Every node counts: Self-ensembling graph convolutional networks for semi-supervised learning

Abstract

Graph convolutional network (GCN) provides a powerful means for graph-based semi-supervised tasks. However, as a localized first-order approximation of spectral graph convolution, the classic GCN can not take full advantage of unlabeled data, especially when the unlabeled node is far from labeled ones. To capitalize on the information from unlabeled nodes to boost the training for GCN, we propose a novel framework named Self-Ensembling GCN (SEGCN), which marries GCN with Mean Teacher – a powerful self-ensemble learning mechanism for semi-supervised task. SEGCN contains a student model and a teacher model. As a student, it not only learns to correctly classify the labeled nodes, but also tries to be consistent with the teacher on unlabeled nodes in more challenging situations, such as a high dropout rate and graph corrosion. As a teacher, it averages the student model weights and generates more accurate predictions to lead the student. In such a mutual-promoting process, both labeled and unlabeled samples can be fully utilized for backpropagating effective gradients to train GCN. In a variety of semi-supervised classification benchmarks, i.e. Citeseer, Cora, Pubmed and NELL, we validate that the proposed method matches the state of the arts in the classification accuracy. The code is publicly available at https://github.com/RoyalVane/SEGCN.