This paper proposes a set of simple adaptations to transform standard
message-passing Graph Neural Networks (GNN) into provably powerful directed
multigraph neural networks. The adaptations include multigraph port numbering,
ego IDs, and reverse message passing. We prove that the combination of these
theoretically enables the detection of any directed subgraph pattern. To
validate the effectiveness of our proposed adaptations in practice, we conduct
experiments on synthetic subgraph detection tasks, which demonstrate
outstanding performance with almost perfect results.
Moreover, we apply our proposed adaptations to two financial crime analysis
tasks. We observe dramatic improvements in detecting money laundering
transactions, improving the minority-class F1 score of a standard
message-passing GNN by up to 45%, and clearly outperforming tree-based and GNN
baselines. Similarly impressive results are observed on a real-world phishing
detection dataset, boosting a standard GNN's F1 score by over 15% and
outperforming all baselines.

本文提出了一系列简单的改进方法，将标准消息传递图神经网络（GNN）转化为可证明强大的有向多重图神经网络，并将其应用于金融犯罪分析和网络钓鱼检测等任务中，得出了极好的实验结果。