Summary of A Cognac Shot to Forget Bad Memories: Corrective Unlearning in Gnns, by Varshita Kolipaka et al.
A Cognac shot to forget bad memories: Corrective Unlearning in GNNs
by Varshita Kolipaka, Akshit Sinha, Debangan Mishra, Sumit Kumar, Arvindh Arun, Shashwat Goel, Ponnurangam Kumaraguru
First submitted to arxiv on: 1 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces Corrective Unlearning for Graph Neural Networks (GNNs) to mitigate the effects of adversarial manipulations or incorrect data on graph-based machine learning applications. GNNs are prone to performance degradation due to message passing, which propagates errors across the graph. Current unlearning methods fail to remove the impact of manipulated entities even when the entire set is known. The proposed method, Cognac, can effectively unlearn manipulation effects with only 5% identification, achieving most of the performance of an oracle-trained model while being eight times more efficient than retraining from scratch. This work aims to assist GNN developers in addressing issues in real-world data post-training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps GNNs do better when there’s bad data or someone tries to trick them. Right now, if you try to fix this problem, the method doesn’t work well. The new way called Cognac can make it better by fixing the issue with just a small part of the bad data known. It’s faster and works almost as well as retraining everything from scratch. |
Keywords
» Artificial intelligence » Gnn » Machine learning
