## 5 Discussion
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Model** & **Core links** & **Attention Mechanism** & **Advantages** \\ \hline \multirow{4}{*}{Original GAT} & Introduction attention mechanisms for graph neural networks (GNN), allowing the model to learn the importance (intention weight) of each graph. & Using a single layer followed several network to improve attention scores, followed by a softmax function to maximize those scores, & Subtask for road to understandly varied graphs \\  & Exploiting node-wise aggregation mechanisms. The attention mechanism is applied to models of random and deep data. & & \\ \cline{2-3}  & Extensive the original GAT by using multiple attention mechanisms (Denoft) & Each channel depends in some attention scores, and the weights are either contextual or averaged. & \\ \cline{2-3}  & Multi-Head Attention Attention & **Coars** the original GAT by using multiple attention mechanisms (Denoft) & Each channel depends in some attention scores, and the weights are either contextual or averaged. & \\ \cline{2-3}  & Combining different attention models. & & \\ \hline \multirow{4}{*}{GAT-2} & An improvement over the original GAT that feeds the attention mechanism mechanism to make it more expensive and less sensitive to the network and finally learn the attention mechanism. & Imadad of computing attention across a single layer & \\  & Attentive attention & **Coars** the original GAT by using multiple attention mechanisms to make it more expensive and less sensitive to the network and finally learn the attention mechanism. & & \\ \cline{2-3}  & A variation designed by multiple attention mechanisms for graph neural networks. & & \\ \cline{2-3}  & Semi-GAT and graphs (GATs) before the supervised learning of the graph. & & \\ \cline{2-3}  & Is there no attention mechanism for the attention mechanism. & & \\ \cline{2-3}  & Is there no attention mechanism for the attention mechanism. & & \\ \cline{2-3}  & Incorporating edge weights directly into the attention mechanism, including the attention mechanism, including the attention mechanism, including the attention mechanism, including the attention mechanism for the attention mechanism. & & \\ \cline{2-3}  & Edge-Weighted GAT & adding the model more sensitive to the attention mechanism for the attention mechanism. & & \\ \cline{2-3}  & Is there no attention mechanism to include edge weights, including the attention mechanism for the importance of neighboring nodes during & & \\ \cline{2-3}  & Is there no attention mechanism to include edge weights, including the attention mechanism for the attention mechanism. & & \\ \cline{2-3}  & Is there no attention mechanism to include edge weights, including the attention mechanism, including the attention mechanism, including the attention mechanism, including the attention mechanism, including the attention mechanism, including the attention mechanism, including the attention mechanism, including the attention mechanism, including the attention mechanism, including the attention mechanism, including the attention mechanism, including the attention mechanism, including the attention mechanism, including the attention mechanism, and the attention mechanism, including the attention mechanism, including the attention mechanism, and the attention mechanism, and the attention mechanism, and the attention mechanism, and the attention mechanism, and the attention mechanism, and the attention mechanism, and the attention mechanism, and the attention mechanism, and the attention mechanism, respectively. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Overview of different GAT-based architectures, core concepts, and their advantages. to address complex challenges across various scientific domains. Notably, several ground-breaking studies published this year highlight the expanding utility of GATs, particularly in fields such as polymer science, drug discovery, and bioinformatics, where traditional methods have often fallen short.