Graph Attention Networks (GATs) are novel neural network architectures designed for graph-structured data. They use masked self-attentional layers to overcome limitations of previous graph convolution methods. By allowing nodes to attend to their neighbors' features, GATs implicitly assign different weights to different nodes in a neighborhood without costly matrix operations or prior knowledge of the graph structure. This approach addresses key challenges of spectral-based graph neural networks and enables application to both inductive and transductive tasks. GATs have achieved or matched state-of-the-art results on four established benchmarks: Cora, Citeseer, Pubmed citation networks, and a protein-protein interaction dataset. The GAT architecture consists of a graph attentional layer that computes node features through self-attention. This layer applies a shared attention mechanism to compute attention coefficients, which are normalized using the softmax function. Multi-head attention is used to enhance performance, with each head independently computing attention coefficients and then combining them. The layer's output is a linear combination of the features of the neighbors, weighted by the attention coefficients. Compared to prior methods, GATs are computationally efficient, allowing for different importances to be assigned to nodes in a neighborhood without requiring knowledge of the entire graph structure. They are also applicable to inductive learning tasks, where the model generalizes to unseen graphs. GATs have been shown to outperform other methods on both transductive and inductive tasks, achieving state-of-the-art results on multiple benchmarks. The effectiveness of GATs is demonstrated through experiments on various datasets, including citation networks and a protein-protein interaction dataset. The models show improved performance compared to existing methods, particularly in tasks where the entire neighborhood is considered. The results highlight the potential of attention-based models in handling arbitrarily structured graphs. GATs are also compared to other approaches such as GCNs and GraphSAGE, showing their competitive performance and ability to capture complex relationships in graph data. The models are initialized with Glorot initialization and trained using the Adam optimizer, achieving high accuracy on both transductive and inductive tasks. The results confirm that GATs can achieve or match state-of-the-art performance across multiple graph-based benchmarks.Graph Attention Networks (GATs) are novel neural network architectures designed for graph-structured data. They use masked self-attentional layers to overcome limitations of previous graph convolution methods. By allowing nodes to attend to their neighbors' features, GATs implicitly assign different weights to different nodes in a neighborhood without costly matrix operations or prior knowledge of the graph structure. This approach addresses key challenges of spectral-based graph neural networks and enables application to both inductive and transductive tasks. GATs have achieved or matched state-of-the-art results on four established benchmarks: Cora, Citeseer, Pubmed citation networks, and a protein-protein interaction dataset. The GAT architecture consists of a graph attentional layer that computes node features through self-attention. This layer applies a shared attention mechanism to compute attention coefficients, which are normalized using the softmax function. Multi-head attention is used to enhance performance, with each head independently computing attention coefficients and then combining them. The layer's output is a linear combination of the features of the neighbors, weighted by the attention coefficients. Compared to prior methods, GATs are computationally efficient, allowing for different importances to be assigned to nodes in a neighborhood without requiring knowledge of the entire graph structure. They are also applicable to inductive learning tasks, where the model generalizes to unseen graphs. GATs have been shown to outperform other methods on both transductive and inductive tasks, achieving state-of-the-art results on multiple benchmarks. The effectiveness of GATs is demonstrated through experiments on various datasets, including citation networks and a protein-protein interaction dataset. The models show improved performance compared to existing methods, particularly in tasks where the entire neighborhood is considered. The results highlight the potential of attention-based models in handling arbitrarily structured graphs. GATs are also compared to other approaches such as GCNs and GraphSAGE, showing their competitive performance and ability to capture complex relationships in graph data. The models are initialized with Glorot initialization and trained using the Adam optimizer, achieving high accuracy on both transductive and inductive tasks. The results confirm that GATs can achieve or match state-of-the-art performance across multiple graph-based benchmarks.