Temporal Graph Learning (TGL) is crucial for capturing the evolving nature of stock markets. Traditional methods often ignore the interplay between dynamic temporal changes and static relational structures between stocks. To address this issue, we propose the Dynamic Graph Representation with Contrastive Learning (DGRCL) framework, which integrates dynamic and static graph relations to improve the accuracy of stock trend prediction. Our framework introduces two key components: the Embedding Enhancement (EE) module and the Contrastive Constrained Training (CCT) module. The EE module focuses on dynamically capturing the temporal evolution of stock data, while the CCT module enforces static constraints based on stock relations, refined within contrastive learning. This dual-relation approach allows for a more comprehensive understanding of stock market dynamics. Our experiments on two major U.S. stock market datasets, NASDAQ and NYSE, demonstrate that DGRCL significantly outperforms state-of-the-art TGL baselines. Ablation studies indicate the importance of both modules. Overall, DGRCL not only enhances prediction ability but also provides a robust framework for integrating temporal and relational data in dynamic graphs. Code and data are available for public access.

翻译：时序图学习（TGL）对于捕捉股票市场的演化特性至关重要。传统方法往往忽略动态时序变化与股票间静态关系结构之间的相互作用。为解决此问题，我们提出了基于对比学习的动态图表示（DGRCL）框架，该框架整合了动态与静态图关系，以提高股票趋势预测的准确性。我们的框架引入了两个关键组件：嵌入增强（EE）模块和对比约束训练（CCT）模块。EE模块专注于动态捕捉股票数据的时序演化，而CCT模块则在对比学习框架内，基于股票关系施加静态约束。这种双重关系方法能够实现对股票市场动态更全面的理解。我们在纳斯达克（NASDAQ）和纽约证券交易所（NYSE）这两个主要美国股票市场数据集上的实验表明，DGRCL显著优于最先进的TGL基线方法。消融研究证实了两个模块的重要性。总体而言，DGRCL不仅提升了预测能力，还为在动态图中整合时序与关系数据提供了一个鲁棒的框架。代码与数据已公开提供。