PetarV-/GAT

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

3548 stars · 671 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Reference implementation of Graph Attention Networks (GAT), the ICLR 2018 model by Veličković et al., written in TensorFlow 1.x with a minimal Cora training example and an experimental sparse variant. It is primarily a research reference rather than a production library.

Use cases

• implement graph attention networks
• train a GAT model on the Cora citation dataset
• understand how masked self-attention works on graphs
• reproduce results from the GAT paper
• get a reference GAT layer implementation in TensorFlow
• learn graph representation learning from a canonical example

When to choose

• you need the original reference implementation of GAT for research or citation
• you want a minimal, readable TensorFlow example of attention on graphs
• you are reproducing the ICLR 2018 paper's Cora results

When to avoid

• you need a maintained, optimized GNN library for production (use PyTorch Geometric, DGL, or Spektral)
• you require modern TensorFlow 2.x or PyTorch support
• you need large-scale or inductive training beyond the toy Cora example

Facets

library · maturity maintenance

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

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