Drift-Aware Temporal Graph Rewiring (DATGR) for Adaptive Semantic Modeling in Biomedical Text

Jul 10, 2026

A new framework, DATGR, dynamically updates co-occurrence edges in biomedical text graphs to capture semantic drift without retraining embeddings. Evaluated on the BIOMRC corpus, it achieved a 0.066 AUROC improvement over static baselines while maintaining comparable AUPRC. The method is lightweight, interpretable, and enhances link-prediction recall.

Why it matters: This approach offers a computationally efficient way to keep biomedical knowledge graphs current with evolving terminology, improving retrieval and discovery tasks.

Full story at: arXiv AI/ML ↗