Unveiling the Unseen Potential of Graph Learning through MLPs: Effective Graph Learners Using Propagation-Embracing MLPs (2311.11759v1)

Abstract: Recent studies attempted to utilize multilayer perceptrons (MLPs) to solve semi-supervised node classification on graphs, by training a student MLP by knowledge distillation (KD) from a teacher graph neural network (GNN). While previous studies have focused mostly on training the student MLP by matching the output probability distributions between the teacher and student models during KD, it has not been systematically studied how to inject the structural information in an explicit and interpretable manner. Inspired by GNNs that separate feature transformation $T$ and propagation $\Pi$, we re-frame the KD process as enabling the student MLP to explicitly learn both $T$ and $\Pi$. Although this can be achieved by applying the inverse propagation $\Pi^{-1}$ before distillation from the teacher GNN, it still comes with a high computational cost from large matrix multiplications during training. To solve this problem, we propose Propagate & Distill (P&D), which propagates the output of the teacher GNN before KD and can be interpreted as an approximate process of the inverse propagation $\Pi^{-1}$. Through comprehensive evaluations using real-world benchmark datasets, we demonstrate the effectiveness of P&D by showing further performance boost of the student MLP.

• The paper introduces Propagate & Distill to effectively transfer graph structure from GNNs to MLPs using inverse and direct propagation, reducing computational overhead.
• The methodology improves feature transformation and propagation, demonstrating superior performance on benchmarks like Cora, CiteSeer, and Pubmed in both transductive and inductive settings.
• Empirical results reveal that deeper propagation correlates with enhanced MLP performance, underscoring the method’s potential for efficient and scalable graph learning.

Enhancing Graph Neural Network Distillation through Propagation-Embracing MLPs

Introduction

The optimization of Graph Neural Networks (GNNs) for real-world scenarios calls for models that balance performance with computational efficiency. While GNNs are instrumental in harnessing the power of graph structures for tasks such as node classification and link prediction, their scalability is often challenged by increased inference times, especially as the depth of the network grows. A novel solution to this dilemma is the application of Knowledge Distillation (KD) techniques, specifically transitioning from a GNN (teacher) to a Multilayer Perceptron (MLP) (student). This transition promises significant reductions in inference times but introduces the challenge of effectively transferring the structural knowledge embedded in the GNN to the MLP.

Methodological Innovation

The paper presents a unique approach titled Propagate & Distill (P&D) to enhance the MLP's capacity to learn graph structures effectively through KD, without directly feeding the structural information as input. This method innovatively applies the concept of inverse and direct propagation. By examining the KD process through the lens of feature transformation and propagation, the authors illuminate how a student MLP can learn to replicate the function of a GNN, focusing on both transforming node features and propagating information across the graph structure. Unlike previous methods, which either increased computational complexity or required substantial model adjustments, P&D introduces a computationally efficient mechanism. It encompasses propagating the teacher GNN's output before distillation—a process that theoretically engages in approximate inverse propagation, thereby encoding structural data more directly and interpretably.

Empirical Evaluations

Comprehensive evaluations underline the efficacy of P&D against several benchmarks on real-world graph datasets like Cora, CiteSeer, and Pubmed, showcasing superior performance in both transductive and inductive scenarios. Particularly, the experiments reveal that deeper and stronger propagation directly correlates with heightened MLP performance post-distillation. These findings are pivotal, as they not only demonstrate P&D's superiority over traditional methods but also provide novel insights into how the propagation parameters (depth and strength) influence learning outcomes.

Theoretical Insights and Future Directions

A theoretical analysis explains the success of P&D through the self-correction mechanism enabled by propagation, which is significantly influenced by the graph's homophily. This theoretical backing provides a solid foundation for the empirical observations, suggesting that the propagation of the teacher's output induces a form of graph signal denoising, which in turn bolsters the MLP's ability to learn structure effectively.

Looking ahead, the potential for further refining P&D includes exploring alternative propagation strategies to enhance its adaptability and efficacy across a broader spectrum of graph-related tasks. The relationship between the homophily of the underlying graph and the efficacy of KD deserves deeper investigation to fine-tune the distillation process according to specific graph characteristics.

Conclusion

The propagation-embracing MLP framework introduced in this research significantly bridges the learning gap between GNNs and MLPs for graph-related tasks. By providing a clear, efficient path for transferring structural knowledge from GNNs to MLPs, P&D not only reduces computational costs but also opens fertile ground for further exploration in the efficient deployment of GNNs in real-world applications.