## 3 Graph Attention Network Categories
### Hybrid GATs
This approach excited the far-field sources incident on the aperture using a set of spatio-temporally incoherent modes to encode and compress the spectrum of the sources into a single channel, eliminating the need for a reconstruction step. In a low signal-to-noise ratio (SNR) environment, the proposed GAT integrated a single-pixel DoA framework that could accurately retrieve the DoA information. RelMN [107], is a deep sparse graph attention network for object recognition and relationship categorization in visual scenes. It classified object pairs from denser graphs into foreground and background groups, utilized sparse graphs with message passing, and surpassed state-of-the-art results on multiple benchmark datasets. PGAT [108] is a path-enhanced bidirectional GAT that accurately predicts quality indices of a manufacturing process. It utilized graph attention networks to learn the relationships between the different machines in the multistage process and incorporated dependency path information into the machine features. A masked loss function was used to address the label noise problem and batch training could be used for improved efficiency. Experiments on a real-world production line dataset validated the effectiveness of the proposed approach compared to existing methods. PD-RGAT [109] is a model that has been proposed for aspect-based sentiment analysis, which considered phrase information and direction of dependency when constructing the graph. It was able to effectively capture long-range dependencies and investigate aspect-sentiment polarities, with similar effectiveness to state-of-the-art models. In addition, a graph attention network model for predicting potential miRNA-disease associations from existing data outperformed the state-of-the-art models [110]. It potentially provides a better understanding of numerous biological processes relevant to human disease. Value Decomposition with Graph Attention Network (VGN) [111], is an approach that factored the joint reward function into individual reward functions. Two graph neural network-based algorithms, VGN-Linear and VGN-Nonlinear, were developed and tested on the StarCraft Multiagent Challenge (SMAC) benchmark. VGN methods surpassed existing value-based multi-agent reinforcement simulations in difficult tasks. GATs and ADFP-AC [112] were used for PBT chemical screening and eight new classes of PBT chemicals were identified from the Inventory of Existing Chemical Substances in China. AL-NEGAT [113] is an adversarial learning-based node edge GAT that has been designed for the identification of autism spectrum disorder (ASD) based on multimodal MRI data. This model achieved higher accuracy and better generalizability than existing state-of-the-art methods, making it a promising tool for the identification of brain disorders. The combination of a convolutional neural network (CNN) and graph neural network (GNN) in WFCG for HSI (hyperspectral image) classification has been proposed [114]. It utilized a GAT and CNN to create a weight fusion of their respective features, achieving comparable results to state-of-the-art methods. ASEGAT, is a brain cortical surface parcellation method [115] that employed a graph attention module and a squeeze-and-excitation module to recognize node features and incorporated anatomical prior information to improve the accuracy of region labeling. Experiments on a public dataset showed that ASEGAT outperformed other methods, achieving an accuracy of 90.65% and a dice score of 89.00%. CGAT was presented for ground-based remote sensing cloud classification [116]. It made use of Context Attention Coefficients and two transformation matrices to enhance the discrimination of aggregated features (AFs). Additionally, a new GCD was published, and experiments were conducted to prove the effectiveness of the model. GNNImpute, is a single-cell dropout imputation method, that was introduced and tested on various real datasets [117]. It was found that GNNImpute provided accurate and effective imputation of dropouts and reduced dropout noise.