## 3 Graph Attention Network Categories
### Hybrid GATs  
Hybrid Graph Attention Networks (GATs) are a type of computational framework that combine multiple technologies to perform optimally in diverse tasks. These networks have been used in various applications, including miRNA-disease connection discovery, multi-agent dynamic traffic control, knowledge graph completion, character representation, and session-based recommendation. GATMDA [85], is a computational framework that applied graph attention network and multi-source information to uncover miRNA-disease connections. GATMDA achievedan average AUC of 0.9566 and identified a high percentage of verified candidates. DQ-GAT [86] is a graph attention-based deep Q learning model that was proposed for multi-agent dynamic traffic scenarios. DQ-GAT yielded higher success rates and better balancing between safety and efficiency than previous deep learning and traditional rule-based approaches in both seen and unseen scenarios. A multi-relational graph attention network (MRGAT) was proposed for knowledge graph completion [87]. Experimental results demonstrated the superiority of MRGAT compared to other models. In the field of character representation, a polymorphic grapheme attention network has been presented [88] to dynamically capture the relevance between characters and their corresponding words. This improved the performance of character representation in all four dimensions and resulted in significant performance improvements in experiments. CKSR is a knowledge-aware session-based recommendation model, proposed in [89], that combined a cross-session graph and a knowledge graph to create a cross-session knowledge graph. The CKSR model used this graph to capture transition patterns among interacted items and outperformed other SBR methods in experiments on two benchmark datasets. A multi-view framework for anomaly detection of Internet routing traffic has been presented [90]. It utilizes seasonal and trend decomposition combined with a graph attention network to recognize relationships among multiple features and correlations in time. The framework achieved improved anomaly detection performance, with an F1 score of 96.3% and 93.2% on balanced and imbalanced datasets, respectively. Moreover, the proposed framework could be extended to detect unseen anomalous events. GTGenie [91] was proposed as a computational model for discovering biomarker-disease associations. The model made use of graph attention networks and pre-trained BERT-based models and achieved competitive performance on a variety of benchmark datasets. OmicsGAT [92] is a graph attention network, designed for analyzing RNA-seq data to identify deeper relationships between gene expression and network structure for cancer subtype analysis, patient stratification, and cell clustering. The algorithm's multi-head attention mechanism gave attention coefficients to a sample's neighbors, allowing the capture of information related to a sample more effectively and giving visibility into the importance of particular neighbors in any specific sample's cancer subtype analyses. An RVTR [93] graph attention network-based RNA virus transmission network representation model made use of natural language processing and a graph context loss function to more accurately train the model in detecting asymptomatic propagators of COVID-19. GTGAT [94] is a gated tree-based graph attention network that improved upon the success of graph attention networks (GAT) for transductive and inductive reasoning in generalized knowledge graphs. The approach was successful in transductive tests and outperformed existing methods when applied to medical knowledge graphs in inductive tasks. GANet [95], is a deep learning-based model, which was proposed to obtain point correspondences from two-view images, resulting in improved mean Average Precision (mAP) by up to 1.5% and 0.6% on the YFCC and SUN3D datasets, respectively.