  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.
