Efficient Model Updates for Approximate Unlearning of Graph-Structured Data
Authors: Eli Chien, Chao Pan, Olgica Milenkovic
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical evaluations of six benchmark datasets demonstrate excellent performance/complexity/privacy trade-offs of our approach compared to complete retraining and general methods that do not leverage graph information. |
| Researcher Affiliation | Academia | Eli Chien Chao Pan Olgica Milenkovic Department of Electrical and Computer Engineering University of Illinois, Urbana-Champaign {ichien3,chaopan2,milenkov}@illinois.edu |
| Pseudocode | Yes | Algorithm 1 Training procedure and Algorithm 2 Unlearning procedure in Section A.6. |
| Open Source Code | Yes | Our code is available online1. 1https://github.com/thupchnsky/sgc_unlearn |
| Open Datasets | Yes | We test our methods on benchmark datasets for graph learning, including Cora, Citseer, Pubmed (Sen et al., 2008; Yang et al., 2016; Fey & Lenssen, 2019) and large-scale dataset ogbnarxiv (Hu et al., 2020) and Amazon co-purchase networks Computers and Photo (Mc Auley et al., 2015; Shchur et al., 2018). |
| Dataset Splits | Yes | The data split is public and obtained from PyTorch Geometric Fey & Lenssen (2019). We used the full split option for Cora, Citeseer and Pubmed. Since there is no public split for Computers and Photo, we adopted a similar setting as for the citation networks via random splits (i.e., 500 nodes in the validation set and 1,000 nodes in the test set). The data split for ogbn-arxiv is the public split provided by the Open Graph Benchmark Hu et al. (2020). |
| Hardware Specification | Yes | All our experiments were executed on a Linux machine with 48 cores, 376GB of system memory, and two NVIDIA Tesla P100 GPUs with 12GB of GPU memory each. |
| Software Dependencies | No | The paper mentions 'PyTorch Geometric' and 'LBFGS as the optimizer' but does not specify their version numbers or other software dependencies with version information. |
| Experiment Setup | Yes | Unless specified otherwise, we fix K = 2, δ = 10 4, λ = 10 2, ϵ = 1, α = 0.1 for all experiments, and average the results over 5 independent trails with random initializations. |