## 3 Graph Attention Network Categories
### Hybrid GATs
GCHGAT [96] is a group-constrained hierarchical graph attention network that has been presented for predicting pedestrian trajectories, incorporating pair-wise and group-wise interactions between individuals. GCHGAT achieved superior performance with the smallest prediction error when compared to other methods on the ETH and UCY datasets. In the field of fake news detection, a graph attention network-based approach was created to identify potential fake news on social networks by analyzing user-user connections and content graphs. The method demonstrated improved accuracies and F1 scores compared to existing methods, as demonstrated through experiments conducted on two datasets [97]. TGAT [98] is a multi-relational graph attention network framework that was introduced to bridge the gap between IoT data and intelligent applications and services. TGAT can capture rich interactions between mixed triples, entities, and relationships, while the Tucker model was employed to reduce storage and calculation consumption. TGAT achieved up to 7.6% improvement in the hits@1 accuracy compared to other models on real-world heterogeneous graphs. A model [99] that combined convolutional neural networks and a graph neural network was presented for predicting crowd flow in cities partitioned into irregular regions based on road networks and functionality. The model utilized a location-aware and time-aware graph attention mechanism called Semantic Graph Attention Network (Semantic-GAT) that relied on dynamic node attribute embedding and multi-view graph reconstruction. Experimental results showed that the model effectively reduced the prediction error. A heterogeneous graph attention network was proposed to detect and forecast food safety risks based on various variables. The proposed risk profile was tested on a dataset from China and was found to be accurate [100]. GraphReg [101] is a deep learning technique that models the effect of non-coding genetic variations on target gene expression. It uses 3D interactions from chromosome conformation capture tests to predict gene expression and is better than current state-of-the-art deep learning approaches. It can also predict direct transcription factor targets by in silico deletion of other transcription factor binding motifs. In the field of autonomous driving, DRL-GAT-SA [102] has been proposed. It is an autonomous driving safety system that combines graph attention reinforcement learning and dynamic safety assurance for efficient driving in uncertain conditions. A deep semantic information propagation method [103] was proposed for aligning a single labeled source domain and multiple unlabeled target domains. Experiments on four public datasets showed that this method outperformed other prominent domain adaptation methods. A virtual sensor-based imputed graph attention network [21] that could improve anomaly detection in complex equipment with incomplete data by combining real signals and simulated signals with a graph attention network has also been presented. A Learning from Demonstration (LfD) approach using graph attention networks (GATs) was presented in [104]. The approach demonstrated how a robot could learn non-interactive, interactive, and uni- or bi-manipulative operations from a human by receiving human hand paths and goal object poses. It was tested on simulated data and through real-life experiments, and the results showed that the robot could successfully learn the tasks. EGRET [105] is a transfer learning-based approach, which had yielded increased evidence of protein-protein interaction sites over alternate methods. EGRET was available on open-source GitHub, and the network behavior had been studied to explain how the decisions were made. A single-pixel compressive direction of arrival (DoA) estimation technique has been proposed [106]. It uses a graph attention network (GAT)-based deep-learning framework that leveraged metasurface antenna-based coded aperture to reduce the physical layer dimension.