This repository contains a PyTorch implementation of ICLR 2024 paper "PolyGCL: GRAPH CONTRASTIVE LEARNING via Learnable Spectral Polynomial Filters".
We provide the small datasets in the folder 'data'. You can access the heterophilic datasets and the large heterophilic graph arXiv-year via heterophilous-graphs and LINKX respectively.
You can run the following commands directly.
sh exp_PolyGCL.sh
Heterophilic datasets:
cd HeterophilousGraph
sh exp_PolyGCL.sh
Large heterophilic graph arXiv-year:
cd non-homophilous
sh exp_PolyGCL.sh
Generate the cSBM data firstly.
cd cSBM
sh create_cSBM.sh
Then run the following command directly.
sh run_cSBM.sh
This project includes code or ideas inspired by the following repositories:
@inproceedings{
chen2024polygcl,
title={Poly{GCL}: {GRAPH} {CONTRASTIVE} {LEARNING} via Learnable Spectral Polynomial Filters},
author={Jingyu Chen and Runlin Lei and Zhewei Wei},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=y21ZO6M86t}
}
If you have any questions, please feel free to contact me with jy.chen@ruc.edu.cn.