  Knowledge distillation is a learning paradigm for boosting 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 defined
over edges across the student and teacher's node embeddings. This paper studies
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. We propose Graph Contrastive
Representation Distillation (G-CRD), which uses contrastive learning to
implicitly preserve global topology by aligning the student node embeddings to
those of the teacher in a shared representation space. Additionally, 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.
Experiments across 4 datasets and 14 heterogeneous GNN architectures show that
G-CRD consistently boosts the performance and robustness of lightweight GNNs,
outperforming LSP (and a global structure preserving variant of LSP) as well as
baselines from 2D computer vision. An analysis of the representational
similarity among teacher and student embedding spaces reveals that G-CRD
balances preserving local and global relationships, while structure preserving
approaches are best at preserving one or the other. Our code is available at
https://github.com/chaitjo/efficient-gnns
