Xiaoran Wei

LEMD: Latent Environment Extrapolation and Message Disentanglement for Dynamic Graph Under Distribution Shift

Dynamic graph neural networks (DyGNNs) are widely used to model evolving interactions, but may fail under data distribution shift. Due to limited and unreliable interventions and insufficient disentanglement, the existing dynamic graph domain generalization approaches lead to suboptimal results. We