Generalizable Insights for Graph Transformers in Theory and Practice

The introduction of the Generalized-Distance Transformer (GDT) marks a significant advancement in the field of Graph Transformers, which have previously shown strong empirical performance but suffered from inconsistencies in design and application. The GDT leverages standard attention mechanisms while integrating recent advancements, providing a more robust framework for various tasks. Extensive evaluations on over eight million graphs reveal that the GDT performs exceptionally well across multiple domains, including image-based object detection, molecular property prediction, and code summarization. This research not only highlights effective design choices but also seeks to close the gap between theoretical insights and practical applications, ensuring that the findings can be generalized beyond specific domains. The strong performance of the GDT in few-shot transfer settings without the need for fine-tuning further emphasizes its potential impact on future AI applications.