We consider a family of problems that are concerned about making predictions for the majority of unlabeled, graph-structured data samples based on a small proportion of labeled samples. Relational information among the data samples, often encoded in the graph/network structure, is shown to be helpful for these semi-supervisedlearningtasks.

Let θ and φ denote the optimal parameters after model training. The detailed statistics of three datasets used in this paper are listed in Table 1. In this paper, when evaluating the performance in the standard experimental scenario and in the label-scarce scenario, we compare with six state-of-the-art baselines used for graph-based semisupervised learning. Three of them are deterministic GNN-based models, which are GCN [1], Graph Attention Networks(GAT)[2]andGraphSAGE[3]respectively.

Graph Neural Networks (GNNs) have achieved remarkable performance in the task of the semi-supervised node classification. However,most existing models learn a deterministic classification function, which lack sufficient flexibility to explore better choices in the presence of kinds of imperfect observed data such as the scarce labeled nodes and noisy graph structure.