A Review on Graph Neural Network Methods in Financial Applications

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