TG-Any: Train Once, Predict Links on Unseen Temporal Graphs

A new temporal graph should not require a new predictor. We introduce TG-Any, a train-once approach to temporal link prediction through a shared interface of 24 streaming behavioral scores. An 18,584-parameter network learns query-dependent, nonnegative fusion on tgbl-wiki; its parameters and source-fitted normalization maps remain fixed on new graphs. A post-hoc pipeline correction and complete matched rerun retain strong transfer relative to source-trained logistic regression and LightGBM: TG-Any reaches 0.1614 mean reciprocal rank on Review and 0.2379 on GoogleLocal, respectively 6.2× logistic regression's MRR and 23.7% above LightGBM. On Coin's official split, its 0.8057 is close to a target-window-trained logistic regression's 0.8056, with an interval containing zero for their difference. Simpler fusion rules qualify these gains: uniform weights reach 0.3133 on Review, and Global is close to TG-Any on GoogleLocal. Global also exceeds query conditioning on the three target validation splits. The shared signal interface supports cross-graph reuse, while greater source accuracy and query conditioning do not guarantee better transfer.