Graph Diffusion Models vs. STGCN: Scaling Laws and Performance on Large-Scale Spatio-Temporal Benchmarks

This report synthesises findings from 13 peer-reviewed papers addressing the following research question: How does the parameter scaling law of graph diffusion models affect convergence speed and final MAE compared to STGCN on large-scale spatio-temporal benchmarks. Timely accurate traffic forecast is crucial for urban traffic control and guidance. Due to the high nonlinearity and complexity of traffic flow, traditional methods cannot satisfy the requirements of mid-and-long term prediction tasks and often neglect spatial and temporal. 7 claims were extracted from source literature; 7 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.7/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: How does the parameter scaling law of graph diffusion models affect convergence speed and final MAE compared to STGCN on large-scale spatio-temporal benchmarks?

Autonomous literature synthesis. Automated review score: 8.7/10. Full text and citation available at Assignee Research.