Multiplex Experts Governance Collaboration for Label Noise-Resistant Graph Representation Learning

Abstract

Recently emerged label noise-resistant graph representation learning (LNR-GRL) has received increasing attention, which aims to enhance the generalization of graph neural networks (GNNs) in semi-supervised node classification with noisy and limited labels. Most of the existing LNR-GRL tend to introduce more complex sample selection strategies developed in nongraph areas to distinguish more noisy nodes to alleviate their misguidance. However, these proposed methods neglect the importance of inaccurate graph structure relationships rectification, and information collaboration between inaccurate graph structure relationships and noisy node label rectification in improving the quality of noisy node identification and its rectified node labels. To solve the above-mentioned issues, we propose a novel multiplex experts governance collaboration (MEGC) framework for LNR-GRL. Specifically, an unsupervised graph structure governance expert is first designed to rectify inaccurate graph structure relationships. Based on the rectified graph structure, a simple label noise governance expert is proposed to accurately identify noisy node labels and further improve the quality of noisy nodes’ rectified labels and unlabeled nodes’ pseudo-labels. Finally, the above-proposed governance experts can be effectively combined with GNNs to jointly guide their training via the introduced cross-view graph contrastive loss and cross-entropy loss, which can maximally limit the effect of noisy node labels and discover more effective supervision guidance from data itself for GNNs optimization. Extensive experiments on three benchmarks, two label noise types, four noise rates, and four training label rates demonstrate the superiority of the proposed method in comparison to the existing LNR-GRL methods.