junesookang / LMC-ICLR-23

Convergence Improvement of GNNAutoScale (Historical Embedding based Sampling method)

LMC: Fast Training of GNNs via Subgraph Sampling with Provable Convergence

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]

Dependencies

• Python 3.7
• PyTorch 1.9.0
• torch-geometric 1.7.2
• ogb 1.3.3
• hydra-core 1.1.0

Reproduce the Results

1. Compile the subgraph sampling codes

To compile the subgraph sampling codes in the csrc directory, run the following commands.

cd code
python setup.py

2. Reproduce the Results

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'

Citation

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}
}

Acknowledgement

We refer to the code of PyGAS. Thanks for their contributions.