Temporal Graph Learning (TGL) has become a prevalent technique across diverse real-world applications, especially in domains where data can be represented as a graph and evolves over time. Although TGL has recently seen notable progress in algorithmic solutions, its theoretical foundations remain largely unexplored. This paper aims at bridging this gap by investigating the generalization ability of different TGL algorithms (e.g., GNN-based, RNN-based, and memory-based methods) under the finite-wide over-parameterized regime. We establish the connection between the generalization error of TGL algorithms and "the number of layers/steps" in the GNN-/RNN-based TGL methods and "the feature-label alignment (FLA) score", where FLA can be used as a proxy for the expressive power and explains the performance of memory-based methods. Guided by our theoretical analysis, we propose Simplified-Temporal-Graph-Network, which enjoys a small generalization error, improved overall performance, and lower model complexity. Extensive experiments on real-world datasets demonstrate the effectiveness of our method. Our theoretical findings and proposed algorithm offer essential insights into TGL from a theoretical standpoint, laying the groundwork for the designing practical TGL algorithms in future studies.

翻译：时序图学习（Temporal Graph Learning, TGL）已成为广泛应用于各种现实场景的技术，尤其是在数据可表示为图并随时间演化的领域。尽管TGL在算法解决方案上近期取得了显著进展，但其理论基础仍基本未被探索。本文旨在填补这一空白，研究在有限宽度过参数化条件下不同TGL算法（如基于GNN、基于RNN和基于记忆的方法）的泛化能力。我们建立了TGL算法的泛化误差与“GNN/RNN基础TGL方法中的层数/步数”以及“特征-标签对齐（FLA）分数”之间的联系，其中FLA可作为表达能力的代理指标，并解释基于记忆方法的性能。在我们的理论分析指导下，我们提出了简化时序图网络（Simplified-Temporal-Graph-Network），该网络具有较小的泛化误差、改进的整体性能和较低的模型复杂度。在真实世界数据集上的大量实验证明了我们方法的有效性。我们的理论发现和所提算法从理论角度为TGL提供了重要见解，为未来设计实用的TGL算法奠定了基础。