This paper analyses a set of simple adaptations that 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 30%, and closely matching or outperforming tree-based and GNN baselines. Similarly impressive results are observed on a real-world phishing detection dataset, boosting three standard GNNs' F1 scores by around 15% and outperforming all baselines.

翻译：本文分析了一系列简单改进方法，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。这些改进包括多重图端口编号、自我标识符和反向消息传递。我们证明，这些方法的组合在理论上能够检测任何有向子图模式。为验证所提出改进方法在实际中的有效性，我们在合成子图检测任务上进行了实验，结果表现出色，几乎达到完美性能。此外，我们将所提出的改进方法应用于两项金融犯罪分析任务。在检测洗钱交易方面，我们观察到显著提升，将标准消息传递GNN的少数类F1分数提高了30%，并紧密匹配或优于基于树的基线模型和GNN基线模型。在真实的钓鱼检测数据集上也取得了同样令人印象深刻的结果，将三种标准GNN的F1分数提升约15%，并超越所有基线模型。