AdapterGNN: Efficient Delta Tuning Improves Generalization Ability in Graph Neural Networks

Fine-tuning pre-trained models has recently yielded remarkable performance gains in graph neural networks (GNNs). In addition to pre-training techniques, inspired by the latest work in the natural language fields, more recent work has shifted towards applying effective fine-tuning approaches, such as parameter-efficient tuning (delta tuning). However, given the substantial differences between GNNs and transformer-based models, applying such approaches directly to GNNs proved to be less effective. In this paper, we present a comprehensive comparison of delta tuning techniques for GNNs and propose a novel delta tuning method specifically designed for GNNs, called AdapterGNN. AdapterGNN preserves the knowledge of the large pre-trained model and leverages highly expressive adapters for GNNs, which can adapt to downstream tasks effectively with only a few parameters, while also improving the model's generalization ability on the downstream tasks. Extensive experiments show that AdapterGNN achieves higher evaluation performance (outperforming full fine-tuning by 1.4% and 5.5% in the chemistry and biology domains respectively, with only 5% of its parameters tuned) and lower generalization gaps compared to full fine-tuning. Moreover, we empirically show that a larger GNN model can have a worse generalization ability, which differs from the trend observed in large language models. We have also provided a theoretical justification for delta tuning can improve the generalization ability of GNNs by applying generalization bounds.