GraphBridge: Graph-Mediated Transfer under Structural Modality Missingness

Machine learning systems are often trained in domains with rich information but deployed in domains where an entire modality is unavailable and raw feature spaces do not align. External knowledge graphs (KGs) can provide a semantic bridge across such domains, but they can be noisy, incompletely aligned to data, and may contain relations that are useful in one population but harmful in another. We propose GraphBridge, a framework for KG-mediated transfer under structural modality missingness. GraphBridge anchors raw variables from each domain to graph node families with abstention, learns a source-target-specific transferable graph by filtering relations according to scores from literature support, source and target utility, and cross-domain stability, and constructs a low-dimensional spectral bridge for source-to-target signal transport. It then learns the transformation in the source domain from this bridge to the source-only modality and applies this transformation in the target domain. We provide guarantees for anchor and graph recovery, spectral bridge perturbation, transported-feature error, target excess risk, and a validation-protected model selection. On synthetic benchmarks, GraphBridge consistently improves over target-only prediction across various settings. On a real benchmark of PLATO pharmacogenomic transfer with BioKG, GraphBridge achieves strong improvement, reaching Pearson $R=0.824$ on GDSC cell-line transfer and nearly matching the oracle on a PDX transfer pair with $R=0.569$ compared to oracle $R=0.574$. These results show that \method can support transfer via noisy external knowledge graphs across structurally mismatched domains.