Exploring the Capabilities and Limitations of Graph Transformers: A Comprehensive Taxonomy and Empirical Study
Graph transformers have emerged as a promising alternative to graph neural networks, but their theoretical properties and practical capabilities require deeper understanding. This work provides a comprehensive taxonomy of graph transformer architectures, analyzes their theoretical properties, and empirically evaluates their ability to capture graph structure, mitigate over-smoothing, and alleviate over-squashing.