Who Should Teach? Confidence-Aware Dual-Teacher Learning for Few-Shot Node Classification on Text-Attributed Graphs

2026-08-22

AI Summary

针对文本属性图上的少样本节点分类问题，提出了一种基于置信度的双教师学习框架CoTeach，动态选择更可靠的教师以提高性能并降低成本。

Abstract

Text-Attributed Graphs (TAGs) integrate graph structures and node-associated textual attributes, and recent studies have increasingly leveraged Large Language Models (LLMs) to improve TAG learning in few-shot settings. However, existing approaches typically utilize LLM-derived information uniformly across all nodes, despite substantial variations in its reliability, while also incurring considerable monetary costs. We argue that the most appropriate source of supervision may differ across nodes, as Graph Neural Networks (GNNs) and LLMs exhibit complementary strengths in exploiting structural and semantic information, respectively. To this end, we propose CoTeach, a Confidence-aware dual-teacher learning framework that dynamically selects the more reliable teacher for each node. Experimental results demonstrate that CoTeach consistently improves few-shot node classification performance while reducing unnecessary LLM utilization and associated monetary costs.

Problem

• Few-Shot Node Classification
• Text-Attributed Graphs
• Large Language Models
• Graph Neural Networks

Innovation

• Confidence-aware
• Dual-teacher Learning
• Few-shot Node Classification
• Text-Attributed Graphs