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Lost in Tokenization: Fundamental Trade-offs in Graph Tokenization for Transformers
Transformers have become a central architecture for graph learning, but their application to graphs requires first choosing a tokenization: a graph-to-token map that determines which structural information is exposed at the input. In this work, we show that this choice is a fundamental component of
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