GATs

GATs are an extension of GCNs that incorporate an attention mechanism to assign different weights to neighboring nodes based on their relevance. While GCNs apply a fixed aggregation function to combine the features of neighboring nodes, GATs allow for a more flexible and adaptive approach by learning the importance of each neighbor during the aggregation process. The core of GATs is the attention network.

Attention networks

An attention network, often referred to as an attention mechanism or attention model, is a powerful concept in machine learning and artificial intelligence, particularly in the field of neural networks. It’s inspired by how human attention works – focusing on specific parts of input data while processing information.

An attention mechanism allows the model to dynamically focus on different parts of the input data, assigning varying degrees of importance or attention to each part. This enables the model to weigh the relevance of different...