Graphs are a fundamental data structure that can represent a wide range of real-world problems, such as social networks, biological networks, and recommender systems. Graph neural networks (GNNs) are a family of neural networks that operate on graph-structured data and have shown promising results in various applications. However, traditional GNNs are limited in their ability to capture long-range dependencies and attend to relevant nodes and edges. This is where Graph Attention Networks (GATs) come in. In this blog post, we will explore the concept of GATs, their advantages over traditional GNNs, and their implementation in TensorFlow. Graph Attention Networks: A Brief Overview Graph Attention Networks (GATs) were introduced in a paper by Petar Veličković et al. in 2018 . GATs are a type of GNN that uses an attention mechanism to allow each node to selectively attend to its neighbors. In other words, GATs learn to assign different weights to different nodes in the graph, based on