Graph Attention Networks (GATs) are a type of neural network designed to operate on graph-structured data, incorporating attention mechanisms to weigh the importance of different nodes and edges. This allows GATs to dynamically focus on relevant parts of a graph when making predictions, enhancing their ability to learn from complex relationships inherent in graph data. By utilizing attention, GATs can capture both local and global structural information, making them highly effective for tasks such as node classification and link prediction.
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