Learning Relations for Temporal Graph Foundation Models

Temporal graph learning has traditionally relied on dataset-specific models, whereas recent advances suggest that large-scale pre-training can yield broadly transferable representations. Extending this foundation model paradigm to temporal graphs is difficult because structural and temporal information interact across heterogeneous data regimes: spatiotemporal graphs with evolving node signals and fixed topology, interaction networks with dynamic edges and static features, and fully dynamic graphs in which both change. We address this heterogeneity with a unified architecture that maps such diverse observation formats into a shared representation space. The model captures spatio-temporal patterns by integrating the observed topology with a temporal correlation-based message-passing operator. When pre-trained across multiple graphs within each domain, our model transfers zero-shot to unseen test graphs, achieving state-of-the-art performance. These results provide a concrete step toward temporal graph foundation models and highlight the benefits of treating relational structure as data-dependent.