Mindspore Graph Attention Network(GAT)

This is a MindSpore implementation of the Graph Attention Network (GAT) model, originally proposed by Veličković and colleagues in 2017 (https://arxiv.org/abs/1710.10903).

Citation

If you use this implementation in your research, please cite the original paper:

@article{velickovic2018graph,
title="{Graph Attention Networks}",
author={Veli{\v{c}}kovi{'{c}}, Petar and Cucurull, Guillem and Casanova, Arantxa and Romero, Adriana and Li{'{o}}, Pietro and Bengio, Yoshua},
journal={International Conference on Learning Representations},
year={2018},
url={https://openreview.net/forum?id=rJXMpikCZ},
note={accepted as poster},
}

About the Cora dataset:

Cora is a dataset containing 2708 scientific papers, grouped into seven distinct categories. The citation network comprises 10556 connections. Each paper is represented by a binary word vector, which indicates whether a particular word from the 1433-word dictionary is present or absent.

Cora Dataset:

• Total Nodes: 2708
• Total Edges: 10556
• Number of Class: 7

Label split:

• Training: 140
• Validation: 500
• Testing: 1000

Performances

Transductive Learning

• The final accuracy is between 84 % and 85 % for epochs = 1000.

Environment

• GPU
• MindSpore version: 2.0.0rc1.dev20230416