Graph Attention Networks (GAT) is a deep learning model designed for graph data. It leverages self-attention mechanisms to aggregate information from neighboring nodes in a graph, allowing it to capture complex dependencies between nodes. GAT models have been successfully applied to tasks such as node classification, link prediction, and graph classification.
The key idea behind GAT is to assign different importance weights to different neighboring nodes during the aggregation process. This is accomplished by using attention mechanisms, where each node learns its attention weights based on its features and the features of its neighbors. Nodes with more relevant information receive higher attention weights, enabling the model to focus on important nodes and capture local patterns effectively.
Node Classification: GAT can be used to classify nodes in a graph based on their features and the graph structure. This is useful in applications such as predicting protein function from protein-protein interaction networks or predicting user preferences in social networks.
Link Prediction: GAT can be applied to predict missing or future links in a graph. This is valuable in recommender systems, where the model can predict potential connections between users and items to provide personalized recommendations.
Graph Classification: GAT can be utilized to classify entire graphs into different categories. This is relevant in tasks such as chemical compound classification or document categorization based on citation networks.