UPL: Uncertainty-aware Pseudo-labeling for Imbalance Transductive Node Classification

TL;DR

This paper introduces UPL, an uncertainty-aware pseudo-labeling method that improves imbalanced transductive node classification by reducing pseudo-label noise and leveraging unlabeled data, outperforming existing methods.

Contribution

The paper presents a novel uncertainty-aware pseudo-labeling algorithm that addresses class imbalance in graph node classification, with theoretical risk bounds and empirical superiority.

Findings

UPL outperforms state-of-the-art methods on benchmark datasets.

Theoretical upper bound on population risk for imbalanced transductive classification.

Uncertainty-aware pseudo-labeling reduces training noise and improves accuracy.

Abstract

Graph-structured datasets often suffer from class imbalance, which complicates node classification tasks. In this work, we address this issue by first providing an upper bound on population risk for imbalanced transductive node classification. We then propose a simple and novel algorithm, Uncertainty-aware Pseudo-labeling (UPL). Our approach leverages pseudo-labels assigned to unlabeled nodes to mitigate the adverse effects of imbalance on classification accuracy. Furthermore, the UPL algorithm enhances the accuracy of pseudo-labeling by reducing training noise of pseudo-labels through a novel uncertainty-aware approach. We comprehensively evaluate the UPL algorithm across various benchmark datasets, demonstrating its superior performance compared to existing state-of-the-art methods.