With multiple components and relations, financial data are often presented as graph data, since it could represent both the individual features and the complicated relations. Due to the complexity and volatility of the financial market, the graph constructed on the financial data is often heterogeneous or time-varying, which imposes challenges on modeling technology. Among the graph modeling technologies, graph neural network (GNN) models are able to handle the complex graph structure and achieve great performance and thus could be used to solve financial tasks. In this work, we provide a comprehensive review of GNN models in recent financial context. We first categorize the commonly-used financial graphs and summarize the feature processing step for each node. Then we summarize the GNN methodology for each graph type, application in each area, and propose some potential research areas.

翻译：由于金融市场的复杂性和波动性,金融数据所构建的图表往往具有多样性或时间差异,给建模技术带来了挑战。在图形模型技术中,图形神经网络模型能够处理复杂的图形结构,取得巨大的性能,从而可用于解决财务任务。在这项工作中,我们提供了对近期金融背景下通用的GNN模型的全面审查。我们首先对常用的财务图表进行了分类,并概述了每个节点的特征处理步骤。然后我们总结了每个图表类型、每个领域的应用、以及一些潜在的研究领域采用GNN方法。