  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 - another powerful model in semi-supervised
learning. 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 collapse. 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
three article classification tasks, i.e. Citeseer, Cora and Pubmed, we validate
that the proposed method matches the state of the arts in the classification
accuracy.
