  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.
