EMAGN: Efficient Multi-Attention Graph Network via Learned Clustering for Scalable Traffic Forecasting

arXiv cs.AI · 18 hours ago

EMAGN is a traffic forecasting model that reduces the computational complexity of self-attention mechanisms from O(N^2 d) to O(NMd) through learned clustering of attention vectors. On PEMS-BAY and METR-LA datasets, EMAGN achieves 2.7-3.2% MAE difference from full-attention baselines while reducing training time by 32%, inference time by 38%, and GPU memory use by 58%. The efficiency gains enable operation with 16 attention heads on 11 GB GPUs where existing full-attention models run out of memory, allowing deployment of larger model configurations.