SCOT: Multi-Source Cross-City Transfer with Optimal-Transport Soft-Correspondence Objectives

Cross-city transfer leverages labeled data from well-instrumented cities to improve prediction in label-scarce ones, but remains challenging when cities adopt incompatible partitions with no ground-truth region correspondences. Even with expressive GNN encoders, transfer quality varies dramatically across methods sharing nearly identical backbones—indicating that alignment design, not encoder capacity, is the binding constraint. Existing paradigms exhibit complementary failure modes: heuristic anchor matching collapses to hubness under unequal partitions, while distribution-level matching over-mixes embeddings under heterogeneity. Both stem from a single missing primitive—explicit, mass-controlled soft correspondence between unequal region sets. The challenge intensifies in multi-source transfer, where independent source-to-target alignments yield conflicting gradients and source domination. We propose SCOT, which adapts entropic OT to this regime through three application-specific designs: an OT-weighted contrastive objective that resolves the geometric–semantic tension, a one-sided cycle regularizer respecting the rectangular $n_s\ne n_t$ geometry, and—as our central contribution—a shared prototype hub coordinated through balanced entropic OT under a target-induced prior, bypassing the source-selection problem in label-scarce regimes. Across real-world cities and tasks, \scot consistently improves transfer accuracy, achieving 5–50% relative MAE/MAPE reductions over the strongest baseline, with learned couplings and hub assignments quantitatively confirming that the diagnosed failure modes are resolved.