MTKD-RL: Multi-Teacher Knowledge Distillation Method for Reinforcement Learning Based on Few-Shot Node Classification

Few-shot node classification aims to effectively predict new class nodes using only a small number of labeled samples, which is an important research direction in graph data mining. Existing self-training-based few-shot node classification methods are constrained by the bias and local optima of a single-teacher model. Meanwhile, multiple-teacher models often suffer from knowledge conflicts and redundant information, which degrade distillation efficiency and model generalization performance. To address these issues, we propose a multi-teacher knowledge distillation method with reinforcement learning for few-shot node classification (MTKD-RL). This framework is composed of a multi-teacher distillation network and a multi-teacher weight optimization module to deliver complementary supervision information from multiple perspectives. The reinforcement learning agent dynamically assigns adaptive weights to different teachers based on their prediction performance and the discrepancy between teacher and student models, which greatly enhances pseudo-label quality and distillation performance. Experiments on nine graph network datasets demonstrate that our method achieves consistent accuracy improvements ranging from 1.4% to 6.2% in few-shot node classification.