Semi-supervised Learning on Graphs Using Adversarial Training with Generated Sample

Abstract

Given a graph composed of a small number of labeled nodes and a large number of unlabeled nodes, semi-supervised learning on graphs aims to assign labels for the unlabeled nodes. Generative adversarial networks have shown strong ability in semi-supervised learning, but the research of generative adversarial networks for semi-supervised learning on graphs is few. The current work mainly focuses on the generation of unlabeled samples in low-density regions to weaken the information transmission between subgraphs, so as to make the decision boundary clearer. However, in this kind of methods, too few labeled samples is still the main challenge. This paper  proposes a semi-supervised learning algorithm on graphs using adversarial training with generated sample. The algorithm is based on generative adversarial networks, which generates the labeled samples from the real sample distribution and the unlabeled samples different from the real sample distribution. The generated labeled samples expand the supervised information, while the generated unlabeled samples reduce the influence of neighboring nodes in the density gap, thus improving the semi-supervised classification effect on graphs. Compared with the existing methods, the proposed algorithm fully considers the effects of labeled samples and unlabeled samples on graph-based semi-supervised learning, which makes its classification ability stronger. Meanwhile, a large number of experiments are carried out on different datasets to verify the effectiveness of the method.

Keywords

• semi-supervised learning on graphs; generative adversarial networks (gan); adversarial training; ge-nerated sample; graph embedding