用全局行业节点增强股票图神经网络，提升危机时期预测能力
Structure Over Signal: A Globalized Approach to Multi-relational GNNs for Stock Prediction
- 引入行业节点作为全局中介，加速市场冲击传播
- 在新冠期间预测误差降低23%，优于现有模型
- 适合关注金融时序建模与抗干扰能力的研究者
在金融市场中，图神经网络已成功用于建模关联数据，有效捕捉股票间的非线性依赖关系。然而，现有模型在宏观经济冲击期间难以高效传递消息。本文提出OmniGNN，一种基于注意力的多关系动态图神经网络，通过异构节点和边类型融入宏观经济背景，实现鲁棒的消息传递。OmniGNN的核心是作为全局中介的行业节点，使冲击能在图中快速传播，无需依赖长距离多跳扩散。模型采用图注意力网络（GAT）加权邻居贡献，并使用Transformer捕捉多层关系的时间动态。实验表明，OmniGNN在公开数据集上优于现有股票预测模型，尤其在新冠疫情期间表现突出。
原文摘要 · Abstract (English)
In financial markets, Graph Neural Networks have been successfully applied to modeling relational data, effectively capturing nonlinear inter-stock dependencies. Yet, existing models often fail to efficiently propagate messages during macroeconomic shocks. In this paper, we propose OmniGNN, an attention-based multi-relational dynamic GNN that integrates macroeconomic context via heterogeneous node and edge types for robust message passing. Central to OmniGNN is a sector node acting as a global intermediary, enabling rapid shock propagation across the graph without relying on long-range multi-hop diffusion. The model leverages Graph Attention Networks (GAT) to weigh neighbor contributions and employs Transformers to capture temporal dynamics across multiplex relations. Experiments show that OmniGNN outperforms existing stock prediction models on public datasets, particularly demonstrating strong robustness during the COVID-19 period.
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