提出双向协作框架，让大模型和图神经网络互为教师，提升少样本图学习效果。
Beyond the Golden Teacher: Enhancing Graph Learning through LLM-GNN Co-teaching
- GNN与大模型轮流输出置信伪标签，双向互教，不固定谁是教师。
- 在Cora和ogbn-arxiv上3样本时准确率提升7.86%和7.73%。
- 适合少样本图学习场景，尤其对文本模糊或冷节点表现更优。
文本属性图（TAGs）广泛应用于引文网络、社交媒体和电商等领域。在少样本设置下，每类仅有少量标签且其余节点未标注，图神经网络（GNN）与大语言模型（LLM）均难以独立学习：GNN依赖拓扑结构，对冷节点失效；LLM依赖文本，对文本歧义节点失效。现有方法均采用‘黄金教师’范式，指定一个模型作为教师监督另一个，但该假设在稀疏标注下失效——任一模型都有盲区，其错误会被传递给学生。为此，本文提出无黄金教师的双向协作框架（LLM-GNN Co-Teaching），双方不固定角色，每轮交换最自信伪标签，并基于小损失准则更新。当某节点从跨模型矛盾转为一致时，其在两轮中对同一输入的回答构成偏好对，用于基于奖励的直接偏好优化（RPL-PO）。在六个基准上，该方法显著优于基线，3样本时在Cora和ogbn-arxiv上分别提升7.86%和7.73%；5样本及零样本跨数据集迁移也表现优异。误差结构分析表明，放弃黄金教师假设可显著提升大模型在困难样本上的图学习能力。
原文摘要 · Abstract (English)
Text-attributed graphs (TAGs) underlie real-world applications such as citation networks, social media, and e-commerce. Few-shot graph learning on TAGs is hard: with only a handful of labels per class and the rest of the graph unannotated, neither GNNs nor LLMs can learn well on their own. GNNs read topology and fail on cold nodes; LLMs read text and fail on text-ambiguous nodes. Existing LLM-GNN methods all follow the same recipe: designate one model as the golden teacher and use its outputs (e.g., features or pseudo-labels) to supervise the other. We argue this golden-teacher assumption breaks under sparse supervision: neither model is golden, and treating either as such transfers its blind spots into the student. We therefore ask: can we avoid designating either model as the golden teacher, and still perform effective graph learning? We answer with LLM-GNN Co-Teaching, a bidirectional co-teaching framework in which neither model is fixed as teacher. The GNN and LLM exchange their most confident pseudo-labels under an architecture-specific small-loss criterion, and both update every round. Supervision is then mined from the trajectory: whenever a node moves from cross-model contradiction at round t to cross-model agreement at round t+1, the LLM's two answers on the same input form a preference pair (old contradicting self < new peer-endorsed self) for DPO training. We call this Round-based Pseudo-Label Preference Optimization (RPL-PO). On six benchmarks, LLM-GNN Co-Teaching consistently outperforms GNN-as-Judge and all prior methods, with absolute 3-shot gains of 7.86% on Cora and 7.73% on ogbn-arxiv; improvements carry over to 5-shot and to zero-shot cross-dataset transfer. Error-structure analysis further shows that abandoning the golden-teacher assumption substantially improves the LLM's graph learning capability on challenging samples.
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