412604be30f701b1b1e3124c252065e6-AuthorFeedback.pdf

We thank the reviewers for their time and valuable feedback. "scalability is particularly appealing", "theoretical analysis is great, substantial, valid, correct", experiments are We believe these clarifications, together with our new analyses, resolve all key issues raised. As suggested by reviewers, we will carefully discuss this in the final version. We are not claiming novelty in "the idea of using local subgraphs to compute node GNN on an entire graph together with MAML, performs 42.5% worse than G-M's variant is that it uses the's performance can vary with local subgraph size We will include this analysis in our final version. Empirically, we find the subgraph construction takes 14.7% of training time, and this can be's comment of "evaluating each node label individually", we note that each mini-batch consists We will include the full study in the final version. Disjoint Labels" problem, each task defines an N -size label set, and samples K nodes for each label N