GAT (Graph Attention Network) adds: learned attention weights between neighbors

GATs use learned attention weights between neighbors

Graph Attention Networks (GATs) introduce a mechanism for learning attention weights, which allows for the dynamic weighting of the importance of neighboring nodes during message passing. This attention mechanism enables GATs to focus on the most relevant parts of the graph for a given task.

Example

In a graph representing a molecule, GATs can learn to assign higher attention weights to atoms that are crucial for the molecule's efficacy in eliminating E. coli bacteria, while giving less weight to less important atoms.

Remember this

GATs can improve the performance of graph neural networks by allowing them to prioritize information that is most relevant to the task at hand.

Text adapted from Wikipedia, licensed under CC BY-SA 4.0.

Related concepts

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GNNs use pairwise message passing for node representation updates

message passing does in GNNs: each node aggregates features from its neighbors

Nodes in GNNs aggregate features from neighbors

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grouped query attention (GQA) does

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