SEGA: Structural Entropy Guided Anchor View for Graph Contrastive Learning

This code reproduces the experimental results obtained with the SEP layer as presented in the ICML 2023 paper:

ICML arXiv

Junran Wu^*, Xueyuan Chen^*, Bowen Shi, Shangzhe Li, Ke Xu

Overview

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.

Experiments

• Unsupervised learning [TU Datasets]
• Transfer learning [MoleculeNet and PPI]
• Semi-supervised learning [TU Datasets]

Citation

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}
}