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:

• Download datasets from Google Drive

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

• Download datasets from Tsinghua Cloud

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

• g++ 7.5.0
• pybind11
• networkx 2.5
• numpy 1.19.2
• scikit_learn 1.0.2
• scipy 1.5.2
• torch 1.8.1 (cuda 10.2)
• torch_scatter 2.0.6

Compilation

make clean && make

Running the code

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

Example:

• Running model on Pubmed for 10 runs with personalized pagerank matrix: sh scripts/run_pubmed.sh 10 <cuda_id> ppr

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}
}