ROD: Reception-aware Online Distillation for Sparse Graphs

Knowledge distillation, a technique recently gaining popularity for enhancing model generalization in Convolutional Neural Networks (CNNs), operates under the assumption that both teacher and student models are trained on identical data ...

• We propose an Online Adversarial Knowledge Distillation framework for GNNs.
• OAD trains a group of GNN models to capture structure updates in GNNs.
• OAD extracts both local and global knowledge in graph structured data.
• Extensive ...

Despite the success of Graph Neural Networks (GNNs) on various applications, GNNs encounter significant performance degradation when the amount of supervision signals, i.e., number of labeled nodes, is limited, which is expected as GNNs are trained ...

Graph neural networks (GNNs) have achieved state-of-the-art results for semi-supervised node classification on graphs. Nevertheless, the challenge of how to effectively learn GNNs with very few labels is still under-explored. As one of the ...