How can GNNs be effectively regularized?

Example Answer

I would regularize a GNN according to the failure mode. Feature dropout removes selected feature components during training; it is different from removing nodes or edges. Weight decay can limit parameter growth, and early stopping can prevent continued fitting after validation performance stops improving.

Edge dropout or DropEdge changes the message-passing graph. That can help some models, but it can also remove the only informative relation for a rare node or disconnect an important structure. I would preserve required self-loops and relation semantics and validate the effect by node degree or task slice. Attention is an aggregation mechanism, not inherently a regularizer.

I would also distinguish an L2 penalty added to the loss from decoupled weight decay in an adaptive optimizer; they are not generally identical. Finally, regularization cannot repair leakage. For link prediction, held-out target edges must be excluded from the training message graph where the evaluation protocol requires it, and negative examples must not silently overlap positives.

Follow-up

If dropping edges helps the average metric, does it also help isolated and low-degree nodes, or only well-connected ones?

DropEdge · Decoupled weight decay