DBHGNN: Dual-Branch Hamiltonian Graph Neural Network for Spatial Flow Prediction

2026

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DOI 10.1145/3833142.3833201
Data confidence 70 medium
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Abstract

Large-scale urban flow prediction needs models capturing local diffusion and heterogeneous attraction. Existing GNNs rely on neighborhood aggregation, causing over-smoothing and weak functional modeling. DBHGNN decouples propagation into Laplacian diffusion and POI-derived potential modulation, improving OD flow prediction across four U.S. cities with lower training cost.