## 3 Graph Attention Network Categories
### Graph-Embedding GATs
A system using heterogeneous graphs, self-enhanced graph attention networks, and tri-aggregator neural networks [39] identifies drug-virus associations, outperforming existing models and identifying potential SARS-CoV-2 treatments. GANLDA (graph attention network for lncRNA-disease associations) [40] predicts lncRNA-disease associations by combining lncRNA and disease heterogeneous data, showing significant improvement over current methods. CAMT (Context-Aware method to learn invocations patterns and descriptions for Mashup Tagging) [41], a context-aware mashup tagging algorithm, leverages neural networks to consider high-level linkages in two graphs and a multi-head attention mechanism to differentiate adjoining mashups' significance. SGANM (self-adaptation graph attention network via meta-learning) [42], a self-adaptive graph attention network, employs meta-learning to quickly recognize new fault types with few samples, outperforming other few-shot learning algorithms. GATust [43], a trust assessment framework for online social networks, integrates information from different aspects and uses graph attention and convolutional networks to generate accurate trust predictions, showing improvements of 4.3% and 5.5%. GSCS [44] employs graph attention networks, an RNN-based sequence model, and a transformed embedding layer to generate high-quality Java method summaries. In predicting drug-drug interactions (DDIs) for poly-drug treatments, DGAT-DDI [45] encapsulates information from drug sources, targets, and self-roles, outperforming state-of-the-art models. The Attention-Gated Conditional Random Fields (AG-CRF) model [46] learns and fuses representations for pixel-level prediction, achieving state-of-the-art performance in monocular depth estimation, object contour prediction, and semantic segmentation. GAT_SCNet [8] is a graph attention network for recognizing road markings from point clouds generated by Mobile Laser Scanning systems. With results exceeding 91% under three criteria, GAT_SCNet sets a new state-of-the-art in linear road marking recognition. MV-GAN [22], a travel recommendation model, incorporates user and product representations from multiple sources and efficiently combines node representations to capture user and product patterns. To capture the latent embeddings of both users and products, the model incorporates an embedded propagation layer linking the user and product entities. The embedding update mechanisms for users and products are governed by propagation-based and pooling-based rules, which can be formulated as follows:  
\[\mathbf{u}_{i}^{0,l+1} =\sigma\Big{(}\mathbf{W}^{l+1}\times\Big{(}\mathbf{u}_{i}^{0,l}+ \text{AGG}\Big{(}\mathbf{v}_{j}^{0,l}\mid j\in\mathcal{N}_{ui}\Big{)}+\mathbf{ b}^{l+1}\Big{)}\Big{)}, \tag{1}\] \[\mathbf{v}_{j}^{0,l+1} =\sigma\Big{(}\mathbf{W}^{l+1}\times\Big{(}\mathbf{v}_{j}^{0,l}+ \text{AGG}\Big{(}\mathbf{u}_{i}^{0,l}\mid i\in\mathcal{N}_{nj}\Big{)}+\mathbf{ b}^{l+1}\Big{)}\Big{)}, \tag{2}\]  
where \(\mathbf{u}_{i}^{0,l}\in\mathbb{R}^{D\times 1}\) is the free embedding of user \(u_{i}\) on the \(l\)-th layer, and \(\mathbf{v}_{j}^{0,l}\in\mathbb{R}^{D\times 1}\) is the free embedding of product \(v_{j}\) on the \(l\)-th layer. \(D\) is the embedding size. \(\mathbf{W}^{l+1}\in\mathbb{R}^{D\times D}\) and \(\mathbf{b}^{l+1}\in\mathbb{R}^{D\times D}\) are the learned weight and learned bias at step \(l+1\). \(\sigma\) is a nonlinear activation function, specifically LeakyReLU. \(\mathcal{N}_{ui}\) represents the travel products clicked by user \(u_{i}\), and \(\mathcal{N}_{vj}\) denotes the users that click item \(v_{j}\). \(\text{AGG}(\cdot)\) is the aggregation function, such as averaging or max-pooling operation. AR-KGAN [47] jointly embeds fact triplets and logical rules to complete knowledge bases with enhanced accuracy, outperforming existing methods on three benchmark datasets.