GRANDE：有向多重图上的神经网络模型及其在反洗钱中的应用
GRANDE: a neural model over directed multigraphs with application to anti-money laundering
摘要：
近年来，图表示学习技术在金融风险管理领域的应用引起了广泛的关注。然而，使用图神经模型直接建模交易网络仍然具有挑战性：首先，交易网络本质上是有向多重图，这不能用大多数现有的图神经网络（GNN）来正确处理。其次，FRM场景（如反洗钱（AML））中的一个关键问题是识别风险交易，并且最自然地被转换为具有丰富边缘级别特征的边缘分类问题，这些特征没有被遵循以节点为中心的消息传递协议的流行GNN设计充分利用。在本文中，我们提出了一个系统的研究设计方面的神经模型在有向多重图，并开发了一种新的GNN协议，克服了上述挑战，通过有效地结合方向信息，以及提出了一个增强，目标边缘相关的任务，使用一种新的消息传递方案的边缘到节点的对偶图的扩展。一个具体的GNN架构，称为GRANDE派生使用所提出的协议，有几个进一步的改进和推广的时间动态图。我们将GRANDE模型应用于现实世界的反洗钱任务和公共数据集。实验评估表明，拟议的GRANDE架构的优越性，最近的国家的最先进的模型，动态图建模和有向图建模。
英文摘要：
The application of graph representation learning techniques to the area of financial risk management (FRM) has attracted significant attention recently. However, directly modeling transaction networks using graph neural models remains challenging: Firstly, transaction networks are directed multigraphs by nature, which could not be properly handled with most of the current off-the-shelf graph neural networks (GNN). Secondly, a crucial problem in FRM scenarios like anti-money laundering (AML) is to identify risky transactions and is most naturally cast into an edge classification problem with rich edge-level features, which are not fully exploited by the prevailing GNN design that follows node-centric message passing protocols. In this paper, we present a systematic investigation of design aspects of neural models over directed multigraphs and develop a novel GNN protocol that overcomes the above challenges via efficiently incorporating directional information, as well as proposing an enhancement that targets edge-related tasks using a novel message passing scheme over an extension of edge-to-node dual graph. A concrete GNN architecture called GRANDE is derived using the proposed protocol, with several further improvements and generalizations to temporal dynamic graphs. We apply the GRANDE model to both a real-world anti-money laundering task and public datasets. Experimental evaluations show the superiority of the proposed GRANDE architecture over recent state-of-the-art models on dynamic graph modeling and directed graph modeling.
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