A Graph Attention Network (GAT) is a neural network architecture that operates on graph-structured data, leveraging masked self-attentional layers to allow the model to learn from graph-structured data [1]. GATs have been used for a range of tasks, including recommendation systems, natural language processing, and computer vision. GATs use an attention-based mechanism to learn the graph structure and generate an embedding for each node in the graph. The embeddings are then used in downstream tasks such as classification or regression. GATs have been shown to outperform other graph neural networks, such as Graph Convolutional Networks (GCNs), on a variety of taskS.
Attention mechanisms are a type of neural network architecture that allows a network to focus on certain inputs and ignore others. They were introduced in the paper "Attention Is All You Need" by Vaswani et al. The paper explains how the attention mechanism works by assigning weights to input elements based on the relevance of the input to the task being performed. The attention mechanism is composed of two parts: a query and a key. The query is used to identify which elements in the input are important, while the key is used to determine how important each element is. The attention mechanism then computes a weighted sum of the input elements, which is used to produce the output.
Attention mechanisms have been used in a variety of tasks, including natural language processing, machine translation, and question answering. Additionally, attention mechanisms have been used to improve the performance of graph neural networks (GNNs), such as Graph Attention Networks (GATs) and Graph Convolutional Networks (GCNs). Attention mechanisms have also been used to improve the performance of transformer-based models [1], such as the Transformer model.
https://paperswithcode.com/method/gat
