  Graph neural networks (GNNs) have received much attention recently because of
their excellent performance on graph-based tasks. However, existing research on
GNNs focuses on designing more effective models without considering much about
the quality of the input data. In this paper, we propose self-enhanced GNN
(SEG), which improves the quality of the input data using the outputs of
existing GNN models for better performance on semi-supervised node
classification. As graph data consist of both topology and node labels, we
improve input data quality from both perspectives. For topology, we observe
that higher classification accuracy can be achieved when the ratio of
inter-class edges (connecting nodes from different classes) is low and propose
topology update to remove inter-class edges and add intra-class edges. For node
labels, we propose training node augmentation, which enlarges the training set
using the labels predicted by existing GNN models. SEG is a general framework
that can be easily combined with existing GNN models. Experimental results
validate that SEG consistently improves the performance of well-known GNN
models such as GCN, GAT and SGC across different datasets.
