Fig. 4

The algorithm flow chart of GCN based on MAML: (1) extracting local graphs according to the neighbor nodes; (2) dividing the local graphs into three datasets (training, validation and testing) and constructing tasks for each dataset; (3) feeding a batch of k support sets into GCN to get k θ and calculating the θ’ based on k query sets and θ’; (4) using the support set of the testing set to fine-tune the GCN with meta-parameter θ’ as the initial parameter and the query set to evaluate the model’s performance. In addition, the validation set is also used to adjust the hyperparameters in this step