This code reproduces the experimental results obtained with the SEP layer as presented in the ICML 2023 paper:
Junran Wu*, Xueyuan Chen*, Bowen Shi, Shangzhe Li, Ke Xu
In this paper, based on the theory of graph information bottleneck, we deduce the definition of the anchor view for graph contrastive learning; put differently, the anchor view with essential information of input graph is supposed to have the minimal structural uncertainty.
- Unsupervised learning [TU Datasets]
- Transfer learning [MoleculeNet and PPI]
- Semi-supervised learning [TU Datasets]
If you found the provided code with our paper useful in your work, we kindly request that you cite our work.
@inproceedings{wu2023sega,
title={SEGA: Structural entropy guided anchor view for graph contrastive learning},
author={Wu, Junran and Chen, Xueyuan and Shi, Bowen and Li, Shangzhe and Xu, Ke},
booktitle={International Conference on Machine Learning},
year={2023},
organization={PMLR}
}