Summary of Erase Then Rectify: a Training-free Parameter Editing Approach For Cost-effective Graph Unlearning, by Zhe-rui Yang et al.
Erase then Rectify: A Training-Free Parameter Editing Approach for Cost-Effective Graph Unlearning
by Zhe-Rui Yang, Jindong Han, Chang-Dong Wang, Hao Liu
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Erase then Rectify (ETR) approach efficiently and scalably achieves graph unlearning without additional training or full training data access. This method is essential in applications where privacy, bias, or data obsolescence is a concern. The ETR algorithm eliminates the influence of specific nodes, edges, or attributes from a trained Graph Neural Network (GNN) by strategically editing model parameters and estimating the model’s gradient on the remaining dataset. The approach preserves model utility while reducing computational overhead. The authors demonstrate the superiority of ETR in model utility, unlearning efficiency, and unlearning effectiveness through extensive experiments on seven public datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Graph unlearning is important when privacy or data obsolescence is a concern. Researchers have proposed methods to eliminate the influence of specific nodes or edges from trained Graph Neural Networks (GNNs). However, these methods often require additional training, which can be costly and time-consuming. To address this issue, scientists developed an approach called Erase then Rectify (ETR) that doesn’t need extra training data. The ETR method first removes the influence of unlearned samples and their effects on other nodes. Then, it uses a special technique to update the model’s performance. This new approach is more efficient and effective than previous methods. |
Keywords
» Artificial intelligence » Gnn » Graph neural network
