## 3 Graph Attention Network Categories
### Global Attention Networks  
Global Attention Graph Neural Networks represent a sophisticated approach in the field of graph-based deep learning, designed to capture and utilize both local and global contextual information within graph-structured data. These networks enhance traditional graph neural networks by introducing attention mechanisms that allow the model to focus on the most relevant parts of the graph, thereby improving its ability to capture intricate patterns and relationships. By leveraging global attention, these networks are capable of understanding the broader context in which nodes and edges exist, which is crucial for tasks where the relationships between distant nodes or the overall structure of the graph play a significant role. This ability to aggregate information from across the entire graph, rather than just from immediate neighbors, enables Global Attention GATs to produce more accurate and robust node embeddings, leading to better performance in a wide range of applications, from recommendation systems to sentiment classification and beyond. The CGAT model (Contextualized Graph Attention Network) [5] successfully captures both local and non-local contexts in knowledge graphs for enhanced recommendation methods. By combining a user-specific graph attention system, a biased random walk process, and an item-specific attention system, CGAT demonstrates superior performance compared to existing approaches. Addressing the challenges of global sequence contexts and structural syntax in aspect-category sentiment classification, the BiGAT model [6] employs graph attention networks, Biaffine modules, and aspect-specific mask operations. This method improves the capture of relations between words, resulting in better classification and outperforming existing methods. The HFGAT framework (hybrid framework based on GAT) [7] offers a novel approach to predicting metabolic pathways by combining global and local characteristics of compounds. This method outperforms traditional machine learning and graph convolutional network-based methods, providing valuable insights for drug discovery. GAT_SCNet [8] showcases its effectiveness in recognizing various categories of road markings using point clouds from Mobile Laser Scanning systems. With impressive results exceeding 91% across three criteria, this method sets a new state-of-the-art standard, particularly for linear road markings. The RA-AGAT model [9] addresses stock prediction and recommendation tasks by exploiting intercorrelation and temporal features. When tested on the Chinese A-share market, RA-AGAT outperformed existing approaches. A learnable feature map filtration module and an influence-based graph attention network [10] were introduced for visual position recognition in environments with extreme appearance changes. This approach yielded better results than existing methods, demonstrating its adaptability and effectiveness. Furthermore, the hierarchical graph attention network (HGAT) [11] addresses the challenge of obtaining full global information in semi-supervised node classification. Tests on four datasets revealed state-of-the-art results, with a sensitivity analysis further highlighting HGAT's ability to collect global structure information and transfer node features effectively. The model makes predictions according to the following formula:  
\[H^{\text{out}}=\text{softmax}\Bigg{(}\frac{1}{K}\sum_{k=1}^{K}\alpha^{k}W^{k} H_{l}^{*}\Bigg{)}\]where \(H^{\text{out}}\in\mathbb{R}^{|V|\times|Y|}\) is the prediction of nodes belonging to the class \(y_{i}\in|Y|\), and \(H_{1}^{*}\) is the concatenated node representation of \(H_{1}\) and \(H_{2l+1}\). The category of Global Attention Networks also includes the Holistic Graph Neural Network (HGNN), a two-fold architecture that introduces a global-based attention mechanism for learning and generating node embeddings [12].