Standard GNNs match specialized models on zero-shot link prediction via graph reification
Researchers demonstrate that graph transfer learning doesn't require specialized architectures. By reifying knowledge graphs into a uniform node-and-edge representation with a fixed six-relation vocabulary, standard GNNs trained on single small graphs achieve zero-shot generalization across 40 inductive benchmarks. This challenges the assumption that transfer mechanisms must be baked into model design, suggesting that representation design alone can unlock cross-domain capability. The finding matters for practitioners: commodity architectures like GAT and GraphSAGE now compete with purpose-built foundation models on unseen graph tasks, lowering the barrier to deployment and potentially reshaping how knowledge graph systems are built.62
