Graph Attention Networks

Graph Attention Networks (GATs) are a type of machine learning model designed to analyze data represented as graphs, where entities are nodes and their relationships are edges. GATs enable each node to selectively focus on and learn from its neighboring nodes by assigning different importance weights, or attention scores, to each connection. This process allows the network to adaptively emphasize more relevant relationships, leading to better understanding of complex, interconnected data such as social networks, molecules, or knowledge graphs. Overall, GATs improve how models interpret and learn from structured information by dynamically prioritizing important connections.