A Safe Semi-supervised Graph Convolution Network
This addresses a specific bottleneck in graph-based semi-supervised learning for non-Euclidean data, but it is incremental as it builds on existing GCN methods.
The paper tackled the problem of Graph Convolution Networks (GCNs) degrading performance when using risk unlabeled data in semi-supervised learning, proposing a Safe-GCN framework that iteratively labels high-confidence unlabeled data to safely incorporate them, achieving improved results on citation network datasets.
In the semi-supervised learning field, Graph Convolution Network (GCN), as a variant model of GNN, has achieved promising results for non-Euclidean data by introducing convolution into GNN. However, GCN and its variant models fail to safely use the information of risk unlabeled data, which will degrade the performance of semi-supervised learning. Therefore, we propose a Safe GCN framework (Safe-GCN) to improve the learning performance. In the Safe-GCN, we design an iterative process to label the unlabeled data. In each iteration, a GCN and its supervised version(S-GCN) are learned to find the unlabeled data with high confidence. The high-confidence unlabeled data and their pseudo labels are then added to the label set. Finally, both added unlabeled data and labeled ones are used to train a S-GCN which can achieve the safe exploration of the risk unlabeled data and enable safe use of large numbers of unlabeled data. The performance of Safe-GCN is evaluated on three well-known citation network datasets and the obtained results demonstrate the effectiveness of the proposed framework over several graph-based semi-supervised learning methods.