THUDM / Grand
Licence: mit
Source code and dataset of the NeurIPS 2020 paper "Graph Random Neural Network for Semi-Supervised Learning on Graphs"
Stars: ✭ 75
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GRAND
This is the code of paper: Graph Random Neural Network for Semi-Supervised Learning on Graphs [arxiv]
Please cite our paper if you think our work is helpful to you:
@inproceedings{feng2020grand,
title={Graph Random Neural Network for Semi-Supervised Learning on Graphs},
author={Wenzheng Feng, Jie Zhang, Yuxiao Dong, Yu Han, Huanbo Luan, Qian Xu, Qiang Yang, Evgeny Kharlamov, Jie Tang},
booktitle={NeurIPS'20},
year={2020}
}
Requirements
- Python 3.7.3
- Please install other pakeages by
pip install -r requirements.txt
Usage Example
- Running one trial on Cora:
sh run_cora.sh - Running 100 trials with random initializations on Cora:
sh run100_cora.sh - Calculating the average accuracy of 100 trails on Cora:
python result_100run.py cora
Results
Our model achieves the following accuracies on Cora, CiteSeer and Pubmed with the public splits:
| Model name | Cora | CiteSeer | Pubmed |
|---|---|---|---|
| GRAND | 85.4% | 75.4% | 82.7% |
Running Environment
The experimental results reported in paper are conducted on a single NVIDIA GeForce RTX 2080 Ti with CUDA 10.0, which might be slightly inconsistent with the results induced by other platforms.
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