Teaching Yourself: Graph Self-Distillation on Neighborhood for Node Classification

TL;DR

This paper introduces GSDN, a graph self-distillation framework that enables MLPs to leverage structural information during training, achieving GNN-like performance with much faster inference suitable for latency-sensitive applications.

Contribution

The paper proposes a novel GSDN framework that uses self-distillation to incorporate graph topology into MLPs without data dependency during inference, bridging the gap between GNNs and MLPs.

Findings

GSDN improves MLP performance by 15.54% on average.

GSDN outperforms state-of-the-art GNNs on six datasets.

GSDN achieves 75X-89X faster inference than GNNs.

Abstract

Recent years have witnessed great success in handling graph-related tasks with Graph Neural Networks (GNNs). Despite their great academic success, Multi-Layer Perceptrons (MLPs) remain the primary workhorse for practical industrial applications. One reason for this academic-industrial gap is the neighborhood-fetching latency incurred by data dependency in GNNs, which make it hard to deploy for latency-sensitive applications that require fast inference. Conversely, without involving any feature aggregation, MLPs have no data dependency and infer much faster than GNNs, but their performance is less competitive. Motivated by these complementary strengths and weaknesses, we propose a Graph Self-Distillation on Neighborhood (GSDN) framework to reduce the gap between GNNs and MLPs. Specifically, the GSDN framework is based purely on MLPs, where structural information is only implicitly used as prior to guide knowledge self-distillation between the neighborhood and the target, substituting the explicit neighborhood information propagation as in GNNs. As a result, GSDN enjoys the benefits of graph topology-awareness in training but has no data dependency in inference. Extensive experiments have shown that the performance of vanilla MLPs can be greatly improved with self-distillation, e.g., GSDN improves over stand-alone MLPs by 15.54% on average and outperforms the state-of-the-art GNNs on six datasets. Regarding inference speed, GSDN infers 75X-89X faster than existing GNNs and 16X-25X faster than other inference acceleration methods.