Bridging the Gap to Unseen Graphs: Target-Side Adaptation for Frozen Graph Foundation Models

Graph foundation models (GFMs) are emerging as a key paradigm for cross-domain graph learning. However, most GFMs typically face two limitations during the adaptation phase: (i) Firstly, the adaptation method of GFMs is closely coupled with the internal architecture of the model, making it difficult for users to adapt published models to new target graphs in black-box scenarios. Furthermore, (ii) most existing methods rely on a limited number of labels to optimize parameters during the adaptation phase. When the target graph differs from the pre-training domain, limited supervision may not effectively guide adaptation, potentially leading to negative transfer. To address these limitations, this paper proposes a target-side adaptation framework for frozen GFMs to improve the practicality and effectiveness of GFMs. Specifically, we design a feature adapter and a structural adapter at the model’s input to enable the frozen GFM to adapt to the features and structure of the target data, allowing users to perform adaptation without needing to understand the model’s internal design. Subsequently, we employ a maximal coding rate reduction loss to exploit unlabeled target nodes and promote compact but diverse adapted features, while using a spectrum alignment loss to adjust the target graph such that its information spectrum matches the encoding preference of the frozen GFM. Additionally, we theoretically analyzed that our adaptation method achieves a lower generalization error bound. Comprehensive experiments demonstrate the effectiveness and generalization capability of this method on various downstream tasks.