Technological advancements in cryptocurrency markets have increased accessibility for investors, but concurrently exposed them to the risks of market manipulations. Existing fraud detection mechanisms typically rely on machine learning methods that treat each financial asset (i.e., token) and its related transactions independently. However, market manipulation strategies are rarely isolated events, but are rather characterized by coordination, repetition, and frequent transfers among related assets. This suggests that relational structure constitutes an integral component of the signal and can be effectively represented through graphical means. In this paper, we propose three graph construction methods that rely on aggregated hourly market data. The proposed graphs are processed by a unified spatio-temporal Graph Neural Network (GNN) architecture that combines attention-based spatial aggregation with temporal Transformer encoding. We evaluate our methodology on a real-world dataset comprised of pump-and-dump schemes in cryptocurrency markets, spanning a period of over three years. Our comparative results showcase that our graph-based models achieve significant improvements over standard machine learning baselines in detecting anomalous events. Our work highlights that learned market connectivity provides substantial gains for detecting coordinated market manipulation schemes.

翻译：加密货币市场的技术进步提高了投资者的可及性，但同时也使他们面临市场操纵的风险。现有的欺诈检测机制通常依赖机器学习方法，将每个金融资产（即代币）及其相关交易视为独立个体进行处理。然而，市场操纵策略极少是孤立事件，而是以协调性、重复性以及相关资产间的频繁转移为特征。这表明关系结构构成了信号的重要组成部分，可以通过图形手段有效表示。在本文中，我们提出了三种基于聚合小时市场数据的图构建方法。所构建的图由统一的时空图神经网络（GNN）架构处理，该架构结合了基于注意力的空间聚合与时序Transformer编码。我们在一个包含超过三年加密货币市场拉高出货方案的真实数据集上评估了我们的方法。对比结果表明，我们的基于图的模型在检测异常事件方面显著优于标准机器学习基线。我们的工作强调了所学到的市场连接性在检测协调市场操纵方案方面提供了实质性收益。