Graph-Based Uncertainty-Aware Self-Training with Stochastic Node Labeling

• URL: http://arxiv.org/abs/2503.22745v1
• Date: Wed, 26 Mar 2025 21:54:19 GMT
• Title: Graph-Based Uncertainty-Aware Self-Training with Stochastic Node Labeling
• Authors: Tom Liu, Anna Wu, Chao Li,
• Abstract summary: We propose a novel emphgraph-based uncertainty-aware self-training (GUST) framework to combat over-confidence in node classification.<n>Our method largely diverges from previous self-training approaches by focusing on emphstochastic node labeling grounded in the graph topology.<n> Experimental results on several benchmark graph datasets demonstrate that our GUST framework achieves state-of-the-art performance.
• Score: 2.600103729157093
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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