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'].