Conducting experiments and collecting data for machine learning models is a complex and expensive endeavor, particularly when confronted with limited information. Typically, extensive experiments to obtain features and labels come with a significant acquisition cost, making it impractical to carry out all of them. Therefore, it becomes crucial to strategically determine what to acquire to maximize the predictive performance while minimizing costs.

Fully connected Graph Transformers (GT) have rapidly become prominent in the static graph community as an alternative to Message-Passing models, which suffer from a lack of expressivity, oversquashing, and under-reaching.