## 5 Discussion  
Graph attention networks (GATs) represent an enhancement to graph convolutional networks (GCNs), a class of neural networks specifically designed for processing data represented as graphs. By incorporating attention mechanisms, GATs offer greater flexibility and parameterization for graph convolutions, allowing them to more effectively capture and leverage the structural intricacies of graph data. These networks can be categorized based on the type of attention mechanism employed, the techniques used to extract information from the graph structure, and the specific domain applications for which they have been optimized. Over time, GATs have evolved into several specialized categories, each tailored to address distinct challenges in graph-structured data processing. Global GATs focus on capturing global features within a graph by using attention scores that are independent of the graph's edges. This approach enables them to identify and leverage broader patterns that span the entire graph, making them particularly effective in scenarios where a comprehensive global context is crucial. In contrast, Multi-Layer GATs enhance the learning of intricate, higher-level features by stacking multiple attention layers. Each layer aggregates information from neighboring nodes, progressively refining node features and enabling the network to capture more abstract and complex patterns from a larger neighborhood within the graph. Graph-embedding GATs combine graph-embedding techniques with attention mechanisms to learn latent coordinates for each graph node, effectively representing the underlying graph structure. This category excels in capturing both local and global graph structures, making it versatile across various domains. Spatial GATs focus on capturing local graph information by integrating both spatial and temporal attention mechanisms. This capability makes them particularly effective in applications like video anomaly detection, where the spatial and temporal relationships between nodes are critical. Variational GATs introduce a probability-based regularization technique that constrains graph attention scores, thereby mitigating overfitting. This makes them highly effective in complex, multimodal data environments where overfitting is a significant concern. Finally, Hybrid GATs combinemultiple technologies from these different categories to perform optimally across diverse tasks, such as miRNA-disease connection discovery and session-based recommendations, offering a flexible and powerful framework for a wide range of applications. Attention mechanisms in GATs can vary, encompassing dot product attention, additive attention, and even more advanced or customized attention models. The methods employed by GATs to obtain information from graph structures are diverse, involving edge-labeling approaches, aggregation functions, general integrators, softmax normalization, and edge masking. In this paper, we emphasized five key categorizations: Global Attention Networks, which use attention scores independent of graph edges to capture global features; Multi-Layer GATs, which harness multiple attention layers to better capture intricate higher-level features; graph-embedding GATs, which utilize graph-embedding techniques to learn latent coordinates for each graph node, representing the underlying graph structure; spatial GATs, which select both spatial and temporal attention to capture local graph information; and Variational GATs, which introduce a probability-based regularization technique to constrain graph attention scores, thereby mitigating overfitting. We also include a hybrid category comprising methods that integrate more than one of the aforementioned categories (Table 4). Graph attention networks (GATs), while based on a core architecture, exhibit substantial technical differences across various applications, adapting uniquely to the specific needs of each domain.