• ogb>=1.3.3
• torch>=1.10.0
• torch-geometric>=2.0.4

GraphSAINT
python saint_graph.py --epochs <epochs> --load_CL <load_CL> --par <mu> --rate <rate> --topk <topk> --limt <delta>
where <mu> is a Node-IB loss ratio. <rate> is the initial perturbation ratio of data augmentation. <topk> is the number of subgraphs involved in contrastive learning. <load_CL> is to add contrastive learning at the Nth epoch, default is 0. <delta> is AutoR’s penalty intensity.

Cluster-GCN
python cluster_graph.py --epochs <epochs> --load_CL <load_CL> --par <mu> --rate <rate> --limt <delta>

GraphSAGE
python ns_graph.py --epochs <epochs> --par <mu> --rate <rate> --limt <delta>

If you find our repository useful for your research, please consider citing our paper:

@article{wang2025adagcl+,
  title={AdaGCL+: An Adaptive Subgraph Contrastive Learning Towards Tackling Topological Bias},
  author={Wang, Yili and Liu, Yaohua and Liu, Ninghao and Miao, Rui and Wang, Ying and Wang, Xin},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2025},
  publisher={IEEE}
}

@inproceedings{wang2022adagcl,
title={AdaGCL: Adaptive Subgraph Contrastive Learning to Generalize Large-scale Graph Training},
author={Wang, Yili and Zhou, Kaixiong and Miao, Rui and Liu, Ninghao and Wang, Xin},
booktitle={Proceedings of the 31st ACM International Conference on Information & Knowledge Management},
pages={2046--205},
year={2022}
}