## 3 Graph Attention Network Categories
### Spatial GATs  
Spatial graph attention networks (spatial GATs) are specialized graph neural networks designed to focus specifically on the spatial relationships between nodes in a graph. Unlike traditional GATs that may consider general node connections, spatial GATs emphasize the importance of spatial proximity and spatial dependencies in the graph structure. They employ an attention mechanism to dynamically prioritize the influence of neighboring nodes based on their spatial relevance, allowing the model to effectively capture spatial patternsand local interactions. This makes spatial GATs particularly useful in applications like urban planning, traffic flow prediction, and remote sensing, where understanding the spatial context and relationships between entities is crucial for accurate analysis and prediction. In video anomaly detection, deep learning-based models such as the Spatial-Temporal Graph Attention Network (STGA) [53] have been developed to capture the spatial and temporal relationships in video data. STGA leverages spatial and temporal attention to capture local information from the graph, achieving state-of-the-art results on popular benchmarks. Another recent work in graph neural networks is the SA-GAT model [54], which introduces a Substructure Interaction Attention module to improve graph classification performance. The SA-GAT outperformed existing graph kernel and graph neural network approaches, demonstrating its effectiveness for complex graph analysis. In the domain of multi-domain dialogue systems, a multi-task learning framework that incorporates graph attention networks (GATs) was proposed to tackle cross-domain slot sharing and dialogue act temporal planning. The study demonstrated superior performance compared to existing methods on the MultiWOZ 2.0 and 2.1 datasets [55]. A graph-based VCA model employing graph attention networks was proposed to simulate land use change by quantifying spatial interaction between urban entities. The proposed model was tested with data from Queensland and showed superior performance compared to existing CA models. The study also highlighted the importance of tuning discrete topological orders to improve calibration efficiencies [56]. The model employs a high-order neighborhood extension to iteratively explore the neighbors of each node by utilizing the original adjacency matrix \(A\). This process collects all identified neighbors to form a new high-order adjacency matrix. The \(k\)th-order (\(k=1,2,3,\ldots\)) adjacency matrix \(A^{k}\) is defined to represent the nodes connected through \(k\) hops. Mathematically, \(A^{k}\) is derived by multiplying matrix \(A\) by itself \(k-1\) times, such that \(A^{1}=A\), \(A^{2}=A\times A\), and, in general, \(A_{k}=A\times A\times\ldots\times A^{k}\) (\(k\) times). To capture the cumulative neighborhood information up to \(K\) hops, the \(K\)-order adjacency matrix \(\bar{A}^{K}\) is introduced, which includes all neighbors from one hop to \(K\) hops. This matrix is computed by summing all adjacency matrices from each order: \(\bar{A}^{K}=\sum_{k=1}^{K}A^{k}\), where \(K\) is a hyperparameter representing the maximum order of the neighborhood considered in the model. In the field of Smart Enterprise Management System (EMS) knowledge graphs, a Tensor-based Graph Attention Network called MR-GAT was proposed to enhance the accuracy and depth of the fusion of dense and multi-relational knowledge graphs. A Relation Attention Mechanism and joint Entity and Relation Alignment Framework were used to enhance Knowledge Fusion accuracy. The study of three datasets demonstrated the superiority of the proposed MR-GAT in representation learning for Knowledge Fusion on Smart EMS [57]. A method combining a graph-level attention network and a graph neural network (GNN) has been presented to predict lncRNA-disease associations.