Robust Semi-supervised Representation Learning for Graph-Structured Data

The success of machine learning algorithms generally depends on data representation and recently many representation learning methods have been proposed. However, learning a good representation may not always benefit the classification tasks. It sometimes even hurt the performance as the learned representation maybe not related to the ultimate tasks, especially when the labeled examples are few to afford a reliable model selection. In this paper, we propose a novel robust semi-supervised graph representation learning method based on graph convolutional network. To make the learned representation more related to the ultimate classification task, we propose to extend label information based on the smooth assumption and obtain pseudo-labels for unlabeled nodes. Moreover, to make the model robust with noise in the pseudo-label, we propose to apply a large margin classifier to the learned representation. Influenced by the pseudo-label and the large-margin principle, the learned representation can not only exploit the label information encoded in the graph-structure sufficiently but also can produce a more rigorous decision boundary. Experiments demonstrate the superior performance of the proposal over many related methods.