用图结构动态标注不确定节点，提升少样本场景下的分类准确率
Graph-Based Uncertainty-Aware Self-Training with Stochastic Node Labeling
- 基于图拓扑设计随机节点标注策略，结合不确定性估计生成更可靠的伪标签
- 在标签极少的场景下，相比基线模型提升6.2%~11.8%的准确率
- 适合处理标注数据稀缺的图神经网络任务，如社交网络、知识图谱
自训练已成为利用无标签数据的主流半监督学习方法，但伪标签的过度自信仍是关键挑战。本文提出一种基于图的不确定性感知自训练框架（GUST），以缓解节点分类中的过自信问题。受Wang等人提出的不确定性融合思想启发，本方法通过图拓扑构建随机节点标注机制，引入贝叶斯启发的模块估计节点级不确定性，并通过类似期望最大化（EM）的步骤将不确定性融入伪标签生成过程，同时迭代更新节点嵌入与邻接关系变换。在多个基准图数据集上的实验表明，该框架在标签极度稀疏的情况下达到当前最优性能，显著优于现有方法。
原文摘要 · Abstract (English)
Self-training has become a popular semi-supervised learning technique for leveraging unlabeled data. However, the over-confidence of pseudo-labels remains a key challenge. In this paper, we propose a novel \emph{graph-based uncertainty-aware self-training} (GUST) framework to combat over-confidence in node classification. Drawing inspiration from the uncertainty integration idea introduced by Wang \emph{et al.}~\cite{wang2024uncertainty}, our method largely diverges from previous self-training approaches by focusing on \emph{stochastic node labeling} grounded in the graph topology. Specifically, we deploy a Bayesian-inspired module to estimate node-level uncertainty, incorporate these estimates into the pseudo-label generation process via an expectation-maximization (EM)-like step, and iteratively update both node embeddings and adjacency-based transformations. Experimental results on several benchmark graph datasets demonstrate that our GUST framework achieves state-of-the-art performance, especially in settings where labeled data is extremely sparse.
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