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ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learning (ICML 2022)
PyTorch implementation for ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learning accepted by ICML 2022.
Requirements
- Python 3.7.4
- PyTorch 1.7.0
- torch_geometric 1.5.0
- tqdm
Training & Evaluation
ProGCL-weight:
python train.py --device cuda:0 --dataset Amazon-Computers --param local:amazon-computers.json --mode weight
ProGCL-mix:
python train.py --device cuda:0 --dataset Amazon-Computers --param local:amazon-computers.json --mode mix
Useful resources for Pretrained Graphs Neural Networks
- The first comprehensive survey on this topic: A Survey of Pretraining on Graphs: Taxonomy, Methods, and Applications
- A curated list of must-read papers, open-source pretrained models and pretraining datasets.
Citation
@inproceedings{xia2022progcl,
title={ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learning},
author={Xia, Jun and Wu, Lirong and Wang, Ge and Li, Stan Z.},
booktitle={International conference on machine learning},
year={2022},
organization={PMLR}
}