AI 中文总结
研究反洗钱图中GNN处理密集邻域问题，提出SALT-GNN轻量级架构，在消息传递层融合度感知统计聚合与注意力。实验表明其在减少参数同时提升密集上下文F1分数，增益与注意力风格无关。
AI 中文摘要
洗钱威胁金融稳定并使机构面临处罚，促使自动化检测。由于洗钱方案常通过关系模式出现，图神经网络（GNN）越来越多地用于反洗钱（AML）。然而，AML GNN通常用总体F1分数等聚合指标评估，掩盖了一个操作问题：高活动接收账户集中了许多传入交易，使可疑信号更难分离且调查成本更高。我们引入了一种接收者度分层评估，报告跨接收者上下文密度的标准AML指标。在三个数据集上，它揭示了密集接收者上下文中的一致退化，我们将其追溯到三个GNN特征。在此诊断指导下，我们提出了SALT-GNN，一种轻量级的统计感知架构，在每个消息传递层将度感知统计聚合与注意力融合。消融实验支持融合位置是密集上下文性能的关键因素。在两个数据集上，SALT-GNN比特定任务的图变换器基线少用77%的参数，同时提高密集上下文F1分数3 - 6分；在另一个数据集上，它将最高度F1分数提高16 - 20分。增益对两种注意力风格都成立，表明好处来自统计和注意力证据融合的位置，而非特定注意力算子。
英文摘要
Money laundering threatens financial stability and exposes institutions to penalties, motivating automated detection. Because laundering schemes often emerge through relational patterns, graph neural networks (GNNs) are increasingly used for anti-money laundering (AML). Yet AML GNNs are typically evaluated with aggregate metrics such as overall F1 score, which hide an operational issue: high-activity recipient accounts concentrate many incoming transactions, making suspicious signals harder to isolate and costlier to investigate. We introduce a recipient-degree stratified evaluation that reports standard AML metrics across recipient-context density. Across three datasets (HI-Small, HI-Medium, and AMLSim-32k-5%), it reveals consistent degradation in dense recipient contexts, which we trace to three GNN characteristics: two known limitations that AML amplifies, i.e., (1) multiset non-discriminability and (2) cardinality blindness, and (3) an attention-specific effect: in dense neighborhoods, normalized attention attenuates weak but pattern-relevant multi-hop signals. Guided by this diagnosis, we propose SALT-GNN, a lightweight statistics-aware architecture that fuses degree-aware statistical aggregation with attention at each message-passing layer, so distributional and cardinality information shapes the node states used by subsequent attention steps. Ablations support fusion placement as a key factor in dense-context performance. On HI-Small and HI-Medium, SALT-GNN uses up to 77% fewer parameters than task-specific graph-transformer baselines while improving dense-context F1 score by 3-6 points; on AMLSim-32k-5%, it improves highest-degree F1 score by 16-20 points. The gains hold for both Transformer- and GAT-style attention, indicating that the benefit comes from where statistical and attentional evidence is fused rather than from a specific attention operator.
Comments19 pages, 7 figures. Code available upon publication
