GAT - Graph Attention Network (PyTorch) + graphs + = ❤️. This repo contains a PyTorch implementation of the original GAT paper (:link: Veličković et al.).

This repository contains a PyTorch implementation of the Graph Attention Networks (GAT) based on the paper "Graph Attention Network" by Velickovic et al.

This is a PyTorch implementation of the paper Graph Attention Networks. GATs work on graph data. A graph consists of nodes and edges connecting nodes.

In this tutorial, you learn about a graph attention network (GAT) and how it can be implemented in PyTorch.

In this article, we'll see how to calculate these attention scores and implement an efficient GAT in PyTorch Geometric (PyG).

Jul 26, 2023 · A detailed and illustrated walkthrough of the “Graph Attention Networks” paper by Veličković et al. with the PyTorch implementation of the proposed model.

Oct 20, 2023 · Using an attention mechanism, we can enable Graph Convolution Networks to do such an analysis when the underlying data is modeled as a graph.

Sep 13, 2021 · In this tutorial, we will implement a specific graph neural network known as a Graph Attention Network (GAT) to predict labels of scientific papers based on ...

In this tutorial, you learn about a graph attention network (GAT) and how it can be implemented in PyTorch.

Jan 25, 2021 · The model learns which parts of the input should it pay more attention to (put bigger weights to those parts of the input)