Review for NeurIPS paper: Graph Meta Learning via Local Subgraphs

Weaknesses: - The main weakness of the work relates to the computational complexity of 1) computing the local subgraphs (are shortest paths computed ahead of the training process?), 2) evaluating each node's label individually. Can authors comment on the impact on training/evaluation time? - Another important missing element from the paper is the value of neighborhood size h, as well as an analysis of its influence over the model's performance. This is the key parameter of the proposed strategy and providing readers with intuitive knowledge of the value of h to use, and the robustness of the method with respect to larger or smaller neighborhoods is essential. Similarly, different hyperparameter sets are used per dataset, which is not ideal. Can authors provide insights into how performance varies with a constant set of parameters? - Certain aspects of the training set-up needs clarifying.