The effectiveness of Graph Convolutional Networks (GCNs) has been demonstrated in a wide range of graph-based machine learning tasks. However, the update of parameters in GCNs is only from labeled nodes, lacking the utilization of unlabeled data. In this paper, we apply Virtual Adversarial Training (VAT), an adversarial regularization method based on both labeled and unlabeled data, on the supervised loss of GCN to enhance its generalization performance. By imposing virtually adversarial smoothness on the posterior distribution in semi-supervised learning, VAT yields improvement on the Symmetrical Laplacian Smoothness of GCNs. In addition, due to the difference of property in features, we perturb virtual adversarial perturbations on sparse and dense features, resulting in GCN Sparse VAT (GCNSVAT) and GCN Dense VAT (GCNDVAT) algorithms, respectively. Extensive experiments verify the effectiveness of our two methods across different training sizes. Our work paves the way towards better understanding the direction of improvement on GCNs in the future.

本文提出了一种GCN半监督学习的虚拟对抗训练方法，并通过GCN Sparse VAT（GCNSVAT）和GCN Dense VAT（GCNDVAT）算法在标记和未标记数据上进行优化，提高其泛化性能。该方法在GCN的Symmetrical Laplacian Smoothness上取得了改进，并通过大量实验证实了我们方法的有效性。

节点分类中基于图卷积网络的虚拟对抗训练