This paper proposes a meta-learning method for graph data. The use of the local subgraphs provides more flexibility and allows us to adopt the same framework to different scenarios. The effectiveness of the proposed method is supported by theory and experiments. However, Meta-GNN also computes the nodes representations using a sub-graph based on the number of aggregation layers. Also, the performance improvement of the proposed method compared with Meta-GNN might come from the usage of metric learning. The paper should be revised such that the advantages of the proposed method over Meta-GNN become clearer.