On Representation Knowledge Distillation for Graph Neural Networks

Knowledge distillation is a promising learning paradigm for boosting the performance and reliability of resource-efficient graph neural networks (GNNs) using more expressive yet cumbersome teacher models. Past work on distillation for GNNs proposed the Local Structure Preserving loss (LSP), which matches local structural relationships across the student and teacher's node embedding spaces. In this paper, we make two key contributions: From a methodological perspective, we study whether preserving the global topology of how the teacher embeds graph data can be a more effective distillation objective for GNNs, as real-world graphs often contain latent interactions and noisy edges. The purely local LSP objective over pre-defined edges is unable to achieve this as it ignores relationships among disconnected nodes. We propose two new approaches which better preserve global topology: (1) Global Structure Preserving loss (GSP), which extends LSP to incorporate all pairwise interactions; and (2) Graph Contrastive Representation Distillation (G-CRD), which uses contrastive learning to align the student node embeddings to those of the teacher in a shared representation space. From an experimental perspective, we introduce an expanded set of benchmarks on large-scale real-world datasets where the performance gap between teacher and student GNNs is non-negligible. We believe this is critical for testing the efficacy and robustness of knowledge distillation, but was missing from the LSP study which used synthetic datasets with trivial performance gaps. Experiments across 4 datasets and 14 heterogeneous GNN architectures show that G-CRD consistently boosts the performance and robustness of lightweight GNN models, outperforming the structure preserving approaches, LSP and GSP, as well as baselines adapted from 2D computer vision.