the four-layer heterogeneous graph has been successfully deployed in the Remote Banking Center's intelligent ticket generation system, achieving an accuracy of up to 80%, representing an approximately 5% improvement over the previous system. The source code is available at https://github.com/Joker3993/MHG-Predictor.
Authors: Jiaxing Wang, Yifeng Yu, Ning Fang, Bin Cao, Jing Fan, Ji Zhang
Language: English
Subfield: Management Information Systems
Domain: Social Sciences
Open Access status: gold
OpenAlex ID: W4410636736