DATGR framework uses drift-aware graph rewiring to improve biomedical text modeling

The paper introduces Drift-Aware Temporal Graph Rewiring (DATGR), a framework that models concept evolution by dynamically updating co-occurrence edges based on estimated semantic drift. Instead of retraining embeddings for each time slice, DATGR performs lightweight, feedback-driven rewiring using a logistic update rule applied to edge weights.

• Evaluated on the Biomedical Multi-Relation Corpus (BIOMRC), the method achieved a mean AUROC improvement of approximately 0.066 absolute difference (0.699 vs. 0.633) over a static baseline.
• Area Under the Precision-Recall Curve (AUPRC) remained comparable (0.738 vs. 0.744), showing that drift-aware adaptation enhances link-prediction recall without a loss in precision.

These results demonstrate that edge-level adaptation effectively captures temporal semantic change in evolving biomedical text while remaining computationally efficient and interpretable.