diegoantognini/pyGAT

Health v2 · maintenance only

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Adoption not part of the score

3123 stars · 701 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A PyTorch implementation of the Graph Attention Network (GAT) model from Veličković et al. (2017), including a sparse-matrix variant. It reproduces the paper's transductive learning results on the Cora citation dataset.

Use cases

• implement graph attention networks in pytorch
• reproduce GAT paper results on Cora
• run node classification on citation graphs
• learn how attention works in graph neural networks
• benchmark sparse vs dense graph attention layers

When to choose

• you want a faithful PyTorch port of the original GAT paper
• you need a reference implementation for research or citation
• you want to experiment with attention-based graph neural networks

When to avoid

• you need a maintained library with modern PyTorch support (it targets PyTorch 0.4.1)
• you need production graph learning at scale rather than a research demo
• you want a general-purpose GNN framework with many model types

Facets

library · maturity maintenance

machine-learning deep-learning machine-learning deep-learning python graph-neural-networks graph-attention-networks pytorch attention-mechanism self-attention node-classification cora algorithms gpu

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