Provably Powerful Graph Neural Networks for Directed Multigraphs

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.