Dynamic-graph prediction without per-graph retraining: interaction histories become tables a frozen tabular model can rank
CL-DGFM encodes interaction histories on dynamic graphs as tabular features independent of node identity, covering repeated contacts, indirect connectivity and ordered destination transitions, then lets a pretrained tabular foundation predictor learn from diverse synthetic dynamics how to read them. It achieves the highest mean MRR on 10 of 13 datasets across TGB and TGB-Seq, exceeding the strongest listed baselines by 10.78, 15.24 and 11.69 percentage points on Comment, Flickr and YouTube, and transfers to real graphs without target-graph parameter updates.