Graph Attention Networks (GATs) are neural networks designed to work with graph-structured data that exploit hidden layers of self-attention to overcome the shortcomings of previous methods based on graph convolutions or their approximations.
We encounter such data in a variety of real-world applications such as social networks, biological networks, molecular structures, recommender systems...
Overview of GAT.
Implementations of Graph Attention Network and Graph Convolution Network.
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