Under norm, low-rank, or clustering assumptions, a graph transformer's hidden dimension can be compressed with only small error in outputs and attention scores.
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A Theory for Compressibility of Graph Transformers for Transductive Learning
Under norm, low-rank, or clustering assumptions, a graph transformer's hidden dimension can be compressed with only small error in outputs and attention scores.