/NeurIPS20-GRAND

Source code and dataset of the NeurIPS 2020 paper "Graph Random Neural Network for Semi-Supervised Learning on Graphs"

Primary LanguagePythonMIT LicenseMIT

PWCPWCPWC

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.