[论文解读] LineMVGNN: Anti-Money Laundering with Line-Graph-Assisted Multi-View Graph Neural Networks

Chung-Hoo Poon, James Kwok+2 more|arXiv (Cornell University)|Mar 24, 2026

Crime, Illicit Activities, and Governance被引用 0

一句话总结

LineMVGNN 引入线-图辅助的多视图GNN以处理有向交易图，通过传播边级资金流信息并整合入边/出边的消息来提升AML检测。

ABSTRACT

Anti-money laundering (AML) systems are important for protecting the global economy. However, conventional rule-based methods rely on domain knowledge, leading to suboptimal accuracy and a lack of scalability. Graph neural networks (GNNs) for digraphs (directed graphs) can be applied to transaction graphs and capture suspicious transactions or accounts. However, most spectral GNNs do not naturally support multi-dimensional edge features, lack interpretability due to edge modifications, and have limited scalability owing to their spectral nature. Conversely, most spatial methods may not capture the money flow well. Therefore, in this work, we propose LineMVGNN (Line-Graph-Assisted Multi-View Graph Neural Network), a novel spatial method that considers payment and receipt transactions. Specifically, the LineMVGNN model extends a lightweight MVGNN module, which performs two-way message passing between nodes in a transaction graph. Additionally, LineMVGNN incorporates a line graph view of the original transaction graph to enhance the propagation of transaction information. We conduct experiments on two real-world account-based transaction datasets: the Ethereum phishing transaction network dataset and a financial payment transaction dataset from one of our industry partners. The results show that our proposed method outperforms state-of-the-art methods, reflecting the effectiveness of money laundering detection with line-graph-assisted multi-view graph learning. We also discuss scalability, adversarial robustness, and regulatory considerations of our proposed method.

研究动机与目标

• 将AML任务动作为基于图的有向账户交易图的欺诈检测。
• 开发一个轻量级的多视图GNN（MVGNN），同时利用入边与出边信息及边特征。
• 引入线-图视图以增强边特征传播和资金流建模。
• 在真实世界的以太坊钓鱼与金融支付数据集上评估LineMVGNN，并与最先进基线进行比较。

提出的方法

• 提出MVGNN：一种两路消息传递机制，使用共享聚合映射在入边和出边之间进行聚合。
• 在MVGNN基础上扩展线-图视图，在节点更新前传播边特征，采用类似交叉缝合（cross-stitch）的更新方式。
• 两种变体：LineMVGNN-add（加权和融合）和LineMVGNN-cat（连接后通过线性层融合）以结合入边和出边的消息。
• 使用个性化PageRank灵感的聚合来结合多层嵌入并缓解过平滑（over-smoothing）。
• 给出两种实现方案：（i）标准LineMVGNN，显式线-图传播；（ii）无需显式线-图构建的 refined LineMVGNN，用于线性时间的边中心传播。

实验结果

研究问题

• RQ1 线-图基础的边传播是否能在有向交易图中比以节点为中心的GNN更有效检测不法账户？
• RQ2 在真实数据集上同时结合入边与出边消息并结合边特征，是否能提升AML性能？
• RQ3 LineMVGNN 的变体（add vs cat）在精度、对超参数的鲁棒性，以及有/无结构特征数据条件下的表现如何？

主要发现

• LineMVGNN-cat 在所有评估数据集上始终达到最先进的不法类别F1分数，在ETH-Small、ETH-Large、FPT以及有/无SNF条件下均优于基线。
• 在消融实验中，去除线-图视图或两路消息传递都会降低不法类别F1，说明两者的有效性。
• 具备共享参数的MVGNN变体可匹配或超越Dir-GNN基线，同时提供更好的效率。
• LineMVGNN-cat 对嵌入维度和SNF可用性的敏感性较低，在某些设置下，在FPT数据集无SNF时也可实现接近或超过0.99的不法类别F1。