Select and Calibrate the Low-confidence: Dual-Channel Consistency based Graph Convolutional Networks
This work addresses a specific bottleneck in semi-supervised graph learning for node classification, offering an incremental improvement by focusing on low-confidence sample handling.
The paper tackles the problem of Graph Convolutional Networks (GCNs) performing poorly at low label rates in node classification by proposing DCC-GCN, which uses dual-channel consistency to select and calibrate low-confidence samples, resulting in significant accuracy improvements over state-of-the-art baselines across benchmark datasets.
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.