arXiv:2605.22471v1 Announce Type: new Abstract: 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 transformer expressivity. We examine three tokenizations that serve as building blocks for many existing graph tokenizations: spectral, random-walk, and adjacency tokenizations. We prove that different tokenizations induce distinct depth regimes: the s
This research is emerging as Transformers are increasingly applied to graph data, making the fundamental understanding of how to featurize graphs for these powerful models critical for advancing AI capabilities.
Understanding the fundamental trade-offs in graph tokenization directly impacts the expressivity and limitations of Transformer models for graph learning, a key area for many advanced AI applications.
This work deepens the theoretical understanding of how different graph tokenization methods influence Transformer performance, moving from empirical observation to foundational principles.
- · AI researchers
- · Graph AI developers
- · Machine learning platform providers
- · Industries relying on graph analytics
- · Developers using suboptimal graph tokenization methods
- · Entities relying on ad-hoc graph feature engineering
Improved design principles for graph neural networks and Transformer architectures.
Accelerated development of more powerful and efficient AI models for complex relational data.
Enhanced AI capabilities across fields like drug discovery, material science, cybersecurity, and social network analysis.
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Read at arXiv cs.LG