Composing Priors for Temporal Graph Learning

Temporal graph models often perform well on individual datasets yet struggle to generalize the same architecture across multiple datasets or domains. We argue that this transfer gap stems from a common architectural choice: jointly learning temporal dynamics and structural propagation entangles domain-specific spatio-temporal dependencies. We show that temporal and structural priors can instead be effectively composed before graph-specific learning. Building on this insight, we introduce a framework that decouples temporal and structural representation learning and equips a GNN with three complementary, domain-agnostic priors: foundation-model temporal embeddings, local temporal-context summaries, and supra-Laplacian positional encodings of the evolving topology. Rather than asking a recurrent architecture to rediscover temporal dynamics and structural propagation for each domain, our framework learns how reusable representations interact over the graph. Across multiple real-world temporal graph benchmarks, it achieves competitive or state-of-the-art supervised performance. On the MiNT zero-shot benchmark, our framework consistently mitigates negative transfer and outperforms strong temporal graph baselines across 20 unseen networks.