Mutual Teaching for Graph Convolutional Networks

TL;DR

This paper introduces a mutual teaching approach for graph convolutional networks where two models teach each other using high-confidence pseudo labels, significantly improving performance in low-label scenarios.

Contribution

The paper proposes a novel mutual teaching training method for GCNs that enhances label propagation and model performance with minimal labeled data.

Findings

Achieves superior performance over state-of-the-art methods.

Effective in very low label rate settings.

Significant performance improvements demonstrated.

Abstract

Graph convolutional networks produce good predictions of unlabeled samples due to its transductive label propagation. Since samples have different predicted confidences, we take high-confidence predictions as pseudo labels to expand the label set so that more samples are selected for updating models. We propose a new training method named as mutual teaching, i.e., we train dual models and let them teach each other during each batch. First, each network feeds forward all samples and selects samples with high-confidence predictions. Second, each model is updated by samples selected by its peer network. We view the high-confidence predictions as useful knowledge, and the useful knowledge of one network teaches the peer network with model updating in each batch. In mutual teaching, the pseudo-label set of a network is from its peer network. Since we use the new strategy of network training, performance improves significantly. Extensive experimental results demonstrate that our method achieves superior performance over state-of-the-art methods under very low label rates.