linear-complexity

EMAGN is a new efficient multi-attention graph network for traffic forecasting that linearizes self-attention via learned clustering, reducing complexity from quadratic to linear while maintaining accuracy close to full-attention models, with significant reductions in training time, inference time, and GPU memory.