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：本文分析了一组简单改进，将标准消息传递图神经网络（GNN）转化为可证强力的有向多重图神经网络。这些改进包括多重图端口编号、自标识符和反向消息传递。我们证明这些改进的组合在理论上能够检测任意有向子图模式。为验证所提改进在实际中的有效性，我们在合成子图检测任务上进行了实验，结果表明性能卓越，几乎达到完美结果。此外，我们将所提改进应用于两项金融犯罪分析任务。观察发现，在检测洗钱交易方面取得了显著提升，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方法和GNN基线方法。在真实世界的钓鱼检测数据集上也取得了类似令人印象深刻的结果，将三种标准GNN的F1分数提升了约15%，并优于所有基线方法。