/certified_edge_unlearning

Primary LanguagePythonMIT LicenseMIT

Certified Edge Unlearning for Graph Neural Networks

This is an implementation of paper Certified Edge Unlearning for Graph Neural Networks. We provide the code of graph edge unlearning. In addition, we present the code for reproducing the experiments.

Dependencies

Result Reproduction

Tightness of Bounds

python experiment.py -rq bound

Accuracy of CEU

python experiment.py -rq unlearn

Efficency of CEU

python experiment.py -rq efficiency

Efficacy of CEU

python experiment.py -rq efficacy

Effect of $\epsilon$ 

python experiment.py --rq epsilon

CGU Comparison

python experiment.py --rq cgu_compare

Common Parameters

  • -g, the ID of a GPU you want to use. Default: -1 (using CPU)
  • -edges, a list, indicates the numbers of edges you want to unlearn. Default: [100, 200, 400, 800, 1000].
  • -targets, a list, indicates what target models you want to evaluate. Default:['gcn', 'sage', 'gin'].
  • -datasets, a list, indicate what datasets you want to use. Default:['cora', 'citeseer', 'cs'].