## 4 Applications of Graph Attention Networks
### Recommendation
In this vein, graph attention networks (GATs) have been increasingly employed in the development of diverse recommender system applications, including but not limited to movie and media recommendation [144], travel and tourism recommendation [22; 145], finance and stock recommendation [9; 146], health and medicine recommendation [147], web services and API recommendation [148; 149], as  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Language** & **Repository** & **Framework-Related Paper** \\ \hline GAT & Python & [[https://github.com/Petaw/GAT](https://github.com/Petaw/GAT)]([https://github.com/Petaw/GAT](https://github.com/Petaw/GAT)) & [130] \\ \hline PyGAT & Python & [[https://github.com/Dispe99/pyGAT](https://github.com/Dispe99/pyGAT)]([https://github.com/Dispe99/pyGAT](https://github.com/Dispe99/pyGAT)) & [130] \\ \hline keras-gat & Python & [[https://github.com/danielegrators/keras-gat](https://github.com/danielegrators/keras-gat)]([https://github.com/danielegrators/keras-gat](https://github.com/danielegrators/keras-gat)) & [130] \\ \hline pytorch,geometric & Python & [[https://github.com/pygteam/pytorch,geometric](https://github.com/pygteam/pytorch,geometric)]([https://github.com/pygteam/pytorch,geometric](https://github.com/pygteam/pytorch,geometric)) & [131] \\ \hline GATv2 & Python & [[https://nn.labml.ai/graphs/gatv2/index.html](https://nn.labml.ai/graphs/gatv2/index.html)]([https://nn.labml.ai/graphs/gatv2/index.html](https://nn.labml.ai/graphs/gatv2/index.html)) & [132] \\ \hline anomaly-detection-resources & Python & [[https://github.com/ydabo62/anomaly-detection-resources](https://github.com/ydabo62/anomaly-detection-resources)]([https://github.com/ydabo62/anomaly-detection-resources](https://github.com/ydabo62/anomaly-detection-resources)) & N/A \\ \hline dgl & Python & [[https://github.com/dmic/dgl](https://github.com/dmic/dgl)]([https://github.com/dmic/dgl](https://github.com/dmic/dgl)) & [133] \\ \hline dgl-lifsci & Python & [[https://github.com/savals/dgl-lifsci](https://github.com/savals/dgl-lifsci)]([https://github.com/savals/dgl-lifsci](https://github.com/savals/dgl-lifsci)) & [134] \\ \hline dgl-like & Python & [[https://github.com/avals/dgl-like](https://github.com/avals/dgl-like)]([https://github.com/avals/dgl-like](https://github.com/avals/dgl-like)) & [135] \\ \hline benchmarking-gms & Python & [[https://github.com/ggraphdeplerning/benchmarking-gms](https://github.com/ggraphdeplerning/benchmarking-gms)]([https://github.com/ggraphdeplerning/benchmarking-gms](https://github.com/ggraphdeplerning/benchmarking-gms)) & [136] \\ \hline graph4 nlp & Python & [[https://github.com/graph4](https://github.com/graph4) ai/graph4 nlp]([https://github.com/graph4](https://github.com/graph4) ai/graph4 nlp) & [137] \\ \hline GNN-RecSys & Python & [[https://github.com/je-dbl/CNN-RecSys](https://github.com/je-dbl/CNN-RecSys)]([https://github.com/je-dbl/CNN-RecSys](https://github.com/je-dbl/CNN-RecSys)) & N/A \\ \hline GNNLess2 & Python & [[https://github.com/dmlc/GNNLess2](https://github.com/dmlc/GNNLess2)]([https://github.com/dmlc/GNNLess2](https://github.com/dmlc/GNNLess2)) & [138] \\ \hline RNAglib & Python & [[https://github.com/joebrod15/RNAGLib](https://github.com/joebrod15/RNAGLib)]([https://github.com/joebrod15/RNAGLib](https://github.com/joebrod15/RNAGLib)) & [139] \\ \hline OpenIRCNN & Python & [[https://github.com/BUIT-GAMMA/OpenIRCNN](https://github.com/BUIT-GAMMA/OpenIRCNN)]([https://github.com/BUIT-GAMMA/OpenIRCNN](https://github.com/BUIT-GAMMA/OpenIRCNN)) & [140] \\ \hline tgl & Python & [[https://github.com/am](https://github.com/am)]([https://github.com/am](https://github.com/am))well as e-commerce and package recommendation [150; 151]. This highlights the versatility and potential of GATs in advancing the state-of-the-art of the recommender system domain.