## 3 Graph Attention Network Categories
### Multi-Layer Graph Attention Networks  
Multi-Layer Graph Attention Networks (Multi-Layer GATs) are a subcategory of graph attention networks (GATs), which involve stacking multiple attention layers to capture complex higher-level features in graph-structured data. Multi-Layer GATs stack multiple layers of attention to further enhance their ability to learn from graph-structured data. In this architecture, each layer learns a new set of node features based on the features of neighboring nodes, with the output from one layer being fed as input to the next layer. This enables Multi-Layer GATs to learn more abstract, higher-level features by aggregating information from a larger neighborhood of nodes in the graph. Towards this direction, FinGAT [13] is a deep learning-based model that leverages Multi-Layer GATs for stock recommendations, capturing short- and long-term temporal patterns from stock price timelines using fully connected graphs among stocks and sectors. In a related work, sparse graph attention networks (SGATs) [14] were designed to identify and sparse out irrelevant or noisy edges in graph-structured data by learning sparse attention coefficients. Another study introduced HGHAN [15], which identified hacker groups using a heterogeneous graph attention network (HAN), outperforming other heterogeneous graph node embedding algorithms. A graph-based circRNA-disease association prediction method [16] was also presented, showing improved performance compared to existing methods. The DuGa-DIT model [17] is a dual gated graph attention model with dynamic iterative training, addressing problems in traditional entity matching techniques and validating its effectiveness on benchmark datasets and a cross-lingual personalized search case. A three-channel approach, including a Heterogeneous Edge-enhanced graph ATtention network (HEAT) [18], was proposed to improve the decision-making and planning modules of autonomous vehicles by accurately predicting multiple agent trajectories. Robust Representation Learning (RRL-GAT) [19] was developed for more accurate multi-label image characterization, employing a Class Attention Graph Convolution Module (C-GAT) and an Adaptive Graph Attention Convolution Module (A-GAT) to detect the communication structure of categories and assess the dynamic connection between objects. A deep learning model based on the hierarchical graph attention network for miRNA-disease associations (HGANMDA) [20] was developed to predict miRNA-disease associations, out performing existing methods. A virtual sensor-based imputed graph attention network [21] was proposed to improve anomaly detection in complex equipment with incomplete data. The MV-GAN model [22] was developed for travel recommendation, incorporating user and product representations from multiple sources and applying a view-level attention mechanism for efficient combining of node representations. The CellVGAE model [23] is a graph autoencoder model for studying scRNA-seq data, outperforming other deep learning architectures in terms of training times. GAT-LI [24] is a graph learning and interpretation system that utilizes GAT2 and GNNExplainer to better understand autism spectrum disorders and the underlying biological mechanisms. The MDGAT architecture and matcher [25] applied an attention method to 3D point clouds, improving data association between 3D points for LiDAR-based SLAM systems and mapping. An innovative topology-adaptive, high-speed transient stability assessment (HSTSA) scheme using a novel multi-graph attention network with a residual structure (ResGAT) and a new piece-wise transient stability index (PSI) [26] was proposed, showing superior accuracy and resistance to different scenarios when tested on an IEEE 39-bus system and IEEE 300-bus system. HGATMDA [27] is a new method for predicting miRNA-disease associations, using a heterogeneous graph to extract features and applying a neural network for predictions.