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CacheDyG: Decoupling Temporal Propagation for Efficient Dynamic Graph Learning
Dynamic graphs are widely used to model time-evolving relational systems in real-world applications. Dynamic graph neural networks provide an effective framework for capturing both structural dependencies and temporal dynamics in such data. However, they typically intertwine temporal graph propagati
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