Dynamic graph learning is crucial for modeling real-world systems with evolving relationships and temporal dynamics. However, the lack of a unified benchmark framework in current research has led to inaccurate evaluations of dynamic graph models. This paper highlights the significance of dynamic graph learning and its applications in various domains. It emphasizes the need for a standardized benchmark framework that captures temporal dynamics, evolving graph structures, and downstream task requirements. Establishing a unified benchmark will help researchers understand the strengths and limitations of existing models, foster innovation, and advance dynamic graph learning. In conclusion, this paper identifies the lack of a standardized benchmark framework as a current limitation in dynamic graph learning research . Such a framework will facilitate accurate model evaluation, drive advancements in dynamic graph learning techniques, and enable the development of more effective models for real-world applications.

翻译：动态图学习对于建模具有演化关系和时间动态特性的现实系统至关重要。然而，当前研究领域缺乏统一的基准测试框架，导致对动态图模型的评估存在偏差。本文阐述了动态图学习的重要性及其在多个领域的应用场景，强调了构建能够捕捉时间动态、演化图结构及下游任务需求的标准化基准框架的必要性。建立统一基准框架将有助于研究者理解现有模型的优势与局限，促进创新，推动动态图学习领域发展。最终，本文指出现阶段动态图学习研究存在标准化基准框架缺失的局限性。该框架将促进模型精准评估，驱动动态图学习技术发展，从而为现实应用开发更有效的模型。