## 3 Graph Attention Network Categories
### Hybrid GATs
It also produced good metric results on clustering, with ARI and NMI reaching 0.8199 and 0.8368, respectively. HGAT-AMR, a deep graph learning method, was proposed to predict anti-TB drug resistance [118]. This method enabled the consideration of incomplete phenotypic profiles and provided attention scores to identify genes and SNPs associated with drug resistance. The Hierarchical Graph Attention Network (HGAT) [119] was proposed to accurately analyze sentiment information with its complicated semantic relations for e-commerce platforms. Experiments were conducted, and it was found to outperform other baselines. A MARL problem was proposed to manage resources in a cellular network using GAT and DRL, leading to an efficient inter-slice resource management strategy [120]. GAT was applied over both DQN and A2C, and its superiority was verified through simulations. GATNNCDA [121] was developed as a novel method that could efficiently model and integrate microRNA-disease relationships and predict new potential associations. A molecule-editing graph attention network (MEGAN) was created as a neural model for automated synthesis planning, based on models that described a chemical reaction as a model of graph edits, similar to arrow pushing [122]. The authors adapted it for retro-synthesis and enlarged the dataset size, providing state-of-the-art accuracy in standard benchmarks. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Name** & **Year** & **Citation Count** & **Cites Per Year** \\ \hline N/A [29] & 2019 & 115 & 28.75 \\ \hline Mgat [84] & 2020 & 44 & 14.67 \\ \hline N/A [68] & 2022 & 40 & 40.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Top-cited graph attention network (GAT)-based papers the last years with a minimum of 10 citations. A novel message-dependent attention mechanism was introduced to improve graph neural networks in multi-agent path planning [124], and the model was more capable on large-scale path planning tasks in a decentralized setting, consistently achieving better results than other benchmark models. Hawk [127] is a malware detection framework for Android applications that modeled relationships between Android entities to reveal hidden, higher-order relationships to better detect malicious behaviour. It offered the highest detection accuracy, with a detection time of 3.5 ms for out-of-sample applications and a much quicker training time at 50\(\times\) faster than traditional methods. CRF-GAT [129], combined the advantages of conditional random fields and graph attention networks to \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Name** & **Year** & **Citation Count** & **Cites Per Year** \\ \hline WFCG [114] & 2022 & 39 & 39.00 \\ \hline HGAT [77] & 2020 & 37 & 12.33 \\ \hline HGAT [28] & 2021 & 34 & 17.00 \\ \hline Mgat [83] & 2020 & 32 & 10.67 \\ \hline N/A [123] & 2020 & 31 & 10.33 \\ \hline MAGAT [124] & 2021 & 28 & 14.00 \\ \hline N/A [125] & 2021 & 25 & 12.50 \\ \hline GATrust [43] & 2022 & 25 & 25.00 \\ \hline N/A [103] & 2022 & 23 & 23.00 \\ \hline GATMDA [126] & 2021 & 22 & 11.00 \\ \hline MGA-Net [52] & 2022 & 20 & 20.00 \\ \hline Hawk [127] & 2021 & 19 & 9.50 \\ \hline ResGAT [26] & 2021 & 19 & 9.50 \\ \hline MEGAN [122] & 2021 & 19 & 9.50 \\ \hline GANLDA [40] & 2022 & 19 & 19.00 \\ \hline SRGAT [128] & 2021 & 18 & 9.00 \\ \hline PD-RGAT[109] & 2022 & 18 & 18.00 \\ \hline HLGAT [76] & 2021 & 18 & 9.00 \\ \hline HGAT [11] & 2020 & 16 & 5.33 \\ \hline RRL-GAT [19] & 2022 & 16 & 16.00 \\ \hline N/A [82] & 2021 & 16 & 8.00 \\ \hline HEAT [18] & 2022 & 15 & 15.00 \\ \hline ASTGAT [66] & 2022 & 14 & 14.00 \\ \hline RA-AGAT [9] & 2022 & 14 & 14.00 \\ \hline SSGAT [72] & 2022 & 12 & 12.00 \\ \hline MDGAT [25] & 2021 & 12 & 6.00 \\ \hline FTTG [65] & 2022 & 12 & 12.00 \\ \hline Gchgat [49] & 2022 & 11 & 11.00 \\ \hline HGATLDA [50] & 2022 & 10 & 10.00 \\ \hline STGGAT [82] & 2022 & 10 & 10.00 \\ \hline PSCR [81] & 2021 & 10 & 5.00 \\ \hline EGAT [71] & 2022 & 10 & 10.00 \\ \hline KGAT [35] & 2022 & 10 & 10.00 \\ \hline \hline \end{tabular}
\end{table}