SoLIFT: Source–Local Feature Interfaces for Graph Foundation Models

Graph foundation models (GFMs) aim to transfer knowledge across graphs, requiring shared encoders to accommodate heterogeneous node attributes. A prominent class of GFM methods maps node attributes to a fixed input width through graph-local spectral transformations. These select feature directions and coordinates independently within each graph. Consequently, recurring source directions may be discarded if they are not locally dominant. We introduce SoLIFT (Source–Local Interfaces For Transfer), a framework that uses source evidence to guide feature selection and coordinate choice. Under an explicit positional feature convention, it combines frozen source subspace evidence with local variation through two variants. SoLIFT-Split reserves common capacity and fills the remaining width with residual PCA, while SoLIFT-Fusion jointly selects directions and orients the selected basis toward a fixed source reference. Our theoretical analysis characterizes how source evidence affects feature selection and shows that coordinate orientation preserves the selected representations' within-graph geometry. We evaluate our framework with seven representative GFM pipelines for transfer from source graphs to unseen target graphs. Our framework improves overall transfer performance over the tested graph-local preprocessing baselines.