Representation Alignment of Graph Foundation Models for Cross-Dataset Classification

CLIP-based graph foundation models tackle cross-dataset node classification by aligning graph and text embeddings via contrastive learning. However, their practical performance is fundamentally constrained by the distribution shift between source-domain pre-training texts and inference-time classification templates, which causes severe semantic mismatch. To achieve effective cross-domain representation alignment without any fine-tuning, we propose Perturbation Alignment (PA)—a training-free adaptation framework that performs directed perturbations on classification template embeddings guided by subgraph encoding. Specifically, PA applies a single-step perturbation along the composite objective function to enhance the classification margin of the original model, injecting target-domain subgraph information into textual prototypes while preserving pre-trained semantic knowledge. Extensive experiments on a total of seven benchmark datasets demonstrate that PA not only substantially outperforms the vanilla baseline, but also yields particularly significant improvements when source-domain data or template descriptions are missing. Moreover, PA matches or even surpasses lightweight fine-tuning on certain datasets, providing a practical, parameter-efficient, and theoretically principled solution for cross-dataset graph node classification.