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GRAND+

This is a PyTorch implementation of GRAND+ for scalable graph-based semi-supervised learning:

GRAND+: Scalable Graph Random Neural Networks

You may be also interested in the predecessor of this work: Graph Random Neural Network for Semi-Supervised Learning on Graphs [github repo].

Datasets

This repo contains Cora, Citeseer and Pubmed datasets under the path dataset/citation/. The other datasets used in the paper (including AMiner-CS, Reddit, Amazon2M and MAG-Scholar-C) can be downloaded from Google Drive or Tsinghua Cloud. To run model on these datasets, you should download the corresponding zip file, uncompress it and put it under dataset/.

You can directly download the zip file of each dataset with the following scripts:

pip install gdown
gdown --id 1G9Wn1OaqMYpkNmbOESYUFrDgzo0Be0-L -O dataset/aminer.zip
gdown --id 1KauMd-AJXyD6KQQnf4vySjRZEOgWQYvx -O dataset/reddit.zip
gdown --id 1uItY1AGywFv4nSSFpqBaTEUoDn3w414B -O dataset/Amazon2M.zip
gdown --id 1VKHFQfRXkkVShE6d4hA9dImXZalz49qa -O dataset/mag_scholar_c.npz
python scripts/download.py --url https://cloud.tsinghua.edu.cn/f/629a605e453b40fc9a93/?dl=1 --path dataset --fname aminer.zip
python scripts/download.py --url https://cloud.tsinghua.edu.cn/f/384be92876ed4127aa3c/?dl=1 --path dataset --fname reddit.zip
python scripts/download.py --url https://cloud.tsinghua.edu.cn/f/7c867cef16214fe1a30b/?dl=1 --path dataset --fname Amazon2M.zip
python scripts/download.py --url https://cloud.tsinghua.edu.cn/f/5e5c9d8833a143d5abb4/?dl=1 --path dataset --fname mag_scholar_c.npz

Requirements

Compilation

make clean && make

Running the code

sh scripts/run_<dataset>.sh <runs> <cuda_id> <propagation matrix [ppr, avg, single]>

Example:

Cite

If you find this work is helpful to your research, please consider citing our paper:

@inproceedings{feng2022grand+,
  title={GRAND+: Scalable Graph Random Neural Networks},
  author={Feng, Wenzheng and Dong, Yuxiao and Huang, Tinglin and Yin, Ziqi and Cheng, Xu and Kharlamov, Evgeny and Tang, Jie},
  booktitle={Proceedings of the ACM Web Conference 2022 (WWW’22)},
  year={2022}
}