SCARA-PPR

This is the original code for "SCARA: Scalable Graph Neural Networks with Feature-Oriented Optimization" (VLDB 2022) and "Scalable Decoupling Graph Neural Networks with Feature-Oriented Optimization" (VLDBJ 2023).

Paper - VLDB | Paper - VLDBJ | GitHub | Tech Report | arXiv

Citation

If you find this work useful, please cite our papers:

VLDBJ:

Ningyi Liao, Dingheng Mo, Siqiang Luo, Xiang Li, and Pengcheng Yin.
Scalable Decoupling Graph Neural Networks with Feature-Oriented Optimization.
The VLDB Journal, 33, 2023. doi:10.1007/s00778-023-00829-6.

@article{liao2023scalable,
  title={Scalable Decoupling Graph Neural Networks with Feature-Oriented Optimization},
  author={Liao, Ningyi and Mo, Dingheng and Luo, Siqiang and Li, Xiang and Yin, Pengcheng},
  journal={The {VLDB} Journal},
  volume={33},
  year={2023},
  publisher={Springer},
  url={https://link.springer.com/article/10.1007/s00778-023-00829-6},
  doi={10.1007/s10994-021-06049-9}
}

VLDB:

Ningyi Liao, Dingheng Mo, Siqiang Luo, Xiang Li, and Pengcheng Yin.
SCARA: Scalable Graph Neural Networks with Feature-Oriented Optimization.
PVLDB, 15(11): 3240-3248, 2022. doi:10.14778/3551793.3551866.

@article{liao2022scara,
  title={{SCARA}: Scalable Graph Neural Networks with Feature-Oriented Optimization},
  author={Liao, Ningyi and Mo, Dingheng and Luo, Siqiang and Li, Xiang and Yin, Pengcheng},
  journal={Proceedings of the VLDB Endowment},
  volume={15},
  number={11},
  pages={3240-3248},
  year={2022},
  publisher={VLDB Endowment},
  url = {https://doi.org/10.14778/3551793.3551866},
}

Usage

We provide a complete example and its log in the demo notebook. The sample PubMed dataset is available in the data folder.

Data Preparation

1. Download data (links below) in GBP format to path data/[dataset_name]. Similar to the PubMed dataset example, there are three files:

• adj.txt: adjacency table
  • First line: "# [number of nodes]"
• feats.npy: features in .npy array
• labels.npz: node label information
  • 'label': labels (number or one-hot)
  • 'idx_train/idx_val/idx_test': indices of training/validation/test nodes (inductive task)

2. Run command python data_processor.py to generate additional processed files:

• degrees.npz: node degrees in .npz 'arr_0'
• feats_norm.npy: normalized features in .npy array
  • Large matrix can be split
• query.txt: indices of queried nodes

Precompute

1. Environment: CMake 3.16, C++ 14. Dependencies: eigen3
2. CMake cmake -B build, then make
3. Run script: ./run_pubmed.sh

Train and Test

1. Install dependencies: conda create --name [envname] --file requirements.txt
2. Run experiment: python run.py -f [seed] -c [config_file] -v [device]

Baseline Models

• GraphSAINT: GraphSAINT
• APPNP: APPNP
• PPRGo: PPRGo
• GBP: GBP
• AGP: AGP
• GAS: GAS

Dataset Links

• Citeseer & Pubmed: GBP
• PPI: GraphSAGE
• Yelp: GraphSAINT
• Reddit: PPRGo
• Products & Papers100M: OGB
• Amazon: Cluster-GCN
• MAG: PANE