## 3 Graph Attention Network Categories
### Spatial GATs
ASTGAT [66] is an adaptive spatial GAT that simultaneously discovers dynamic graph structures and spatial-temporal associations for traffic flow prediction. It outperformed existing models on the METR-LA and PEMS-BAY datasets, as demonstrated by both numerical and visual evaluations of forecasted traffic flow. For predicting pedestrian trajectories in unpredictable environments, a graph attention-based model called PTPGC was introduced in [67]. It leverages TCN and ConvLSTM in a dynamic graph to capture pedestrian features and spatial connections. Results on two datasets demonstrate PTPGC's improved trajectory prediction performance over existing baselines. A novel self-adaptive graph attention network, SGANM, is introduced for quick recognition of new fault types with limited data samples. SGANM utilizes meta-learning strategies for enhanced meta-knowledge learning ability, resulting in improved few-shot learning performance on benchmark datasets and a practical platform [42]. A mutually supervised few-shot segmentation network was proposed in [68] for image segmentation. The model utilizes feature maps from the middle convolution layers and merges the support image and query image into a two-sided graph, using graph attention network for graph reasoning for segmentation. Experimental results demonstrate the effectiveness of the proposed model. KGANCDA is introduced for predicting circRNA-disease connections. The model utilizes a knowledge graph attention network to capture low-level and high-level neighbor information from diverse associations. The KGANCDA model outperforms existing methods in cross-validation and a case study demonstrates its effectiveness for predicting circRNA-disease connections [49]. A scheduling algorithm based on graph attention networks was proposed for controlling multiple robots in a warehouse, which outperformed existing methods when trained with imitation learning. The approach required no expert knowledge and was applicable to large-scale problems [69]. The Spatiotemporal Gated Graph Attention Network (STGGAT) [70] model was introduced for predicting traffic flow by utilizing License Plate Recognition (LPR) records to calculate average travel times and volume transition relationships. The model combined a gated recurrent unit layer, a graph attention network layer with edge features, a bidirectional long short-term memory, and a residual structure. Validation with the LPR system of Changsha, China showed that STGGAT was more accurate and reliable than existing baseline models and was capable of inductive learning and fault tolerance. EGAT [71] was created for object detection in high spatial resolution remote sensing imagery (HSRI). The network was able to access important information of the rationale graphs to distinguish between different spatial-semantic correlations by introducing a long short-term memory (LSTM) device into EGAT. The experiments showed that the strategy provided state-of-the art results and was an effective method for object detection in HSRI. SSGAT [72], a novel model for HSI classification, was designed to combine information of labeled and unlabeled samples in an unsupervised manner in order to address the issue of the insufficiency of labeled samples. Evaluation results showed its effectiveness and superiority over existing approaches. A self-adaptive graph convolution network (GCN), an encoder-decoder network, and a conditional random field (CRF) algorithm were utilized to enhance the semantic segmentation of laser scanning (LS) data. Promising results were achieved for multiple objects using the ParisLille-3D, Semantic3D, and vKITTI datasets [73]. TAGAT [74] is designed to consider type-related information during the embedding process and used a hierarchical attention mechanism to increase interpretability of the embedding space and improve reasoning performance. A unique air temperature forecasting system was created using graph attention networks and gated recurrent units, resulting in better predictions than existing benchmark models [75].