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Graph Attention Networks(GAT) was proposed in 2017 by Petar Veličković et al. By leveraging masked self-attentional layers to address shortcomings of prior graph based method, GAT achieved or matched state of the art performance on both transductive datasets like Cora and inductive dataset like PPI.
More detail about GAT can be found in:
This repository contains a implementation of GAT based on MindSpore and GraphLearning
The experiment is based on Cora-ML, which was extracted in "Deep gaussian embedding of attributed graphs: Unsupervised inductive learning via ranking." ICLR 2018
CUDA_VISIBLE_DEVICES=0 python model_zoo/gat/trainval_cora.py --data_path {data_path}
Cora dataset
Test acc: 0.8180
MindSpore Graph Learning is an efficient and easy-to-use graph learning framework, which allows researchers and developers to implement graph models according to formula easily and train efficiently.
https://gitee.com/mindspore/graphlearning
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