diegoantognini/pyGAT
PyTorch implementation of the Graph Attention Network (GAT) model for graph-structured data learning.
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This repository provides a PyTorch implementation of the Graph Attention Network architecture introduced by Veličković et al. (2018). The GAT applies self-attention mechanisms to graph nodes to learn importance weights between connected vertices, enabling inductive learning on graph data. It includes training code for transductive learning tasks (Cora dataset), benchmark performance metrics, and both dense and sparse matrix implementations. The project is forked from the classic PyGCN repository and references the original TensorFlow implementation.
Frequently asked
- What is diegoantognini/pyGAT?
- PyTorch implementation of the Graph Attention Network (GAT) model for graph-structured data learning.
- Is pyGAT open source?
- Yes — diegoantognini/pyGAT is open source, released under the MIT license.
- What language is pyGAT written in?
- diegoantognini/pyGAT is primarily written in Python.
- How popular is pyGAT?
- diegoantognini/pyGAT has 3.1k stars on GitHub.
- Where can I find pyGAT?
- diegoantognini/pyGAT is on GitHub at https://github.com/diegoantognini/pyGAT.