## 3 Graph Attention Network Categories
### Spatial GATs
The proposed method, called gGATLDA, outperformed other methods and demonstrated its efficacy in identifying lncRNAs associated with different types of cancer such as breast cancer, gastric cancer, prostate cancer, and renal cancer [58]. A hybrid framework called HFGAT was introduced, which combined both global and local characteristics of a compound to predict its metabolic pathways. The framework leveraged Spatial graph attention networks and provided a valuable method for drug discovery by determining all the metabolic reactions involved in the decomposition and synthesis of pharmaceutical compounds. The proposed method outperformed traditional machine learning methods and graph convolutional network-based methods in terms of multi-class classification accuracy and F1 scores [7]. TrajGAT is a deep learning-based system that integrates spatial and temporal data from GPS trajectories to recover missing observations [59]. It splits the problem into two parts: trajectory prediction based on existing data, and substitution of the missing observations. A rule-based graph attention network and a vectorized lane-level map were used to analyze dynamic spatial patterns, while an encoder-decoder structure extracted and fused temporal features. TrajGAT showed superior performance compared to other models with a strong resilience and wide range of missing trajectory rates. In EEG analysis, a deep learning-based approach was proposed for automated epileptic seizure detection, utilizing spatial and temporal data from EEG channels [60]. A deep spatial-temporal convolutional graph attention network was proposed to capture traffic dependencies among different regions, leveraging multi-resolution transformer networks combined with attentive graph neural networks and convolutional networks [61]. Additionally, a channel-aware recalibration residual network was designed to inject spatial contextual signals into the framework, resulting in a 5% performance improvement. MetaSTGAT [62] was introduced as a meta-learning system for efficient management of traffic signals in urban transportation systems and public transportation. The dynamic weight generation system could detect changing features of graph nodes, enabling the system to adjust to dynamic intersection traffics, leading to a reduction in travel time by up to 19.30%. An attention-based spatiotemporal graph attention network [63] was proposed for traffic flow forecasting, which could accurately capture past, current, and future temporal relationships. The model was evaluated against traditional and prevalent methods, demonstrating superior performance when predicting medium to long-term traffic flow forecasting. An attention-based spatiotemporal graph attention network [63] was proposed and evaluated for traffic flow forecasting, achieving accurate computation of past, present, and future temporal relationships. Comparing the results to traditional and prevalent methods, this model outperformed the baselines when predicting medium- to long-term outcomes. EvoSTGAT [64] is a Changing Spatiotemporal Graph Attention Network that was introduced for predicting approaching pedestrian paths, using a fluctuating and dynamic attention strategy to account for the social impact of current procedural pedestrians. The model was tested on two intricate datasets, verifying its proficiency. FTPG uses a graph attention network (GAT) to accurately and sturidly predict traffic information at intersections [65]. In addition, an approach for estimating the position of the starting point of the queue was proposed, and a spatiotemporal residual graph attention network (ST-RGAN) was employed to further improve prediction precision. GAT_SCNet, ref. [8] was designed for recognizing various categories of road markings from point clouds produced by Mobile Laser Scanning systems (MLSs). Tests were conducted on a total of 100 km captured by different MLS systems, with results exceeding 91% under three criteria (average precision, recall, and F1).