This is the code of paper LMC: Fast Training of GNNs via Subgraph Sampling with Provable Convergence. Zhihao Shi, Xize Liang, Jie Wang. ICLR 2023. [arXiv] [ICLR-Official]
- Python 3.7
- PyTorch 1.9.0
- torch-geometric 1.7.2
- ogb 1.3.3
- hydra-core 1.1.0
To compile the subgraph sampling codes in the csrc
directory, run the following commands.
cd code
python setup.py
To reproduce the results, please run the following commands.
CUDA_VISIBLE_DEVICES=0 python main_large.py dataset=arxiv model=gcn model.json='[PATH of CODE]/json/gcn/arxiv/variant.json'
If you find this code useful, please consider citing the following paper.
@inproceedings{
shi2023lmc,
title={{LMC}: Fast Training of {GNN}s via Subgraph Sampling with Provable Convergence},
author={Zhihao Shi and Xize Liang and Jie Wang},
booktitle={International Conference on Learning Representations},
year={2023},
url={https://openreview.net/forum?id=5VBBA91N6n}
}
We refer to the code of PyGAS. Thanks for their contributions.