Previous graph neural networks (GNNs) usually assume that the graph data is
with clean labels for representation learning, but it is not true in real
applications. In this paper, we propose a new multi-teacher distillation method
based on bi-level optimization (namely BO-NNC), to conduct noisy node
classification on the graph data. Specifically, we first employ multiple
self-supervised learning methods to train diverse teacher models, and then
aggregate their predictions through a teacher weight matrix. Furthermore, we
design a new bi-level optimization strategy to dynamically adjust the teacher
weight matrix based on the training progress of the student model. Finally, we
design a label improvement module to improve the label quality. Extensive
experimental results on real datasets show that our method achieves the best
results compared to state-of-the-art methods.

该论文提出了一种基于双层优化的新型多教师蒸馏方法（即 BO-NNC），用于在图数据上进行有噪声节点分类。通过多个自监督学习方法训练多个教师模型，并通过教师权重矩阵对其预测进行集成；同时设计了一种新的双层优化策略来根据学生模型的训练进程动态调整教师权重矩阵；最后，设计了一个标签改进模块来提高标签质量。在真实数据集上进行了大量实验证明，与现有方法相比，我们的方法取得了最佳结果。