On Representation Knowledge Distillation for Graph Neural Networks (2111.04964v4)

Abstract: 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

• The paper presents G-CRD as a novel distillation method that leverages contrastive learning to boost student GNN performance.
• It demonstrates that preserving both local and global graph topologies enhances generalization and robustness across diverse datasets.
• Experiments reveal that G-CRD outperforms existing methods on various GNN architectures, marking a significant step in efficient model design.

Graph Contrastive Representation Distillation for Graph Neural Networks

The paper "On Representation Knowledge Distillation for Graph Neural Networks" presents a novel approach to enhance the performance of lightweight Graph Neural Networks (GNNs) through a distillation technique known as Graph Contrastive Representation Distillation (G-CRD). Knowledge distillation involves transferring knowledge from complex, expressive teacher models to more resource-efficient student models, making it a critical method in optimizing GNNs for real-world applications that demand scalability and efficiency.

Graph Contrastive Representation Distillation (G-CRD)

The authors introduce G-CRD as a method to implicitly preserve the global topology of graph data during the distillation process. Unlike the Local Structure Preserving loss (LSP), which only maintains local structural relationships, G-CRD employs contrastive learning to align student embeddings with those of the teacher in a shared representation space. This alignment occurs through node-level contrastive tasks that classify positive from negative samples across embedding spaces, which is theorized to preserve both local and global relationships effectively.

Benchmarks and Experiments

The paper expands on previous benchmarks by incorporating four diverse datasets: ARXIV, MAG, MOLHIV, and S3DIS, evaluating multiple heterogeneous GNN architectures. These datasets present significant challenges, showcasing performance discrepancies between teacher and student models, thus making them suitable for distillation studies. Experiments reveal that G-CRD consistently boosts student model performance, surpassing other representation distillation techniques such as LSP, GSP, and methods adapted from 2D computer vision, including FitNet and AT. Specifically, G-CRD's success across various architectures, including GCNs, GINs, and MinkowskiNets, underscores its versatility.

Analysis of Distilled Representations

An insightful analysis of representational similarity demonstrated that G-CRD maintains a balance between preserving local and global topological features. Metrics such as Centered Kernel Alignment (CKA) and the Mantel test were employed to quantify these similarities. Results indicated that G-CRD outperformed explicit relationship-preserving methods like LSP and GSP, both in maintaining the representational integrity and improving generalization to out-of-distribution data.

Additional Considerations

The paper also explored the robustness and transferability of distilled models. In practical scenarios involving sparse or noisy 3D scans, G-CRD-trained models exhibited enhanced resilience compared to purely supervised counterparts. Moreover, the quantizability of distilled representations was shown to enable performance retention in INT8 precision models, with G-CRD improving the effectiveness of quantization-aware training strategies.

Future Directions

This paper advances the understanding of distillation in GNNs by proposing a more effective mechanism for preserving intricate graph structures through contrastive learning. Future work might explore the integration of these distillation techniques into more varied and complex GNN architectures, such as those involving dynamic graphs or temporal data. In the broader context of artificial intelligence, enhancing distillation methods could pave the way for deploying efficient models in data-sensitive environments like healthcare and finance, where computational resources are constrained.

In conclusion, this research provides a pivotal contribution to the field of efficient GNN design, suggesting that contrastive learning-based distillation can optimize lightweight models for better performance without sacrificing the integrity of learned representations.