用图神经网络分析流程图，提升复杂流程预测能力
Directly Follows Graphs Go Predictive Process Monitoring With Graph Neural Networks
- 将流程转化为直接跟随图，用图神经网络建模
- 针对不同图结构设计适配的GNN架构，减少信息损失
- 适合处理长流程与循环密集的复杂业务场景
近年来，基于人工神经网络的预测式流程监控（PPM）已成为监控业务流程未来行为的有效方法。现有方法多将流程视为序列（即轨迹），输入专为序列数据设计的神经网络（如RNN或Transformer）。本研究探索一种替代方案：将每个流程转换为直接跟随图（DFG）表示，并应用图神经网络（GNN）进行预测任务。该方法旨在构建更适用于长流程且包含大量循环的复杂流程的模型。我们提出了多种根据所用GNN类型定制的DFG表示方法，测试了从传统节点型到新型边型架构的各类GNN。此外，还研究了多图表示的可能性。通过这些步骤，我们致力于设计能最小化轨迹转图时信息损失的图表示。
原文摘要 · Abstract (English)
In the past years, predictive process monitoring (PPM) techniques based on artificial neural networks have evolved as a method to monitor the future behavior of business processes. Existing approaches mostly focus on interpreting the processes as sequences, so-called traces, and feeding them to neural architectures designed to operate on sequential data such as recurrent neural networks (RNNs) or transformers. In this study, we investigate an alternative way to perform PPM: by transforming each process in its directly-follows-graph (DFG) representation we are able to apply graph neural networks (GNNs) for the prediction tasks. By this, we aim to develop models that are more suitable for complex processes that are long and contain an abundance of loops. In particular, we present different ways to create DFG representations depending on the particular GNN we use. The tested GNNs range from classical node-based to novel edge-based architectures. Further, we investigate the possibility of using multi-graphs. By these steps, we aim to design graph representations that minimize the information loss when transforming traces into graphs.
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