Graph out-of-distribution (OOD) generalization remains a major challenge in graph neural networks (GNNs). Invariant learning, aiming to extract invariant features across varied distributions, has ...
Sub-headline: Researchers from BUPT introduce the RFGDG framework,utilizing RL to dynamically optimize graph generalization in federated settings. Graph Neural Networks(GNNs)have become pivotal in ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results