通过不确定性感知的迭代优化，提升图自训练的准确率与稳定性。
Uncertainty-Aware Graph Self-Training with Expectation-Maximization Regularization
- 用期望最大化框架动态调整伪标签置信度，避免错误传播。
- 在多个基准数据集上最高提升2.5%准确率，且结果更稳定。
- 适合处理噪声图结构和特征空间，尤其适用于标注稀缺场景。
本文提出一种新颖的不确定性感知图自训练方法，用于半监督节点分类。该方法引入期望最大化（EM）正则化机制，在伪标签生成与模型重训练过程中融入不确定性评估。不同于传统依赖固定伪标签的自训练流程，本方法通过受EM启发的不确定性度量，迭代优化标签置信度，使模型聚焦于可靠图区域，逐步融合模糊节点。受已有不确定性自训练工作启发，该框架能更有效地应对噪声图结构与特征空间。在多个基准图数据集上的大量实验表明，该方法相比强基线最高提升2.5%准确率，且在多次运行中表现方差更低。
原文摘要 · Abstract (English)
In this paper, we propose a novel \emph{uncertainty-aware graph self-training} approach for semi-supervised node classification. Our method introduces an Expectation-Maximization (EM) regularization scheme to incorporate an uncertainty mechanism during pseudo-label generation and model retraining. Unlike conventional graph self-training pipelines that rely on fixed pseudo-labels, our approach iteratively refines label confidences with an EM-inspired uncertainty measure. This ensures that the predictive model focuses on reliable graph regions while gradually incorporating ambiguous nodes. Inspired by prior work on uncertainty-aware self-training techniques~\cite{wang2024uncertainty}, our framework is designed to handle noisy graph structures and feature spaces more effectively. Through extensive experiments on several benchmark graph datasets, we demonstrate that our method outperforms strong baselines by a margin of up to 2.5\% in accuracy while maintaining lower variance in performance across multiple runs.
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