You Only Once Taking a Spectral Detour to Accelerate Graph Neural Network

Training Graph Neural Networks (GNNs) often relies on repeated, irregular, and expensive message-passing operations over all nodes (e.g., N), leading to high computational overhead. To alleviate this inefficiency, we revisit the GNNs training from the spectral perspective.