Towards Graph Foundation Model: Node Feature Transfer Invariant Modeling on General Graphs

A significant challenge in developing graph foundational models is to achieve universality and generalizability across general graphs, beyond text-attribute graphs. Unfortunately, most graph neural networks are designed for specific applications in particular fields and are difficult to generalize across different graphs. The most critical problem is the lack of Transfer Invariant Metadata (TIM) for graphs, akin to pixels for images and vocabularies for text, which prevents the development of graph foundational models. TIM is particularly problematic, albeit critical, on graph nodes with semantically variable features, making it difficult to transfer knowledge across graphs. Here, we analyze TIM and propose a theoretical approach to mining TIM in node features. It extracts semantically consistent information across domains and unifies data space with relatively low information loss. We then introduce a Transfer-Invariant Graph (TIG) foundational model to transform features of different dimensions into a unified structural representation. This transformation can effectively learn and extract TIM and intrinsic graph knowledge by self-supervised learning. We conducted extensive experiments, and the results showed that TIG even outperformed some models trained on data from targeted domains. In one-shot cross-domain scenarios, TIG achieved high accuracy with less training data and without any prompt tuning.