Zhi-Shang Luo

Looped GNNs Beyond Fixed Depth: Graph Size Generalization and Parameter-Efficient Adaptation

Generalizing from small graphs to larger ones can require computation beyond the depth of a neural model used during training. We study target tasks that admit a common local iterative computation and whose effective dependency radius grows with graph size. The central idea is to learn a local update rule through GNN p...