Explainable Multi-perspective Business Process Anomaly Detection Method Based on Graph Neural Networks

Anomalies in business processes can lead to significant losses, making timely detection and handling of these anomalies essential for business process management and optimization. Although current methods in business processes might uncover abnormal cases or attributes in logs, they fail to provide adequate explanations for the anomalies detected. To enable reliable detection, a multi-perspective anomaly detection and explanation method for business processes based on graph neural networks is proposed. Firstly, a graph structure is constructed to reveal the dependencies between various attributes. On this basis, a multiple-graph neural network predictor is trained to predict each attribute of the next event separately. Then, according to the probability distribution of the prediction results, the anomaly score is calculated, and the anomalous attributes and cases are identified. In addition, when an anomaly is detected, a relevance score is assigned to the event attributes in the prefix trace. This score explains the rationale for anomaly detection. The experimental results demonstrate the method's efficacy in detecting anomalies in business processes, providing practical explanations, and enhancing the transparency and credibility of the model.