Subg-Con

Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learning (Jiao et al., ICDM 2020): https://arxiv.org/abs/2009.10273

Overview

Here we provide an implementation of Subg-Con in PyTorch and the geometric deep learning extension library, Pytorch Geometric. The repository is organised as follows:

• subgcon.py is the implementation of the Subg-Con pipeline;
• subgraph.py is the implementation of subgraph extractor;
• model.py is the implementation of components for Subg-Con, including a GNN layer, a pooling layer, and a scoring function;
• utils_mp.py is the necessary processing subroutines;
• dataset/ will contain the automatically downloaded datasets;
• subgraph/ will contain the processed subgraphs.

Finally, train.py puts all of the above together and may be used to execute a full training. The codes can be run on six datasets, including Cora, CiteSeer, PubMed, PPI, Flickr, and Reddit.

Train SubgCon

python train.py --dataset DATASET_NAME

Train SubgCon+

python train+.py --dataset DATASET_NAME

Dependencies

• Python 3.9.0
• PyTorch 1.9.0
• pytorch_geometric 2.0.0
• scikit-learn
• scipy
• cytoolz

Reference

If you make advantage of Subg-Con in your research, please cite the following in your manuscript:

@article{jiao2020sub,
  title={Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learning},
  author={Jiao, Yizhu and Xiong, Yun and Zhang, Jiawei and Zhang, Yao and Zhang, Tianqi and Zhu, Yangyong},
  journal={arXiv preprint arXiv:2009.10273},
  year={2020}
}