Efficient Out-of-Distribution Generalization for Pretrained GNNs via Prompt Learning

Graph out-of-distribution (OOD) generalization enables graph neural networks (GNNs) to perform robustly in applications when confronted with new environments not encountered during training. For better realization of OOD generalization, current methods are increasingly focused on designing more complex causal inference models trained from scratch, without considering the potential of existing pretrained models, and increased computational complexity. In this article, we propose an efficient framework [efficient graph OOD generalization (EGOG)], which uses prompt learning for pretrained GNNs to extract causal subgraphs from input graph data, for enhancing the generalization ability of the pretrained GNNs. Specifically, we design a graph causal prompt module to enhance the generalization ability of the pretrained GNNs. First, we use graph prompt learning with a causal features generator that enables pretrained GNNs to decouple causal features from spurious features in graph nodes. Then, considering that the structure of graph is also affected by distribution shift, we further design a causal structure generator (CSG), which reuses the pretrained GNNs to analyze graph's structure, decoupling causal substructures from spurious substructures. This approach enhances the pretrained GNN's ability to decouple causal subgraph from both the node and structure perspectives. Meantime, we employ gradient reversal layers and information bottleneck to encourage our method to more accurately disentangle causal subgraph from spurious subgraph. Experiment results show that EGOG can be applied to pretrained GNNs to enable generalization ability. Compared with the state-of-the-art baseline, EGOG shows an average relative improvement of 2.73% on two synthetic datasets while using only average 4.38% of the trainable parameters.