DGDNN separates propagation from transformation to make relational mixing more controllable in dynamic stock graphs.
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DGDNN
Decoupled graph diffusion for multi-stock movement prediction.
Problem
Stock relationships and temporal representations can become entangled when graph propagation and feature transformation are learned together.
Approach
Decouple graph diffusion from representation transformation to control how relational information is mixed.
Role
First author and implementation author