## 3 Graph Attention Network Categories
### Graph-Embedding GATs  
Graph-embedding GATs, a subcategory of graph attention networks, focus on learning latent coordinates or embeddings for each graph node, effectively capturing the underlying graph structure. By combining the strengths of graph-embedding techniques and attention mechanisms, these networks can efficiently represent and process graph-structured data, enabling them to address a wide range of problems across various domains. Graph-embedding techniques transform the graph structure into a low-dimensional continuous space that retains the essential properties of the original graph. GATs, on the other hand, use attention mechanisms to weigh the importance of neighboring nodes when aggregating features. In graph-embedding GATs, the attention mechanism is combined with graph-embedding techniques to learn more expressive node representations, capturing both the local and global structure of the graph. Graph-embedding GATs have been successfully applied in various domains, showing promise in improving the performance of graph-based machine learning models and driving further advancements in graph deep learning research. Towards this direction, MS-GAT (Multi-relational Synchronous Graph Attention Network) leverages graph-embedding techniques to capture subtle interactions in traffic systems, such as data coupling between spatial and temporal dimensions [30]. By learning node embeddings that encapsulate these intricate relationships, MS-GAT is able to provide enhanced traffic analysis and prediction. Tested on five real-world datasets, this model demonstrates the power of graph-embedding GATs in handling complex graph data, achieving better results than existing solutions. Similarly, a novel heterogeneous graph neural network framework with a hierarchical attention mechanism is introduced to manage multi-relational data with large amounts of entities and relations [31]. This framework utilizes graph-embedding GATs to learn expressive node representations that effectively capture the underlying structure of heterogeneous graphs. The approach, tested on a variety of heterogeneous graph tasks, further highlights the potential of graph-embedding GATs in various graph-based applications, as it outperforms existing state-of-the-art models. HFGAT [7], a novel hybrid framework, integrates global and local characteristics of compounds for predicting their metabolic pathways. This approach has proven valuable for drug discovery, outperforming traditional machine learning methods and graph convolutional network-based techniques in multi-class classification accuracy and F1 scores. Similarly, MKGAT [32] is a computational framework designed to predict miRNA-disease correlations, with successful validation for three human cancers. Graph4 Web [33] is a relation-aware graph attention network for web service classification. By parsing web service descriptions into a dependency graph and leveraging pre-trained BERT embeddings, Graph4 Web demonstrates improved classification performance compared to seven baseline methods. Another unified framework for graph attention networks [34] combines graph context information and node representations to achieve better performance in semi-supervised node classification. A new framework for explainable recommendation using a knowledge graph attention network model [35] achieves high recommendation accuracy and provides interpretable visual explanations. DTIHNC [36] integrates heterogeneous networks and cross-modal similarities to better understand drug-target interactions, outperforming existing methods. A GAT model in a study [37] accurately predicts Parkinson's disease (PD) by combining morphological, structural, and functional features, identifying regions with the greatest PD impact. A novel source code model [38] combines abstract syntax trees (ASTs) and control flow graphs (CFGs) with a graph attention mechanism-based neural network, improving program classification and defect prediction.