  The Graph Convolutional Networks (GCNs) have achieved excellent results in
node classification tasks, but the model's performance at low label rates is
still unsatisfactory. Previous studies in Semi-Supervised Learning (SSL) for
graph have focused on using network predictions to generate soft pseudo-labels
or instructing message propagation, which inevitably contains the incorrect
prediction due to the over-confident in the predictions. Our proposed
Dual-Channel Consistency based Graph Convolutional Networks (DCC-GCN) uses
dual-channel to extract embeddings from node features and topological
structures, and then achieves reliable low-confidence and high-confidence
samples selection based on dual-channel consistency. We further confirmed that
the low-confidence samples obtained based on dual-channel consistency were low
in accuracy, constraining the model's performance. Unlike previous studies
ignoring low-confidence samples, we calibrate the feature embeddings of the
low-confidence samples by using the neighborhood's high-confidence samples. Our
experiments have shown that the DCC-GCN can more accurately distinguish between
low-confidence and high-confidence samples, and can also significantly improve
the accuracy of low-confidence samples. We conducted extensive experiments on
the benchmark datasets and demonstrated that DCC-GCN is significantly better
than state-of-the-art baselines at different label rates.
