We compare the model's performance relative to other approaches on diverse regression and Bayesian optimization tasks, including the challenging but common

Although GNNs are powerful tools for these "forward" prediction tasks, few works discuss the "backward" (or inverse) problem defined on trained GNNs.

Existing methods frequently assume causal sufficiency, disregarding the presence of unobserved confounding variables.

The general recipe for each communication round is composed of three steps.

Computing gradients through backpropagation is crucial to the success of modern deep neural networks.