Graph neural networks (GNNs) achieve remarkable success in graph-based semi-supervised node classification, leveraging the information from neighboring nodes to improve the representation learning of target node. The success of GNNs at node classification depends on the assumption that connected nodes tend to have the same label. However, such an assumption does not always work, limiting the performance of GNNs at node classification. In this paper, we propose label-consistency based graph neural network(LC-GNN), leveraging node pairs unconnected but with the same labels to enlarge the receptive field of nodes in GNNs. Experiments on benchmark datasets demonstrate the proposed LC-GNN outperforms traditional GNNs in graph-based semi-supervised node classification.We further show the superiority of LC-GNN in sparse scenarios with only a handful of labeled nodes.

本文提出了基于标签一致性的图神经网络（LC-GNN），扩大了GNN中的节点感受野，利用未连接但标签相同的节点对。在基准数据集上的实验证明，LC-GNN在图形半监督节点分类方面优于传统的GNN，在只有少数标记节点的稀疏场景中也表现出优越性。

基于标签一致性的图神经网络用于半监督节点分类