CacheDyG: Decoupling Temporal Propagation for Efficient Dynamic Graph Learning
Experiments on five dynamic graph benchmarks show that CacheDyG adopts substantially fewer trainable parameters and lower runtime to obtain more competitive predictive performance than baselines, demonstrating that cache-based decoupling provides an effective principle for scalable dynamic graph learning.
Pin-Heng Zong, Ye Yuan
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