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Graph Transformer attention doesn't degrade into unstructured noise at scale: learned interaction matrices converge to continuous, predictable graphons that transfer directly to larger graphs.
TL-HFD achieves exact local updates while significantly reducing computational volume, outperforming traditional methods in noisy scenarios.
