[2603.23584] LineMVGNN: Anti-Money Laundering with Line-Graph-Assisted Multi-View Graph Neural Networks

Computer Science > Machine Learning arXiv:2603.23584 (cs) [Submitted on 24 Mar 2026] Title:LineMVGNN: Anti-Money Laundering with Line-Graph-Assisted Multi-View Graph Neural Networks Authors:Chung-Hoo Poon, James Kwok, Calvin Chow, Jang-Hyeon Choi View a PDF of the paper titled LineMVGNN: Anti-Money Laundering with Line-Graph-Assisted Multi-View Graph Neural Networks, by Chung-Hoo Poon and 3 other authors View PDF HTML (experimental) 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 propa...