Review for NeurIPS paper: Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network Embedding

The overall novelty of the proposed model is limited to some extend. I think this module is very similar to meta-GNN. Both of them conduct adaptation on the support set, then do evaluation on the query set, though they employ prototype and MAML respectively. In my view, the overall model stands on the shoulder on some traditional approaches, and seems a bit incremental. Could some other approaches, such as fine-tune (which is often utilized as the comparison with meta-learning), solve this novel label problem?