where 𝑊 ) ( ∈ 𝑅 $×$ ! , 𝑊 * ( ∈ 𝑅 $×$ " and 𝑏 ) ( , 𝑏 * ( ∈ 𝑅 $ . After passing through the embedding layer, the features are expanded to the same dimension. Then, message passing and aggregation are executed via the convolutional layers. In the Graph Transformer, this operation is primarily achieved by the self-attention mechanism. The model performs six convolutional operations, with the convolution process for the l-th layer expressed by the following equations: