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💡 ML Concept of the Day: What is Graph Attention Network? 

In the last installment of our graph neural networks (GNNs) series, we would like to discuss one of its most recent but very popular incarnations: Graph attention networks (GATs). In Edge#203, we covered the concept of graph convolutional networks (GCN) as an architecture that combines node-level features with the graph’s local structure in order to learn solid representations of nodes. GCNs proved to be successful in many graph domains. But they have some limitations, and the main one is that they rely on graphs with a known and stable structure.  

Plenty of problems require the processing of graphs with arbitrarily changing structures. How to address that? →become a Premium subscriber to learn more. Only $25/per year.

Also in this issue:

• we discuss the original GAT paper – the first introduction of GATs in GNN literature.  

• we explore TF-GNN, a library for implementing GNNs in TensorFlow, which provides native interoperability with the rest of the TensorFlow stack, enabling sophisticated tooling for managing the lifecycle of GNN models. 

→read the full article