GAT
Graph Attention Network
A **Graph Attention Network (GAT)** is a neural network architecture that operates on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations. By stacking layers in which nodes are able to attend over their neighborhoods’ features, a GAT enables (implicitly) specifying different weights to different nodes in a neighborhood, without requiring any kind of costly matrix operation (such as inversion) or depending on knowing the graph structure upfront.

See [here](https://docs.dgl.ai/en/0.4.x/tutorials/models/1_gnn/9_gat.html) for an explanation by DGL.
Graph Attention Networks
https://paperswithcode.com/paper/graph-attention-networks
Graph Attention Networks