Noisy Node Classification by Bi-level Optimization based Multi-teacher Distillation

arXiv:2404.17875v1 Announce Type: new
Abstract: 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, …

abstract applications arxiv classification cs.lg data distillation gnns graph graph data graph neural networks labels networks neural networks node optimization paper representation representation learning the graph true type